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Journal articles on the topic 'Leaf and Fruit Diseases'
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Rehman, Samra, Muhammad Attique Khan, Majed Alhaisoni, Ammar Armghan, Fayadh Alenezi, Abdullah Alqahtani, Khean Vesal, and Yunyoung Nam. "Fruit Leaf Diseases Classification: A Hierarchical Deep Learning Framework." Computers, Materials & Continua 75, no. 1 (2023): 1179–94. http://dx.doi.org/10.32604/cmc.2023.035324.
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Deepali Joshi, Et al. "Automatic Classification of Mango Leaf Disease based on Machine Learning and Deep Learning Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (November 2, 2023): 1398–405. http://dx.doi.org/10.17762/ijritcc.v11i10.8683.
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Fruits are the essential source of nutrition for the human body. The fruit needs to be nurtured and cared for in order to remain healthy. Lack of upkeep, illnesses, blemishes, and fungi result in a considerable loss of produce and profit. One of the important and popular fruit that is consumed worldwide is Mango. It is a fragile fruit and is vulnerable to diseases that affects its quality and quantity. Manual inspection for diseases or infection is a tedious process and requires abundant resources such as time and labour. Manual inspection is inefficient and inaccurate. Automatic inspection on the other hand has numerous benefits. The image classification techniques and algorithms can be used to detect infected and healthy mangoes thus reducing the losses to the farmers.
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Harteveld, D. O. C., O. A. Akinsanmi, K. Chandra, and A. Drenth. "Timing of Infection and Development of Alternaria Diseases in the Canopy of Apple Trees." Plant Disease 98, no. 3 (March 2014): 401–8. http://dx.doi.org/10.1094/pdis-06-13-0676-re.
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Alternaria leaf blotch and fruit spot of apple caused by Alternaria spp. cause annual losses to the Australian apple industry. Erratic control using protectant fungicides is often experienced and may be due to the lack of understanding of the timing of infection and epidemiology of the diseases. We found that Alternaria leaf blotch infection began about 20 days after bloom (DAB) and the highest disease incidence occurred from 70 to 110 DAB. Alternaria fruit spot infection occurred about 100 DAB in the orchard. Fruit inoculations in planta showed that there was no specific susceptible stage of fruit. Leaves and fruit in the lower canopy of trees showed higher levels of leaf blotch and fruit spot incidence than those in the upper canopy and the incidence of leaf blotch in shoot leaves was higher than in spur leaves. Temperature, relative humidity, and rainfall affected leaf blotch and fruit spot incidence. The gained knowledge on the timing of infection and development of disease may aid in the development of more effective disease management strategies.
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Truong, Hong H., Toyozo Sato, Seiju Ishikawa, Ayaka Minoshima, Takeaki Nishimura, and Yuuri Hirooka. "Three Colletotrichum Species Responsible for Anthracnose on Synsepalum dulcificum (Miracle Fruit)." International Journal of Phytopathology 7, no. 3 (December 27, 2018): 89–101. http://dx.doi.org/10.33687/phytopath.007.03.2658.
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By 2016, fruit rot and two different leaf diseases (leaf spot and leaf blight) were found on Synsepalum dulcificum (miracle fruit) in Tokyo, Kanagawa and Kagoshima prefectures of Japan. From the lesions, abundant conidial masses and acervuli of three Colletotrichum species, two of which produced sexual state, were observed. We conducted a pathogenicity assay using these Colletotrichum species on healthy fruits and leaves of S. dulcificum. Our artificial inoculation tests showed symptoms of disease on tested fruit and leaf and indicated all three Colletotrichum species as causal agents of anthracnose on S. dulcificum. Based on morphological characters and molecular phylogenetic analyses using ITS, GAPDH, ACT, CAL and TUB2 loci, these species were identified as Colletotrichum aenigma (MAFF 246750), C. siamense (MAFF 246751) and C. karstii (MAFF 245966). They have been previously reported as plant pathogenic fungi elsewhere in the world. This is the first report of fruit rot, leaf blight and leaf spot on S. dulcificum caused by these three Colletotrichum species.
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Jha, Sanjay Kumar, and Sita Lamichhane. "Fungal Diseases of Tomato in Kathmandu Valley." Journal of Nepal Biotechnology Association 4, no. 1 (March 22, 2023): 72–74. http://dx.doi.org/10.3126/jnba.v4i1.53449.
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The infected parts of the tomato plant were collected from Jitpurphedi of Kathmandu, Nepal. The isolated fungi from the infected parts were Septoria lycopersici, Cladosporium oxysporum responsible for leaf spot, Phytophthora infestans and Rhizoctonia solani responsible for leaf blight, Cladosporium cladosporioides responsible for fruit rot, Leveilulla taurica responsible for powdery mildew and Plasmopara viticola responsible for Downey mildew disease. In the survey period, the highest incidence was found at leaf blight (30.08%) and the lowest at stem rot (4.64%). In the case of severity, the maximum severity was found at Downey mildew (77%) and the minimum was recorded at fruit rot (5.25%).
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B.R, SINGH, and TRIPATHI D.P. "LOSS DUE TO LEAF CURL AND SPOTTED WILT DISEASES OF TOMATO." Madras Agricultural Journal 78, January April (1991): 34–36. http://dx.doi.org/10.29321/maj.10.a01821.
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Trends on incidence and losses due to leaf curl (LCD) and spotted wilt (SWD) diseases of tomato in relation to their vectors were studied at Kanpur, India. The population of whitefly and thrips on tomato was found to be maximum respectively in February and March. The incidence of LCD was found maximum in January and February while that of SWD in March. The losses due to LCD were the reduction in plant height, number of fruits and fruit weight, while SWD, in addition, killed the plants resulting in total loss. The average yield loss/ha was 163.68 q and 126.12 q or Rs. 8184 and 6306 due to LCD and SWD respectively.
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Mudholakar, Sunita, Kavitha G, Kanaya Kumari K T, and Shubha G V. "Automatic Detection of Citrus Fruit and Leaves Diseases Using Deep Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 4043–51. http://dx.doi.org/10.22214/ijraset.2022.45868.
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Abstract: Citrus fruit diseases are the major cause of extreme citrus fruit yield declines. Plant disease detection and classification are crucial long term agriculture. Manually monitoring citrus diseases is quite tough. As a result, image processing is used for designing an automated detection system for citrus plant diseases. Image acquisition, image preprocessing, image segmentation, feature extraction and classification are main processes in the citrus disease detection process. Deep learning methods have recently obtained promising results in a number of artificial intelligence issues, leading us to apply them to the challenge of recognizing citrus fruit and leaf diseases. In this approach, an integrated approach is used to suggest a convolutional neural networks (CNNs) model. The proposed CNN model is intended to differentiate healthy fruits and leaves from fruits/leaves with common citrus diseases such as black spot, canker and citrus blight. The proposed CNN model extracts complementary discriminative features by integrating multiple layers.
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T. R., Prashith Kekuda, Raghavendra H. L., Shilpa M., Pushpavathi D., Tejaswini Petkar, and Ayesha Siddiqha. "ANTIMICROBIAL, ANTIRADICAL AND INSECTICIDAL ACTIVITY OF GARDENIA GUMMIFERA L. F. (RUBIACEAE)." International Journal of Pharmacy and Pharmaceutical Sciences 9, no. 10 (October 2, 2017): 265. http://dx.doi.org/10.22159/ijpps.2017v9i10.20252.
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Objective: The present study was carried out to investigate antimicrobial, antiradical and insecticidal potential of leaf and fruit of Gardenia gummifera L. f. (Rubiaceae).Methods: The leaf and fruits were shade dried, powdered and extracted by maceration process using methanol. Antibacterial activity was evaluated against Gram positive and Gram negative bacteria by Agar well diffusion assay. Antifungal activity was determined against six seed-borne fungi by Poisoned food technique. Antiradical activity of leaf and fruit extracts was evaluated by 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2-azinobis 3-ethylbenzothiazoline 6-sulfonate (ABTS) radical scavenging assays. Insecticidal activity of leaf and fruit extracts, in terms of larvicidal and pupicidal activity, was assessed against larvae and pupae of Aedes aegypti.Results: Both the extracts inhibited all test bacteria. Marked antibacterial activity was displayed by fruit extract when compared to leaf extract. S. epidermidis and E. coli were inhibited to highest and least extent by both extracts respectively. Fruit extract was found to exhibit higher antifungal effect when compared to leaf extract. Leaf extract and fruit extract exhibited highest inhibitory activity against A. niger and A. flavus respectively. Leaf and fruit extracts scavenged DPPH radical’s dose dependently with an IC50 value of 49.01µg/ml and 2.53µg/ml respectively. The scavenging of ABTS by leaf and fruit extracts was dose dependent and the IC50 value for leaf and fruit extract was 2.58µg/ml and 2.31µg/ml respectively. Fruit extract was shown to exhibit marked antiradical activity when compared to leaf extract. Leaf and fruit extracts exhibited dose dependent insecticidal activity in terms of larvicidal and pupicidal activity and the susceptibility of larvae and pupae to extracts was in the order II instar larvae>IV instar larvae>pupae. Fruit extract displayed marked insecticidal potential when compared to leaf extract.Conclusion: Overall, fruit extract of G. gummifera exhibited marked antimicrobial, antiradical and insecticidal activity when compared to leaf extract. The plant can be used for developing agents/formulations effective against infectious microorganisms, oxidative stress and insect vectors that transmit dreadful diseases. The observed bioactivities could be ascribed to the presence of active principles which are to be isolated and characterized.
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Kumakech, Alfred, Allan Tekkara Obonyom, Alexandrina Acipa, and Laban Frank Turyagyenda. "Reaction of Selected Citrus Cultivars to Pseudocercospora Leaf and Fruit Spot Disease Under Natural Infection in Northern Uganda." Journal of Agricultural Science 16, no. 4 (March 15, 2024): 8. http://dx.doi.org/10.5539/jas.v16n4p8.
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Citrus is an important crop among many resource constrained subsistence farmers living in rural areas of Uganda. Citrus production is affected mainly by drought, declining soil fertility, pests and diseases. Among diseases, citrus leaf and fruit spot disease caused by fungus Pseudocercospora angolensis is currently one of the major constraints to the production of citrus in Uganda where millions of people rely on the crop for nutritional security and household income. The disease can lead to 50-100% fruit yield loss depending on environmental conditions, disease management and weather conditions. In order to identify resistance to P. angolensis, a study was conducted to identify variability for citrus leaf and fruit spot resistance from adapted commercial cultivars as an initial step in developing integrated disease management strategy. Six cultivars were assessed. The screening was under natural infection conditions in disease hot spots in northern Uganda in 2014a and 2014b seasons. The results showed significance difference (p < 0.01) for Area Under Disease Progressive Curve (AUDPC) for number of leaves with P. angolesnis symptoms and number of lesions. Subsequently, the study identified Kuno as resistant and Tangelo as less susceptible to Pseudocercospora leaf and fruit spot infection, and they could be recommended for citrus leaf and fruit spot disease control.
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Raheeba Tun Nisa, Altaf Ahmad Wani, and Rameesa Rashid. "Chemical Management of Alternaria leaf and fruit spot of apple." International Journal of Current Microbiology and Applied Sciences 10, no. 12 (December 10, 2021): 521–26. http://dx.doi.org/10.20546/ijcmas.2021.1012.057.
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In Jammu and Kashmir, a number of diseases like scab, Alternaria leaf blotch, Marsonena, sooty blotch, fly-speck and a number of post-harvest diseases pose a major threat to the apple industry. The occurrence of Alternaria leaf blotch in J&K was reported and the disease is prevalent in almost all the apple growing districts of Kashmir valley. Alternaria leaf blotch was considered a disease of minor importance in comparison to apple scab. However, the disease resulted in epidemic during summer of 2013, and about 40-60 per cent yield loss was reported. This epidemic was attributed to climate change ( high temperature coupled with prolonged rains), absence of disease forecasting system in the valley and also to the fact that currently used fungicides do not provide satisfactory level of disease control. When overwintering mycelium forms conidia and infects fresh budding apple leaves in the spring, the infection begins. After 90 days after flowering, a rise in temperature combined with significant rainfall and relative humidity enhances secondary infection in leaves and fruits. For disease control, a variety of techniques are available, including cultural, chemical, resistance, and biological approaches. We'll go through the disease's cause, symptoms, and treatment options here.
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Xiao, Jia-Rong, Pei-Che Chung, Hung-Yi Wu, Quoc-Hung Phan, Jer-Liang Andrew Yeh, and Max Ti-Kuang Hou. "Detection of Strawberry Diseases Using a Convolutional Neural Network." Plants 10, no. 1 (December 25, 2020): 31. http://dx.doi.org/10.3390/plants10010031.
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The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.
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Jha, Sanjay Kumar, and Sita Lamichhane. "Study of Some Fungal Diseases of Tomato in Kathmandu Valley." AMC Journal (Dhangadhi) 4, no. 1 (September 27, 2023): 1–7. http://dx.doi.org/10.3126/amcjd.v4i1.58826.
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Tomato plants were observed and collected the infected part from Jitpurphedi of Kathmandu, Nepal. These infected parts were kept in pathology lab for fungal isolation. The isolated fungus from the infected tomato plants were as Septoria lycopersici, Cladosporium oxysporum were responsible for leaf spot, Phytophthora infestans and Rhizoctonia solani were responsible for Leaf blight, Cladosporium cladosporioides was responsible for fruit rot, Leveilullataurica was responsible for powdery mildew and Plasmoparaviticola was responsible for downey mildew disease. In the survey period, the highest incidence was found at leaf blight (30.08%) and lowest at stem rot (4.64%). In the case of severity, the maximum severity was found at Downey mildew (77%) and minimum was recorded at fruit rot (5.25%) on five different plastic houses.
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Subedi, Subash, Sheela Koirala, and Saraswati Neupane. "Diversity and occurrence of major diseases of vegetables and fruit crops during spring season at Aanbukhaireni rural municipality of Tanahun district, Nepal." Journal of Agriculture and Natural Resources 2, no. 1 (October 24, 2019): 60–74. http://dx.doi.org/10.3126/janr.v2i1.26043.
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A survey has been conducted to assess the diversity and occurrence of major vegetables and fruits cultivated in Aanbukhaireni rural municipality of Tanahun district, Nepal during spring season of 2019.The surveyed areas were Satrasayaphant, Baradiphant and Dumridanda villages of ward no 1, Yeklephant, Markichowk and Pateni villages of ward no2 , Gaadapani village of ward no 3, Saakhar village of ward no 4 and Ghummaune village of ward no. 5. The surveyed area consists of upper tropical and sub tropical climate. The total no of farmers field selected for the survey was 34, 32, 24, 17 and 21 from ward no 1,2,3,4 and 5 respectively.The major vegetables cultivated during survey period in the surveyed area were bean, bitter-gourd, brinjal, chilli, cowpea, cucumber, okra, pumpkin, sponge-gourd, tomato, snake-gourd and bottle-gourd. Similarly, the fruits found in the region were banana, papaya, grapes, mango, litchi, peach, guava, lemon, mandarin orange etc. The major diseases of vegetables noticed were early blight, late blight, cercospora leaf spot, powdery mildew, downey mildew, fruit rot, bacterial wilt, bacterial spot, leaf curl and mosaic. In case of fruits, sigatoka leaf spot, panama wilt, black rot, algal leaf spot, canker, root rot, foot rot, sooty mold, red rust, anthracnose, rust, mosaic, alternaria leaf spot, downey mildew and leaf curl were the major diseases. The higher disease incidence (70%) and severity (48%) in vegetables were recorded in ward no 2 where as the lower incidence (45.2%) and severity (37.71%) were found in ward no 4. Similarly, the higher fruit disease incidence (70.24%) and severity (51.27%) in ward no 1 followed by ward no 2 with disease incidence and severity of 66.79% and 45.14% respectively. The reasons for those results are low educational level, lack of best bet technology, no proper irrigation and fertilizer, unavailability of pesticides for controlling the diseases. This study will be useful to identify the major diseases of vegetables and fruits of terai and inner terai region of Nepal and applying control measure, looking for the best possible solutions.
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Li, Tao, Wenzhong Zhu, and Xuan Che. "Design and Implementation of Apple Leaf Disease Recognition System Based on ResNet50." Journal of Computing and Electronic Information Management 12, no. 2 (March 30, 2024): 96–104. http://dx.doi.org/10.54097/u65ay2y1.
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Timely identification of apple leaf diseases is critical to preventing crop losses and safeguarding yields. Spotted leaf drop, brown spot, gray spot, mosaic and rust are all common types of apple leaf diseases, and their presence signals a potential risk that could lead to significant reductions in fruit and crop yields. The apple industry is thus at risk of economic losses. In order to solve this problem, this study applies ResNet50, a deep learning model for image recognition and classification of apple leaf diseases, and develops an intelligent recognition system for apple leaf diseases by combining PyQt technology. The purpose of this system is to overcome the shortcomings of traditional recognition methods. Through experimental validation, the ResNet50 model achieves a high accuracy rate of 93.19% in apple leaf disease recognition, demonstrating its efficiency and practicality in practical applications.
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Kumiya, Radhika, DC Singh, GM Kavya, and Kiran Vashisht. "PHARMACOGNOSTIC AND PHARMACOLOGICAL PROPERTIES OF GAMBHARI FRUIT (GMELINA ARBOREA ROXB.): A REVIEW." International Journal of Research in Ayurveda and Pharmacy 14, no. 2 (April 25, 2023): 136–41. http://dx.doi.org/10.7897/2277-4343.140257.
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Gmelina arborea Roxb. is one of the famous medicinal plants of the family Lamiaceae, which different Ayurvedic physicians widely prescribed as a drug of choice for treating many diseases. This drug is commonly named “Kashmarya” and is one of the popular medicinal plants mentioned in all classical textbooks of Ayurveda. It is also known as “Gambhari” because of its fast-growing property, and it is a widely propagated and cultivated tree that grows throughout India. In the Dashamoola groups of herbs, Gambhari (Gmelina arborea Roxb) is one of the components. In Ayurvedic classical textbooks, different plant parts, like roots, fruit, leaf, flower, and bark, can be used medicinally. The edible fruits of Gambhari bear rejuvenating, brain tonic and aphrodisiac qualities. The leaf of Gambhari has been mentioned in the diseases like vrana (wounds) and Kushtha (Skin diseases). The classical part of the plant Gambhari is the root. The present article provides insight into the literature review of the Gambhari fruit. The current manuscript compiles extensive information about Gambhari (Gmelina arborea Roxb.) fruit which is well-mentioned in most Ayurvedic classics textbooks like Brihatrayee, Laghutree and Nighantu.
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Osipov, G. E., N. V. Petrova, and A. A. Karpova. "Biological and economic features of the new apple variety Renet Povolzhya." Agrarian science, no. 11 (November 25, 2023): 107–11. http://dx.doi.org/10.32634/0869-8155-2023-376-11-107-111.
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Relevance. The Republic of Tatarstan is located in the zone of risky fruit growing. In winter, frosts below -35 °C, thaws are not uncommon here, in spring during flowering – frosts, in summer – hot and dry weather. During the growing season, diseases are widespread – scab, moniliosis, pests – flower beetles, aphids, codling moths. All these factors negatively affect the yield and quality of fruits of apple varieties. To increase the production of apple fruits in the Republic of Tatarstan, new high-yielding varieties of apple trees, adaptive to abiotic and biotic stressors, with fruits of good taste, high content of nutrients and biologically active substances, and long shelf life, are needed.Methods. The purpose of the research is the biological and economic evaluation of the new apple variety Renet Povolzhya in the conditions of the Republic of Tatarstan. Winter hardiness, productivity, yield, scab susceptibility of apple varieties, damage by aphids, fruit taste and economic efficiency were evaluated according to the methodology of the All-Russian Research Institute of Fruit Crop Breeding.Results. Average for 2019–2022 in the new apple variety Renet Povolzhya, the total degree of freezing was 1.3 points, flowering strength – 3 points, leaf damage by scab – 2.3 points, leaf damage by aphids – 2.6 points, productivity – 12.9 kg, yield – 5, 2 t/ha, fruit taste – 4.3 points, profit per 1 ha of orchard – 7.4 thousand rubles, profitability level – 10.5%. In the standard apple variety Antonovka ordinary, the total degree of freezing was 1.3 points, flowering strength – 2.6 points, leaf damage by scab – 2.3 points, leaf damage by aphids – 2.9 points, productivity – 10.3 kg, yield – 4.1 t/ha, fruit taste – 3.9 points.
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Poudel, Nabin Sharma, and Kapil Khanal. "Viral Diseases of Crops in Nepal." International Journal of Applied Sciences and Biotechnology 6, no. 2 (June 29, 2018): 75–80. http://dx.doi.org/10.3126/ijasbt.v6i2.19702.
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Viral diseases are the important diseases next to the fungal and bacterial in Nepal. The increase in incidence and severity of viral diseases and emergence of new viral diseases causes the significant yield losses of different crops in Nepal. But the research and studies on plant viral diseases are limited. Most of the studies were focused in viral diseases of rice (Rice tungro virus and Rice dwarf virus), tomato (Yellow leaf curl virus) and potato (PVX and PVY). Maize leaf fleck virus and mosaic caused by Maize mosaic virus were recorded as minor disease of maize. Citrus Tristeza Virus is an important virus of citrus fruit in Nepal while Papaya ringspot potyvirus, Ageratum yellow vein virus (AYVV), Tomato leaf curlJava betasatellite and Sida yellow vein Chinaalphasatellite were recorded from the papaya fruit. The Cucumber mosaic virus (CMV) and Zucchini yellow mosaic potyvirus (ZYMV) are the viral diseases of cucurbitaceous crop reported in Nepal. Mungbean yellow mosaic India virus (MYMIV) found to infect the many crops Limabean, Kidney bean, blackgram and Mungbean. Bean common mosaic necrosis virus in sweet bean, Pea leaf distortion virus (PLDV), Cowpea aphid‐borne mosaic potyvirus (CABMV), Peanut bud necrosis virus (PBNV) in groundnut, Cucumber mosaic virus (CMV). Chili veinal mottle potyvirus (CVMV) and Tomatoyellow leaf curl gemini virus (TYLCV) were only reported and no any further works have been carried out. The 3 virus diseases Soyabean mosaic (SMV), Soybean yellow mosaic virus and Bud blight tobacco ring spot virus (TRSV) were found in soybean.Int. J. Appl. Sci. Biotechnol. Vol 6(2): 75-80
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Vrsaljko, Anđelko. "The Dynamic of Lead Accumulation in the Almond Leaves and in the Parts of the Fruit." Poljoprivreda 29, no. 1 (June 20, 2023): 35–42. http://dx.doi.org/10.18047/poljo.29.1.5.
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Almond fruits (kernels) are considered to be healthy all across the globe as they contain, in addition to their high nutritional value, an increased concentration of essential biomolecules that have positive effects on human metabolism and, at the same time, prevent the most important immune diseases. , As a result, in the ecological conditions of the Ravni Kotari area, the two-year studies of lead (Pb) accumulation in the leaves and certain parts of the fruit were carried out, and their correlations were calculated. Subsequent to the day on which the fruit was set (DAFS) till germination, the level of Pb decreased slightly in all parts of the fruit, whereas the direction manifested a double sigmoid curve when it comes to the leaf. The level of Pb in the leaf was almost twice as high when compared to the parts of the fruit, especially in relation to the kernels, and toward the end of vegetation, which indicates a weak transfer of Pb from the leaf to the kernel and/or an immobilization of Pb in the leaf endoderm. The concentration of Pb in almond kernel in the phenophase of maturity ranged from 0.27±0.031 to 0.40±0.021 mg/kg of dry matter. This is an extremely low level of concentration if compared to the other fruits, which contain the higher levels of Pb when fresh, often being three to four times greater amounts in terms of dry matter. Positive correlations were found between the kernel and the endocarp, as well as between the kernel and the exocarp. Thus, it is safe to assert that almond kernels produced in the ecological conditions of the Ravni Kotari area may be qualified as “functional food,” but they may also constitute an integral part of infant foodstuffs.
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Anwar, Masrur, Yosi Kristian, and Endang Setyati. "Klasifikasi Penyakit Tanaman Cabai Rawit Dilengkapi Dengan Segmentasi Citra Daun dan Buah Menggunakan Yolo v7." INTECOMS: Journal of Information Technology and Computer Science 6, no. 1 (June 27, 2023): 540–48. http://dx.doi.org/10.31539/intecoms.v6i1.6071.
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Diseases that attack chili plants can be diagnosed early by observing symptoms or changes that occur in the leaves and fruit of the chili plant. However, diseases or pests that attack chili plants within a single plant can vary. In this study, YOLO v7 was used to perform leaf and chili segmentation on images, and the segmented results were then classified for chili plant disease using Deep Convolutional Neural Network (DCNN) Transfer Learning with the Fine Tuning method. The test results of the constructed model showed that the Yolo v7 segmentation accuracy was 0.970 on mAP50 when performing chili plant leaf and fruit segmentation. For the DCNN model testing with transfer learning method using the EfficientNetV2M based model, an accuracy value of 0.912 was obtained for leaf disease classification and an accuracy of 0.889 was obtained for chili fruit classification.
Keyword: Chili Plant Diseases; Classification; Transfer Learning, Yolo v7 segmentation
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Borisova, I. P., Yu N. Prihodko, and M. E. Podgornaya. "Test of fungicide Farmaiod, GS for apple tree viral diseases control." Horticulture and viticulture, no. 3 (July 10, 2019): 52–56. http://dx.doi.org/10.31676/0235-2591-2019-3-52-56.
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In the Krasnodar Territory, on the Renet Simirenko apple variety, symptoms corresponding to the manifestation of viral diseases were found: chlorotic leaf spot, dark green depressed spots on the fruit, which manifest themselves at the end of ripening before harvesting and during storage. Identification of pathogens was performed by Enzyme Immunoassay (ELISA). Samples were tested for the presence of apple chlorotic leaf spot virus (ACLSV), apple stem pitting virus (ASPV), apple stem grooving virus (ASGV), and apple mosaic virus (ApMV). As a result of the analysis, viruses of chlorotic leaf spot of the apple tree (ACLSV) and pitted of apple tree wood (ASPV) were detected. To protect the apple tree from viruses, the drug Farmaiod, GS, whose active ingredient is iodine, was tested. The first treatment was carried out during the 2014 leaf fall period with a consumption rate of 1.0 l/ha and continued in the following 2015 in the phenophase "green cap" and "fruit diameter 10-20 mm" in two versions with consumption rates of 0.5 and 1,0 l/ha. With the onset of leaf fall in the same areas, the treatment cycle was repeated according to the same scheme. Accounting was carried out by the method of visual diagnostics, in addition, at the end of the first and second seasons, virological analyzes were performed by ELISA. As a result of research, it was noted that 3-fold use of the drug Farmaiod, GS in apple trees of the variety Renet Simirenko restrained the development of the apple tree acle virus (ASPV) and completely suppressed the apple leaf chlorophyll virus (ACLSV). The weather conditions of 2016 test triggered a strong manifestation of scab on leaves and fruits. In the experimental variants, it was noted that the drug at 78.3-83.1 % inhibits the development of apple scab. Treatment with Farmaiod, GS also fostered harvest increase and premium output. The maximum results regarding restraints of infectious processes as well as harvest increase were received when applying the preparation with consumption rate of 1,0 l/ha.
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Leonida, Belinda G., Noemi G. Laspiñas, and Greta G. Gabinete. "Survey of diseases affecting tropical fruit trees in Central Panay Island, Philippines." IOP Conference Series: Earth and Environmental Science 1208, no. 1 (July 1, 2023): 012024. http://dx.doi.org/10.1088/1755-1315/1208/1/012024.
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Abstract The study aimed to determine the diseases infecting the foliage and stems of tropical fruit trees such as durian (Durio zibethinus L.), langsat (Lansium domesticum), mangosteen (Garcinia mangostana), pomelo (Citrus maxima), and rambutan (Nephelium lappaceum L.), the species most affected, resistant and susceptible species. The study was conducted at the DBP Forest within the school reservation of the West Visayas State University-College of Agriculture and Forestry, Lambunao, Iloilo, Panay Island, Philippines. The data gathered were tabulated and analyzed using descriptive statistics. There were eleven evident foliage diseases infecting the fruit trees, including anthracnose, chlorosis, curling, leaf blight, leaf rust, leaf spot, mosaic, powdery mildew, tar spot, shot hole, and sooty molds were recorded. The shot hole disease was observed in five species of fruit trees, while chlorosis infected only pomelo. The powdery mildew was found to have the highest percentage of foliage infection, and the least was chlorosis. There were three kinds of disease infecting the stems that were evident during the time of the survey, including canker, stem rust, and dieback. Three of the five species of fruit trees surveyed were associated with stem diseases, including durian, mangosteen, and pomelo. Moreover, it was found that the rambutan and mangosteen were the most susceptible species infected by foliage diseases and durian for stem diseases.
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Ashritha, K., K. Sandhya, Y. Uday Kiran, and V. N. L. N. Murthy. "Plant-Leaf Disease Prediction Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 121–28. http://dx.doi.org/10.22214/ijraset.2023.49338.
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Abstract: Brown spot, Mosaic, Grey spot, and Rust all significantly reduce apple yield. Rust is a sign of Foliar illness in this instance. The primary factor influencing apple output is the occurrence of apple leaf diseases, which results in significant yearly economic losses. Therefore, it is very important to research apple leaf disease identification. Plants are frequently attacked by pests, bacterial diseases, and other microorganisms. Inspection of the leaves, stem, or fruit usually identifies the attack's signs. Powdery Mildew and Leaf Blight are two common plant diseases that can cause severe harm if not treated quickly. In the realm of agriculture, image processing is frequently utilized for classification, detection, grading, and quality control. Finding and identifying plant diseases is crucial, especially when trying to produce fruit of the highest caliber. The real-time identification of apple leaf diseases is addressed in this research using a deep learning strategy that is based on enhanced convolutional neural networks (CNNs). This study uses data augmentation and image annotation tools to create the foliar disease dataset, which is made up of complex images captured in the field and laboratories. Overall, we can identify the illness present in plants on a massive scale by utilizing machine learning to train the vast data sets that are publically available. The project explains how to identify plant leaf diseases, how they affect plant yield, and which pesticides should be used to treat them. in agriculture. To monitor huge plant fields and automatically identify disease symptoms as soon as they develop on plant leaves, research on automatic plant disease is crucial. In this essay, we'll demonstrate how to identify plant illnesses by obtaining photos of their leaves
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Akamatsu, Ena, Takanori Kai, Hideaki Hirabaru, Chizuko Yukizaki, Miho Sakai, Hirofumi Uto, Hirohito Tsubouchi, and Hisato Kunitake. "(171) Blueberry Leaf Inhibits Hepatitis C Virus RNA Replication." HortScience 41, no. 4 (July 2006): 1082A—1082. http://dx.doi.org/10.21273/hortsci.41.4.1082a.
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Blueberry (Vaccinium sp.) fruits contain high concentrations of polyphenols such as anthocyanin. It is well known that polyphenols have antioxidant activity, so it is likely that the fruit has a possible preventative effect against several diseases like cancer. However, only a few reports so far have studied the human health benefits of the leaves. In this study, the antioxidant activity and antiviral effects of blueberry leaves were investigated. The leaves of three groups of blueberry, northern highbush blueberry (NHB), southern highbush blueberry (SHB), and rabbiteye blueberry (REB), were examined. These leaves were harvested in July and extracted with 80% ethanol. Samples were analyzed for antioxidant activity (DPPH radical scavenging activity) and antiviral activity against hepatitis C virus using the replicon cell assay (Lomann et al., 1999). The antioxidant activity showed significant variability between cultivars and species, with REB having about two times the activity of NHB and SHB. Antiviral activity was observed in the extracts of the leaves and the fruit, and the activity of the leaves was higher than that of the fruit. Among the cultivars and species evaluated, the antiviral activity of REB was higher than that of NHB and SHB. In addition, we discovered a positive correlation (r=0.68) between the antioxidant activity and the antiviral activity, using the leaves of hybrid seedlings of REB. Therefore, it is possible to speculate that the antiviral activity bears some relation to the antioxidant activity.
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Singh, Ram Lakhan, and Pankaj Singh. "Quantification of Phytochemicals Imparting Antioxidant Activities in Commonly Used Vegetables." International Journal of Applied Sciences and Biotechnology 6, no. 2 (June 29, 2018): 97–102. http://dx.doi.org/10.3126/ijasbt.v6i2.19636.
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Plant derived phytochemicals have recently became of great importance in the protection of various diseases, like heart disease, cardiovascular disease, cancer, diabetes, Alzheimer’s disease, cataract and age related functional disorders caused by free radicals. The present study was carried out to explore the commonly used vegetables having higher content of antioxidant imparting phytochemicals such as ascorbic acid, carotenoids, total phenolic content, carbohydrate and protein content in commonly used vegetables. The results revealed that the concentration among tested samples ranged from 7.07 mg/100g of FW (Momardica charantia leaf) to 174.15 mg/100g of FW (Allium sativum leaf) for ascorbic acid; 1.31 µg/g of FW (Chenopodium album leaf) to 14.00 µg/g of FW (Allium sativum leaf ) for carotenoid content; 8.72 mg of GAE/g of DW (Cucurbita maxima fruit) to 67.20 mg/g of DW (Colocasia esculentum leaf) for total phenolic content; 27.15 mg/g (Laginaria vulgaris leaf) to 901.00 mg/g (Cucurbita maxima fruit) for carbohydrate content and 35.96 mg/g (Amarphophyllus fruit) to 589.23 mg/g (Beta vulgaris fruit) for protein content. Results also showed that these bioactive phytochemicals are widely distributed in the vegetables and their concentrations are variable in different vegetables as well as vegetable part’s itself. Hence, vegetable rich diet having higher content of phytochemicals can be used to cure or in the prevention of various chronic diseases such as hepatotoxicity, diabetes, cardiovascular diseases, cancer, oxidative stress etc and may serve as a good source of nutraceuticals which have potential for use in health care formulations.Int. J. Appl. Sci. Biotechnol. Vol 6(2): 97-102
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Saleem, Rabia, Jamal Hussain Shah, Muhammad Sharif, Mussarat Yasmin, Hwan-Seung Yong, and Jaehyuk Cha. "Mango Leaf Disease Recognition and Classification Using Novel Segmentation and Vein Pattern Technique." Applied Sciences 11, no. 24 (December 14, 2021): 11901. http://dx.doi.org/10.3390/app112411901.
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Mango fruit is in high demand. So, the timely control of mango plant diseases is necessary to gain high returns. Automated recognition of mango plant leaf diseases is still a challenge as manual disease detection is not a feasible choice in this computerized era due to its high cost and the non-availability of mango experts and the variations in the symptoms. Amongst all the challenges, the segmentation of diseased parts is a big issue, being the pre-requisite for correct recognition and identification. For this purpose, a novel segmentation approach is proposed in this study to segment the diseased part by considering the vein pattern of the leaf. This leaf vein-seg approach segments the vein pattern of the leaf. Afterward, features are extracted and fused using canonical correlation analysis (CCA)-based fusion. As a final identification step, a cubic support vector machine (SVM) is implemented to validate the results. The highest accuracy achieved by this proposed model is 95.5%, which proves that the proposed model is very helpful to mango plant growers for the timely recognition and identification of diseases.
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Zaki, Siti Zulaikha Muhammad, Mohd Asyraf Zulkifley, Marzuraikah Mohd Stofa, Nor Azwan Mohammed Kamari, and Nur Ayuni Mohamed. "Classification of tomato leaf diseases using MobileNet v2." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 2 (June 1, 2020): 290. http://dx.doi.org/10.11591/ijai.v9.i2.pp290-296.
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<span lang="EN-US">Tomato is a red-colored edible fruit originated from the American continent. There are a lot of plant diseases associated with tomatoes such as leaf mold, late blight, and mosaic virus. Tomato is an important vegetable crop that contributes to the world economically. Despite tremendous efforts in plant management, viral diseases are notoriously difficult to control and eradicate completely. Thus, accurate and faster detection of plant diseases is needed to mitigate the problem at the early stage. A computer vision approach is proposed to identify the disease by capturing the leaf images and detect the possibility of the diseases. A deep learning classifier is utilized to make a robust decision that covers a wide variety of leaf appearances. Compact deep learning architecture, which is MobileNet V2 has been fine-tuned to detect three types of tomato diseases. The algorithm is tested on 4,671 images from PlantVillage dataset. The results show that MobileNet V2 is able to detect the disease up to more than 90% accuracy.</span>
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Ellis, M. A., M. Nita, and L. V. Madden. "First Report of Phomopsis Fruit Rot of Strawberry in Ohio." Plant Disease 84, no. 2 (February 2000): 199. http://dx.doi.org/10.1094/pdis.2000.84.2.199c.
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During spring (May and June) 1999, ≈30% of the fruit in a 0.5-ha commercial planting of strawberry (Fragaria × ananassa ‘Allstar’) was lost to an unknown fruit rot. The planting was established on black plastic (plasticulture) during summer 1998. Plasticulture is a relatively new system of perennial strawberry production in Ohio that is rapidly gaining popularity among growers (2). It was observed that the plastic beneath the plants in the plasticulture planting was covered with a layer of dead leaves from the previous season's growth, and virtually all rotted fruits were in contact with dead leaves. Fruit rot was rarely observed on fruits that were not in direct contact with dead leaves, and fruits of the same cultivar grown in the traditional matted-row system in an adjacent field did not show rot symptoms. It was postulated that infested dead leaf material could serve as an inoculum source for infection. Fruit rot symptoms were identical to those described for Phomopsis soft rot (1). Isolations were made from infected berries. Berries were soaked in 70% ethanol for 60 s, and tissue sections were placed on potato dextrose agar and incubated at room temperature (≈20 to 22°C). A fungus was isolated consistently from infected tissue. The fungus produced pycnidia in culture, and the fruiting structure and conidia conformed to the description of Phomopsis obscurans (Ellis & Everh.) Sutton (synamorph Dendrophoma obscurans (Ellis & Everh.) H.W. Anderson). Pathogenicity studies were conducted by placing one drop (20 μl) of a conidial suspension (9 × 106 conidia per ml) obtained from 2-week-old cultures on each of five ripe (red) and five immature (pink) detached strawberry fruits. Inoculated fruits were placed on screens in plastic moisture chambers. Five uninoculated fruits served as controls. Within 3 days, whitish lesions appeared on all inoculated fruit; within 8 days, the symptoms observed in the field were reproduced, and lesions were covered by pycnidia. No fruit rot developed on control fruit. The fungus was reisolated from infected fruit to complete Koch's postulates. Although the incidence of Phomopsis leaf blight is increasing in many Ohio strawberry plantings and is becoming a concern to growers, this is the first observation and report of Phomopsis fruit rot in Ohio. Unlike leaf blight, for which it is difficult to directly relate economic loss to disease incidence or severity, losses due to fruit rot can be high, as in this field. As the number of strawberry plantings under plasticulture continues to increase, the importance of Phomopsis leaf blight and fruit rot also may increase. References: (1) J. L. Maas, ed. 1998. Compendium of Strawberry Diseases, 2nd ed. The American Phytopathological Society, St. Paul, MN. (2) E. B. Poling. HortTechnology 3:384, 1993.
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Silva, Laércio J. da, Carla do C. Milagres, Derly José H. da Silva, Carlos Nick, and João Paulo A. de Castro. "Basal defoliation and their influence in agronomic and phytopathological traits in tomato plants." Horticultura Brasileira 29, no. 3 (September 2011): 377–81. http://dx.doi.org/10.1590/s0102-05362011000300020.
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The incidence of leaf diseases is one of the main factors limiting the tomato crop production, increasing the production cost due to excessive pesticide application. The basal leaf removal could reduce inoculum sources, disease severity and contribute to reducing the use of pesticide. Aiming to evaluate the efficiency of this practice on the reduction of tomato leaf diseases and the effect in the quality and in the productivity of the tomato plants for in natura consumption, two experiments were carried out to test four levels of basal leaf removal. Basal leaves removal, at fruit harvesting, is efficient in reducing the infected plant area by disease, what can mean smaller disease severity and least inoculum source in the field. Also, the all basal leaf removal does not affect yield and quality of the tomato fruits. Other studies are necessary to evaluate the effect of adopting this practice in different planting dates, spacing, varieties, successive plantings and the financial viability of adopting this practice.
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Bayram, Hande Yuksel, Harun Bingol, and Bilal Alatas. "Hybrid Deep Model for Automated Detection of Tomato Leaf Diseases." Traitement du Signal 39, no. 5 (November 30, 2022): 1781–87. http://dx.doi.org/10.18280/ts.390537.
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Tomatoes are preferred by farmers because of their high productivity. This fruit has a fibrous structure and contains plenty of vitamins. Tomato diseases are generally observed on stem, fruit, and leaves. Early diagnosis of the disease in plants is of vital importance for the plant. This is very important for farmers who expect economic gain from that plant. Because if the disease is not treated early, these tomatoes should be destroyed. For these reasons, systems to diagnose the disease early are very important. In this study, a tomato leaf diseases classification model developed with deep learning methods, which is one of the most popular artificial intelligence techniques, is proposed in order to eliminate the possibility of the human eye being mistaken. In this study, 6 different Convolutional Neural Network (CNN) architectures were used. In the first stage of this study, which consists of two stages, the classification process was carried out with the Alexnet, Googlenet, Shufflenet, Efficientb0, Resnet50, and Inceptionv3 architectures that were previously trained. In the second stage, feature maps of tomato leaf images in the dataset were obtained using the six pre-trained deep learning architectures. In the hybrid model proposed in this study, the feature maps extracted using the best two of the six deep learning models are concatenated. Then, the Neighborhood Component Analysis (NCA) method was applied to the extracted features in order to speed up the system, unnecessary features were removed and optimized. The optimized feature map is classified by traditional intelligent classification models. As a result of experimental studies, the average accuracy rate of the proposed model is 99.50 percent.
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S.Abirami, M. Thilagavathi. "Application of Image Processing in Diagnosing Guava Leaf Diseases." International Journal of Scientific Research and Management (IJSRM) 5, no. 7 (July 2, 2017): 5927–33. http://dx.doi.org/10.18535/ijsrm/v5i7.19.
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Plant and leaf diseases in Guava result in poor plant growth and reduced fruit yields. Leaves are indicators of the health and growth of the Guava shrub/short tree that has its origin in tropical and subtropical regions. Precise diagnosing of diseases is vital, as remedies rely on it. Image processing in the place of manual/visual detection of Guava leaf diseases relieves from difficulties experienced, time consumed and inaccuracy resulted. In the present work resized leaf images with improved contrasts are subject to region growing segmentation, colour transformation (YCbCr,CIELAB), and Scale Invariant Feature Transform(SIFT). Support Vector Machine (SVM) and kNearest Neighbor (k-NN) classifiers have been evaluated for their disease-wise classifying accuracies. 125 leaf samples at 25 per disease class and 128 texture features per sample were used in the study. Though both SVM and k-NN perform reasonably well, the former is slightly superior in terms of accuracy
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Arora, Healy, S. K. Jindal, Abhishek Sharma, Rupeet Gill, and N. Chawla. "Development and evaluation of hybrids resistant to late blight and leaf curl virus diseases in tomato." Genetika 54, no. 2 (2022): 801–16. http://dx.doi.org/10.2298/gensr2202801a.
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The current study in tomato (Solanum lycopersicum L.) was conducted at PAU, Ludhiana with the objective of developing hybrids possessing combined resistance to late blight and leaf curl virus diseases along with desirable horticultural characteristics. The experimental material which included 32 F1 hybrids (developed by line ? tester method), 12 parental lines (8 lines and 4 testers; including susceptible check Punjab Chhuhara) and standard check NS-524 were all planted in randomized complete block design with three replications. The values of ?2SCA/?2GCA were more than unity for all the traits except average fruit weight and ascorbic acid content, indicating the predominance of non-additive gene effects. Cross combinations CLN-154 ? LBR-12 and CLN-154 ? LBR-21 recorded significant heterosis over better parent and check for fruit yield and other quality characteristics. Artificial and natural screening was performed for all the experimental material against late blight and leaf curl virus diseases respectively. Out of 32 hybrids, crosses namely CLN-154? LBR-12, CLN-154 ? LBR-21, PVB-1 ? LBR-10, PVB-4 ? LBR-12 and CLN-104 ? LBR-10 were identified for combined disease resistance against late blight and leaf curl virus, in relation to desirable horticultural characteristics particularly fruit yield, average fruit weight, pericarp thickness, dry matter, titrable acidity and ascorbic acid content with fair amount of heterosis. Hence, the hybrids which displayed good potential in yield with acceptable performance of qualitative traits, along with combined disease resistance could be utilized for commercial exploitation.
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Ivić, Dario, Luka Popović, Ivana Križanac, and Mario Bjeliš. "Etiology of Colletotrichum diseases on Sastuma mandarin in Croatia." Pomologia Croatica 25, no. 1-4 (January 2, 2023): 3–18. http://dx.doi.org/10.33128/pc.25.1-4.1.
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During the last decade anthracnose has become a major disease of Satsuma mandarin, the most important citrus crop in Croatia. The aim of this study was to determine Colletotrichum species associated with different symptoms and to identify the origin of inoculum. From 2013 to 2016, 437 samples of plant material were collected. Colletotrichum spp. was isolated from 93% of dried twigs, 35% of dropped flowers, 89% of leaf spots, all fruit (100%) with anthracnose or calyx-end rot symptoms, 12% of fruit with post-harvest soft rot and from 40% of fruit showing spots remaining on trees after harvest. Out of 258 Colletotrichum isolates, 253 has been morphologically identified as C. gloeosporioides (Penz.) Penz. & Sacc. species complex. Twenty-seven representative isolates were selected for phylogenetic analysis. Sequencing the inter-spacer gene region of ribosomal DNA confirmed the identity of the species. Artificial inoculation of flowers led to more than 2-fold higher young fruit drop compared to control. Pathogenicity tests on green fruit induced typical anthracnose symptoms on 82% of inoculated fruit two months after inoculation. Inoculation of mature fruit caused the appearance of typical anthracnose symptoms on 87% of inoculated fruit. These results showed that C. gloeosporioides species complex is responsible for different disease types on Satsuma mandarin, and that the fungus is present throughout the year on different plant organs.
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Egra, S., M. R. Syaputra, E. Rosamah, H. Kuspradini, A. S. Putri, and K. Yamauchi. "Antibacterial and Antioxidant activity of Leaf and Fruit from Jatropha curcas." IOP Conference Series: Earth and Environmental Science 1282, no. 1 (December 1, 2023): 012037. http://dx.doi.org/10.1088/1755-1315/1282/1/012037.
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Abstract Jatropha curcas is often used as a medicinal plant by traditional ethnic to treat skin and dental diseases. This study aims to reveal the antibacterial and antioxidant activities of leaves and fruit. The antibacterial activity was determined using the agar well method with Streptococcus sobrinus and the antioxidant activity of DPPH free radical scavengers. Inhibition of bacteria S. sobrinus did not appear to be significant with concentrations of 10, 20, 40 µg/ml, on the other hand antioxidant activity with the highest inhibition was found in methanol leaves with a value of 53.5% and 51.9% methanol in fruit with concentration 100 ppm. IC50 value on the leaves and fruit of J. curcas with a value of 50%. J. curcas leaf and fruit have good activity in transferring protons in DPPH activity. There needs to be further research in isolate the compounds that play a role in balancing these free radicals.
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Bihon, Wubetu, Kukom Edoh Ognakossan, Jean-Baptiste Tignegre, Peter Hanson, Kabirou Ndiaye, and Ramasamy Srinivasan. "Evaluation of Different Tomato (Solanum lycopersicum L.) Entries and Varieties for Performance and Adaptation in Mali, West Africa." Horticulturae 8, no. 7 (June 27, 2022): 579. http://dx.doi.org/10.3390/horticulturae8070579.
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Tomato is an important vegetable crop and plays a major role in the food and nutrition security of the people of Mali. Production has increased in the recent decades but improvement in the fruit yield and quality remains suboptimal. Limited access to the best-adapted tomato varieties to the local conditions, pests and diseases are the major limiting factors for improving productivity. This study evaluated the performance of different tomato entries and varieties for their productivity, resistance to pests and diseases and postharvest fruit quality in Mali. Twenty-two entries and varieties of tomato in the rainy season and twenty-four in the dry season were evaluated. Varieties that were well adapted, better yielded, disease resistant and with good fruit quality were identified. Major plant diseases observed included tomato yellow leaf curve disease (TYLCD), bacterial wilt, bacterial leaf spot, early blight and southern blight. However, TYLCD was the major problem during the dry season. The variety of Icrixina was the most affected by TYLCD in both the rainy and dry seasons, although its total yield was not affected and remained one of the highest. Konica was one of the most susceptible varieties to bacterial wilt and bacterial leaf spot diseases. Tomato accession AVTO1710 provided the highest fruit yield (40.9 t/ha), while AVTO1704 provided the lowest (6.50 t/ha) in the rainy season. In contrast the highest yield during the dry growing season was 20 t/ha from VIO43614. Tomato entries and varieties varied in their postharvest fruit quality attributes (firmness, total soluble solid, pH and dry matter). Production season clearly influenced yield, disease occurrence and severity, as well as postharvest fruit qualities. The study identified better disease-resistant and yielding tomato entries suitable for rainy and dry growing seasons, which can be considered and scaled up for production so that farmers in Mali can produce tomato all year round.
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Pai, Akshaya, and Chandrakala Shenoy. "Hepatoprotective activity of Flacourtia jangomas (Lour.) Raeuschleaves and fruit methanolic extract on paracetamol-induced hepatotoxicity in HepG2 Cells." Biomedicine 41, no. 3 (October 25, 2021): 587–91. http://dx.doi.org/10.51248/.v41i3.1197.
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Introduction and Aim: Plants have become the current focus of research in treating the various diseases and ailments. Flacourtia jangomas (Lour.) Raeusch belongs to the familySalicaceae. Itis a small deciduous fruit tree having immense nutritional and medicinal significance. Different parts of the plant are pharmaceutically used forcuring various ailments. In this study, we investigated the hepatoprotective activity of Flacourtia jangomas (Lour.) Raeusch leaves and fruit methanolic extract on Paracetamol induced HepG2 cell line.
Methods: The cytotoxic and hepatoprotective properties were evaluated by measuring cell viability; activities of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH); lipid peroxidation (malondialdehyde (MDA) levels).
Results:The increased cell viability of 140.43± 4.07% and 133.93±3.20%was observed in HepG2 cells treated with methanolic extract of F. jangomas leaf and fruit extract respectively at 10µg/ml concentration and then decreased along with the rise of F. jangomas leaf and fruit extract concentrations. The level of LDH, ALT, AST and MDA decreased after F. jangomas leaf and fruit treatment compared to negative control.
Conclusion: This study suggests that the methanolic Extract of F. jangomas (Lour.) Raeusch leaves(FJL)and fruit (FJF) shows hepatoprotective activity in Paracetamol induced HepG2 cell line by the decrease in AST and ALT activities and LDH and MDA level. Hence, it could be considered as a therapeutic agent in curing liver-related diseases.
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Badiche-El Hilali, Fátima, Juan Miguel Valverde, María E. García-Pastor, María Serrano, Salvador Castillo, and Daniel Valero. "Melatonin Postharvest Treatment in Leafy ‘Fino’ Lemon Maintains Quality and Bioactive Compounds." Foods 12, no. 15 (August 7, 2023): 2979. http://dx.doi.org/10.3390/foods12152979.
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Spain is a great producer of organic lemon; however, it is necessary to reduce the losses caused by post-harvest diseases. Melatonin (MEL) is a naturally occurring compound with physiological functions in fruit growth and ripening and is able to modulate postharvest ripening and senescence, most of it being concentrated in climacteric fruit. Thus, the aim of this study was to apply MEL to organic lemon fruit with stems and leaves (LEAF) and to organic lemon without those components (LEAFLESS) after harvesting and storage during 21 days at 2 °C to understand the effects of this treatment on the fruit quality. For this purpose, two experiments were carried out. First, MEL was applied at 0.01 mM, 0.1 mM and 1.0 mM by immersion for 15 min on lemon fruits, and the quality parameters and bioactive compounds of the fruit were analysed. Subsequently, a second experiment was carried out where the best concentration (1 mM) was selected and another time (15 and 30 min) was added, with the same quality parameters being analysed. As a result, we observed that all MEL treatments showed positive effects on weight loss reduction, softening (higher fruit firmness), total acidity and lower colour changes. Total phenols increased in MEL-treated lemons, both in peel and juice. For the three concentrations tested, the best efficiency was obtained with MEL at 1.0 mM, while LEAF lemons were the most effective. In conclusion, lemons containing stems and leaves (LEAF) improved preservability by using MEL at 1.0 mM with better organoleptic quality and enhanced phenolic compounds.
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Abadie, Catherine, Marie-Françoise Zapater, Luc Pignolet, Jean Carlier, and Xavier Mourichon. "Artificial inoculation on plants and banana leaf pieces withMycosphaerellaspp., responsible for Sigatoka leaf spot diseases." Fruits 63, no. 5 (September 2008): 319–23. http://dx.doi.org/10.1051/fruits:2008030.
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Su, Yu-Chang, and Chen-Lung Ho. "Essential Oil Compositions and Antimicrobial Activities of Various Parts of Litsea cubeba from Taiwan." Natural Product Communications 11, no. 4 (April 2016): 1934578X1601100. http://dx.doi.org/10.1177/1934578x1601100425.
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The essential oils of leaves, fruits, flowers, stems and twigs of Litsea cubeba were extracted by hydrodistillation. A total of 53, 50, 76, 94 and 90 compounds were identified from the leaf, fruit, flower, stem and twig oils, respectively, and their yields were 13.9 ± 0.09, 4.0 ± 0.03, 10.4 ± 0.05, 0.09 ± 0.01 and 0.4 ± 0.02 mL/100 g of the oven-dried (o.d.) materials, respectively. The main component in the leaf, flower and twig oils was 1,8-cineole, whereas in the fruit oil it was citral, and in the stem oil limonene, citronellal, and citronellol. When tested for their antibacterial activities using the paper disc diffusion method, oils from all parts showed excellent activities, particularly the fruit oil. When the oils were infused onto filter paper and tested for their antimicrobial paper capability according to the JIS L1902 method, the fruit oil exhibited excellent antimicrobial activities. Citral was deemed the main cause of the antimicrobial activity. With the multiplicity of contagious diseases and their prevalence in hospitals, these essential oils present a potentially good choice as antibacterial agents. We think that the essential oils of this species are capable of multipurpose applications.
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Defitri, Yuza. "Pengamatan Penyakit pada Tanaman Pinang (Areca Catechu L.) di Desa Mandala Jaya Kecamatan Betara Kabupaten Tanjung Jabung Barat." Jurnal Ilmiah Universitas Batanghari Jambi 22, no. 3 (October 31, 2022): 1806. http://dx.doi.org/10.33087/jiubj.v22i3.2984.
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Areca nut (Areca catechu L.) is one of the leading plantation commodities in Jambi Province which has high economic value and plays an important role as a source of foreign exchange for the country. Areca nut plants in Jambi Province are most widely located in Betara District, West Tanjung Jabung Regency. To increase the yield of areca nut, it is necessary to know the diseases that attack areca nut and the percentage of the disease attack. It is useful for disease control in areca nut. This study aims to find out about the main diseases that attack areca nut plants such as yellowing leaf spot, leaf blight, leaf red rust, root rot/base of stem, fruit rot, shoot rot, yellow leaves, foot rot, flower die back and fruit drop, and Bacterial leaf stripe. The research was conducted using the Simple Random Sampling method. Plant samples were taken randomly at the people's areca nut plantations in Mandala Jaya Village, Betara District. Observations were made by looking at the symptoms of the disease and calculating the percentage of plants affected by the disease. Samples of areca nut plant parts that were attacked by the disease were identified at the Unbari Basic Laboratory.The research that has been conducted in Mandala Jaya Village, Betara District, Tanjung Jabung Barat Regency, it can be concluded that three main diseases were found in areca nut, namely yellowing leaf spot caused by the fungus Curvularia sp, leaf blight disease caused by the Pestalotia palmarum fungus and red rust leaf disease caused by the fungus Cephaleuros sp. The percentage of disease attacks, namely, yellow leaf spot disease (Curvularia sp.) was highest on P1 land by 40%, the highest percentage of Leaft Blight disease was found in P1 area at 35% and the highest percentage of red rust leaf disease was found in P1 area, which was 20%.
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Noon, Serosh Karim, Muhammad Amjad, Muhammad Ali Qureshi, Abdul Mannan, and Tehreem Awan. "An Improved Detection Method for Crop & Fruit Leaf Disease under Real-Field Conditions." AgriEngineering 6, no. 1 (February 9, 2024): 344–60. http://dx.doi.org/10.3390/agriengineering6010021.
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Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.
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Et. al., Jayashri Patil,. "POMEGRANATE FRUIT DISEASES DETECTION USING IMAGE PROCESSING TECHNIQUES: A REVIEW." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (March 21, 2021): 115–20. http://dx.doi.org/10.17762/itii.v9i2.310.
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The Agriculture plant diseases are responsible for farmer economic losses. These diseases affect on plant root, fruit, leaf, and stem. Detection of disease at early stages helps the farmer to improve productivity. In the traditional system agriculture experts and experienced farmer can recognize the plant diseases at the lower accuracy which causes losses to farmers. Currently several researchers are proposing soft computing and expert systems to recognize plant diseases. Plant disease identification by visual way is less accurate because some diseases do not have any visible symptoms or some of the diseases appear too late at the time of harvesting. The modern technology in agriculture sector can substantially improve the agriculture production & sustainability. This paper provides a review for fruit disease detection techniques for pomegranate plants. This study includes preprocessing, segmentation, feature extraction and classification techniques for pomegranate fruit diseases detection systems. This paper also states the comparison and limitations of existing fruit disease detection techniques.
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Desvignes, J. C., and R. Boyé. "DIFFERENT DISEASES CAUSED BY THE CHLOROTIC LEAF SPOT VIRUS ON THE FRUIT TREES." Acta Horticulturae, no. 235 (April 1989): 31–38. http://dx.doi.org/10.17660/actahortic.1989.235.3.
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Lu, Jiangwen, Bibo Lu, Wanli Ma, and Yang Sun. "EAIS-Former: An efficient and accurate image segmentation method for fruit leaf diseases." Computers and Electronics in Agriculture 218 (March 2024): 108739. http://dx.doi.org/10.1016/j.compag.2024.108739.
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Mumtaz, Sidrah, Mudassar Raza, Ofonime Dominic Okon, Saeed Ur Rehman, Adham E. Ragab, and Hafiz Tayyab Rauf. "A Hybrid Framework for Detection and Analysis of Leaf Blight Using Guava Leaves Imaging." Agriculture 13, no. 3 (March 13, 2023): 667. http://dx.doi.org/10.3390/agriculture13030667.
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Fruit is an essential element of human life and a significant gain for the agriculture sector. Guava is a common fruit found in different countries. It is considered the fourth primary fruit in Pakistan. Several bacterial and fungal diseases found in guava fruit decrease production daily. Leaf Blight is a common disease found in guava fruit that affects the growth and production of fruit. Automatic detection of leaf blight disease in guava fruit can help avoid decreases in its production. In this research, we proposed a CNN-based deep model named SidNet. The proposed model contains thirty-three layers. We used a guava dataset for early recognition of leaf blight, which consists of two classes. Initially, the YCbCr color space was employed as a preprocessing step in detecting leaf blight. As the original dataset was small, data augmentation was performed. DarkNet-53, AlexNet, and the proposed SidNet were used for feature acquisition. The features were fused to get the best-desired results. Binary Gray Wolf Optimization (BGWO) was used on the fused features for feature selection. The optimized features were given to the variants of SVM and KNN classifiers for classification. The experiments were performed on 5- and 10-fold cross validation. The highest achievable outcomes were 98.9% with 5-fold and 99.2% with 10-fold cross validation, confirming the evidence that the identification of Leaf Blight is accurate, successful, and efficient.
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Gong, Xulu, and Shujuan Zhang. "A High-Precision Detection Method of Apple Leaf Diseases Using Improved Faster R-CNN." Agriculture 13, no. 2 (January 19, 2023): 240. http://dx.doi.org/10.3390/agriculture13020240.
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Apple leaf diseases seriously affect the sustainable production of apple fruit. Early infection monitoring of apple leaves and timely disease control measures are the key to ensuring the regular growth of apple fruits and achieving a high-efficiency economy. Consequently, disease detection schemes based on computer vision can compensate for the shortcomings of traditional disease detection methods that are inaccurate and time-consuming. Nowadays, to solve the limitations ranging from complex background environments to dense and small characteristics of apple leaf diseases, an improved Faster region-based convolutional neural network (Faster R-CNN) method was proposed. The advanced Res2Net and feature pyramid network architecture were introduced as the feature extraction network for extracting reliable and multi-dimensional features. Furthermore, RoIAlign was also employed to replace RoIPool so that accurate candidate regions will be produced to address the object location. Moreover, soft non-maximum suppression was applied for precise detection performance of apple leaf disease when making inferences to the images. The improved Faster R-CNN structure behaves effectively in the annotated apple leaf disease dataset with an accuracy of 63.1% average precision, which is higher than other object detection methods. The experiments proved that our improved Faster R-CNN method provides a highly precise apple leaf disease recognition method that could be used in real agricultural practice.
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Park, Doie, Geleta Dugassa Barka, Eun-Young Yang, Myeong-Cheoul Cho, Jae Bok Yoon, and Jundae Lee. "Identification of QTLs Controlling α-Glucosidase Inhibitory Activity in Pepper (Capsicum annuum L.) Leaf and Fruit Using Genotyping-by-Sequencing Analysis." Genes 11, no. 10 (September 23, 2020): 1116. http://dx.doi.org/10.3390/genes11101116.
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Diabetes mellitus, a group of metabolic disorders characterized by hyperglycemia, is one of the most serious and common diseases around the world and is associated with major complications such as diabetic neuropathy, retinopathy, and cardiovascular diseases. A widely used treatment for non-insulin-dependent diabetes is α-glucosidase inhibitors (AGIs) such as acarbose, which hinders hydrolytic cleavage of disaccharides and retard glucose absorption. The ability to inhibit α-glucosidase activity has been reported in leaf and fruit of pepper (Capsicum annuum L.). In this study, we aimed to identify quantitative trait loci (QTLs) controlling α-glucosidase inhibitory activity (AGI activity) in pepper leaf and fruit using enzyme assay and genotyping-by-sequencing (GBS) analysis. The AGI activity at three stages of leaf and one stage of fruit development was analyzed by 96 F2 individuals. GBS analysis identified 17,427 SNPs that were subjected to pepper genetic linkage map construction. The map, consisting of 763 SNPs, contained 12 linkage groups with a total genetic distance of 2379 cM. QTL analysis revealed seven QTLs (qAGI1.1, qAGI11.1, qAGI5.1, qAGI9.1, qAGI12.1, qAGI5.2, and qAGI12.2) controlling AGI activity in pepper leaf and fruit. The QTLs for AGI activity varied by plant age and organ. This QTL information is expected to provide a significant contribution to developing pepper varieties with high AGI activity.
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Indur, Basawarajeshwari, D. Vishwajit, V. Ambrish, and V. Prashant. "Abundance of Phytoparasitic Root Nematodes Associated with Tomato (Solanum lycopersicum Mill.) in Kalaburagi, Karnataka, India." International Journal of Environment and Climate Change 13, no. 5 (March 18, 2023): 17–30. http://dx.doi.org/10.9734/ijecc/2023/v13i51738.
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Tomato (Solanum lycopersicum Mill.) is the most commonly grown prime vegetable crop in India and all around the world. Tomato is cultivated majorly in many states of the nation including Karnataka and plays an important role in the Indian economy. The ripe tomato fruits act as the best source of vitamin A, Vitamin D and also have various antioxidant properties. Tomato crop progressively gets infested by various diseases at different stages from vegetation up to fruiting. The diseases such as bacterial leaf spot, bacterial wilt, leaf curl, fruit canker and Septoria leaf spot are caused by different Bacteria, Viruses and Fungi. Nematodes also result in root-knot, stunting and fusarium wilt diseases which may reduce the crop yield and fruit quality. The present survey was carried out from June 2021 to November 2021 in selected tomato plots of Kalaburagi district to identify different species of root nematodes affecting tomato crop in the selected study area. According to the survey results a total of six species of root nematodes were isolated and identified namely Meloidogyne spp., Globodera Spp., Paratrichodors minor, Helicotylenchus dihystera, Pratylenchus spp. and Rotylenchus buxophilus from selected study plots of Nirgudi, Bhosga, Bhosga Tanda, Gobbur, Sannur, Nadikur and Khanadal. The results suggest that Meloidogyne sps and Paratrichodors minor were most commonly recorded. The highest abundance of these nematodes recrded in Nirgudi region and in Nandikur region population of these root nematodes is least abundant.
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Bano, Ilham, and Deora G.S. "Studies on macromorphological taxonomic variations in Abutilon species of Indian Thar Desert." Annals of Plant Sciences 7, no. 1 (January 1, 2018): 1929. http://dx.doi.org/10.21746/aps.2018.7.1.1.
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Abutilon is an important medicinal plant. Its various plant parts such as leaves, flowers, fruits and seeds were used to treat various diseases and ailments from the ancient time. Present work deals with the investigation of three species of Abutilonviz. Abutilon indicum, Abutilon pannosumand Abutilon ramosum with a view to study macro morphological variations and to identify a set of diagnostic characters for individual Abutilon species. Distinct variations exist in stem surface and colour, leaf shape and size, flower diameter, fruit colour, shape and size, number of mericarps per fruit and seed structure. All these macromorphological variations were helpful in identification and delineation of the plant species.
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Babadoost, M. "Outbreak of Phytophthora Foliar Blight and Fruit Rot in Processing Pumpkin Fields in Illinois." Plant Disease 84, no. 12 (December 2000): 1345. http://dx.doi.org/10.1094/pdis.2000.84.12.1345a.
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Approximately 65% of the total commercial processing pumpkins (Cucurbita moschata Poir.) in the United States are produced in central Illinois. In 1999, Phytophthora capsici caused severe foliar blight and fruit rot in processing pumpkin fields in Illinois. Infection was widely observed in July when fruit weights were approximately 5 kg and continued until harvest in late August. Infection of the fruit generally started on the side contacting the soil. However, when an infected leaf came in contact with a fruit, fruit rot started at the site of contact. Many fruits that looked normal fell apart when they were turned for examination. Infected fruit were generally covered with white, cottony growth consisting of mycelium, sporangiophores, and sporangia. Leaf infection began as small chlorotic lesions, which enlarged and became necrotic. Leaf petioles also were infected and developed lesions that girdled petioles, causing the collapse and death of leaves. Vines also were infected and developed girdling lesions. The girdling lesions, which caused collapse and death of the vines, were observed on all parts of the vines. Affected vines collapsed and died. Roots and crowns of the plants with foliar blight and fruit rot exhibited little brownish discoloration or no symptoms. In most fields, the disease started in low-lying areas but spread rapidly throughout the field. The disease occurred in both irrigated and nonirrigated fields. In August, approximately 1 week before harvest, one nonirrigated and eight irrigated fields, a total of 267 ha, were surveyed to assess the incidence of disease. The incidence of disease was determined by examining vines, leaves, and fruit in 10 plots (36 m2 each) per field by walking a path on the longest diagonal of each field. In each plot, 10 plants were inspected, with one vine, 10 leaves on the vine, and one fruit of each plant (total of 10 vines, 100 leaves, and 10 fruits in each plot) were examined for infection. The incidence of vine blight, leaf blight, and fruit rot in the nonirrigated field was 30, 50, and 49%, respectively. The incidence of vine blight, leaf blight, and fruit rot in irrigated fields ranged from 4 to 48% (average 21%), 17 to 68% (average 40%), and 4 to 71% (average 32%), respectively. The incidence of vine blight, leaf blight, and fruit rot were highly correlated. Due to severe fruit rot, two of the irrigated fields were not harvested. In Illinois, processing pumpkins are planted in May and harvested in August. Recorded precipitation in the pumpkin growing area in Illinois in 1999, was 9 days (211 mm), 7 days (113 mm), 7 days (147 mm), and 7 days (91 mm) in May, June, July, and August, respectively. It is believed that the frequent and high rainfall during the growing season in the area resulted in the outbreak of Phytophthora foliar and fruit rot in processing pumpkins in Illinois in 1999. References: (1) D. C. Erwin and O. K. Ribeiro. 1996. Phytophthora Diseases Worldwide. The American Phytopathological Society, St. Paul, MN. (2) M. T. McGrath. 1998. Biological and Cultural Tests. The American Phytopathological Society, St. Paul, MN.
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Radicevic, Sanja, Radosav Cerovic, Milan Lukic, Svetlana Paunovic, Darko Jevremovic, Slobodan Milenkovic, and Milisav Mitrovic. "Selection of autochthonous sour cherry (Prunus cerasus L.) genotypes in Feketic region." Genetika 44, no. 2 (2012): 285–97. http://dx.doi.org/10.2298/gensr1202285r.
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Autochthonous genotypes of fruit species are very important source of genetic variability and valuable material for breeding work. Fruit Research Institute-Cacak has a long tradition of studying autochthonous genotypes of temperate fruits sporadically spread and preserved in some localities in Serbia. Over 2005-2006, the following properties of nine autochthonous sour cherry genotypes grown in Feketic region were investigated: flowering and ripening time, pomological properties, biochemical composition of fruits and field resistance to causal agents of cherry diseases - cherry leaf spot (Blumeriella jaapii (Rehm.) v. Arx.), shot-hole (Clasterosporium carpophilum (L?v.) Aderh.) and brown rot (Monilinia laxa /Ader et Ruhl./ Honey ex Whetz.). The genotypes were tested for the presence of Prune dwarf virus and Prunus necrotic ring spot virus. In majority of genotypes fruits were large, with exceptional organoleptical properties, whereas ripening time was in the first ten or twenty days of June. The highest fruit weight was observed in F-1 genotype (8.1 g). The highest soluble solids and total sugars content were found in F- 4 genotype (17.60% and 14.25%, respectively). As for field resistance to causal agents of diseases and good pomo-technological properties, F-1, F-2, F-3, F-7 and F-8 genotypes were singled out.