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Добірка наукової літератури з теми "APPLE FOLIAR DISEASE"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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Статті в журналах з теми "APPLE FOLIAR DISEASE"

Sulaiman, Adel, Vatsala Anand, Sheifali Gupta, Hani Alshahrani, Mana Saleh Al Reshan, Adel Rajab, Asadullah Shaikh, and Ahmad Taher Azar. "Sustainable Apple Disease Management Using an Intelligent Fine-Tuned Transfer Learning-Based Model." Sustainability 15, no. 17 (September 4, 2023): 13228. http://dx.doi.org/10.3390/su151713228.

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Apple foliar diseases are a group of diseases that affect the leaves of apple trees. These diseases can significantly impact apple tree health and fruit yield. Ordinary apple foliar diseases include frog_eye_leaf_spots, powdery mildew, rust, apple scabs, etc. Early detection of these diseases is important for effective apple crop management to increase the yield of apples. Therefore, this research proposes a fine-tuned EfficientNetB3 model for the quick and precise assessment of these apple foliar diseases. A dataset containing 23,187 RGB images of eleven different apple foliar diseases is used for experimentation. The proposed model is compared with four transfer learning models, i.e., InceptionResNetV2, ResNet50, AlexNet, and VGG16. All models are fine-tuned by adding different layers like the global average pooling layer, flatten layer, dropout layer, and dense layer. The performance of these five models is compared in terms of the precision, recall, accuracy, and F1-score. The EfficientNetB3 outperformed the other models in terms of all performance parameters. The best model is further optimized with the help of three optimizers, i.e., Adam, SGD, and Adagrad. The proposed model achieved the precision, recall, and F1-score values of 86%, 88%, and 86%, respectively, at 32 batch sizes and 10 epochs. This research formulated a model for an apple foliar disease diagnosis within sustainable agriculture.

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Charan, Merugu Sai, Mohammed Abrar, and Bejjam Vasundhara Devi. "Apple Leaf Diseases Classification Using CNN with Transfer Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1905–12. http://dx.doi.org/10.22214/ijraset.2022.44176.

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Abstract: The Foliar diseases of the apple trees commonly reduce the crop Yield and photosynthesis which affects their productivity. Diagnosing foliar damage is not easy if there are no distinct patterns that would be fungal fruiting bodies it will. spread to the rest of the crops. The foliar disease of the apple trees is carried out due to biotic and abiotic causes, some of the biotic causes of foliar damage are - Bacterial Disease, Fungal Diseases, Viral Diseases, Insects, and Mites That Damage Foliage. some of the Abiotic causes are - Iron Chlorosis, Misapplied Herbicide, and Winter Desiccation of Evergreens. Traditional approaches rely on visual inspection by an expert and biological examination is the second choice .these approaches are time-consuming and expensive. we use machine learning methods to classify the disease in apple trees. we use some pre-trained CNN models to extract features from the dataset, we applied the CNN model and compared them with Pre-trained Models, and we achieve accuracies of over 93% with CNN, among the Models We achieved 92% with the Inception V3 model,62% with VGG16, 63% with VGG19.

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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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Blaedow, Ryan, William Chaney, Paul Pecknold, and Harvey Holt. "Investigation of Fungicidal Properties of the Tree Growth Regulator Paclobutrazol to Control Apple Scab." Arboriculture & Urban Forestry 32, no. 2 (March 1, 2006): 67–73. http://dx.doi.org/10.48044/jauf.2006.009.

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Paclobutrazol (PBZ) as a systemic fungicide for control of apple scab (Venturia inaequalis) was investigated in mature (cv. Hopa and Snow Drift) and young sapling (cv. Indian Magic) crabapples (Malus spp.). Treatments consisted of a control and PBZ applied to mature trees at one or two times the recommended rate in April 2002 using the basal drench method. Saplings received either foliar or soil drench applications of PBZ, or foliar applications of propiconazole. Disease assessments of mature trees showed that apple scab symptoms in treated trees were as severe as in untreated ones in the year of treatment but were reduced slightly the year after treatment in ‘Hopa’ and the third year after treatment in ‘Snow Drift.’ Growth reduction occurred in all treated trees, suggesting that the PBZ levels needed for growth reduction were not sufficient to control apple scab in the year of treatment. In contrast, a one-time foliar application of PBZ reduced apple scab incidence to levels found in ‘Indian Magic’ saplings treated every 2 weeks with propiconazole, a fungicide and application method commonly recommended for apple scab control. Delayed uptake and insufficient transport of PBZ to the foliage of mature trees after root drench treatments may account for the lack of apple scab control in the years after treatment, even though growth suppression occurred.

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Hirakue, Ayumi, and Shuichi Sugiyama. "Relationship between foliar endophytes and apple cultivar disease resistance in an organic orchard." Biological Control 127 (December 2018): 139–44. http://dx.doi.org/10.1016/j.biocontrol.2018.09.007.

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Peng, H. X., X. Y. Wei, Y. X. Xiao, Y. Sun, A. R. Biggs, M. L. Gleason, S. P. Shang, M. Q. Zhu, Y. Z. Guo, and G. Y. Sun. "Management of Valsa Canker on Apple with Adjustments to Potassium Nutrition." Plant Disease 100, no. 5 (May 2016): 884–89. http://dx.doi.org/10.1094/pdis-09-15-0970-re.

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Valsa canker, caused by the fungus Valsa mali, is one of the most destructive diseases of apple in the primary production areas of China and other East Asian countries. Currently, there are no effective control methods for this disease. We investigated the occurrence of Valsa canker in 24 apple orchards in Shaanxi Province in concert with foliar nutrient analysis, and found that there was a significant negative correlation of leaf potassium (K) content with incidence and severity of Valsa canker. Fertilization experiments showed that increasing tree K content enhanced resistance to pathogen colonization and establishment. Apple trees with leaf K content greater than 1.30% exhibited almost complete resistance to Valsa mali. Field trials demonstrated that increasing K fertilization could significantly reduce disease incidence. Improved management of tree nutrition, especially K content, could effectively control the occurrence and development of Valsa canker.

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Prokopy, Ronald J. "A LOW-SPRAY APPLE-PEST-MANAGEMENT PROGRAM FOR SMALL ORCHARDS." Canadian Entomologist 117, no. 5 (May 1985): 581–85. http://dx.doi.org/10.4039/ent117581-5.

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AbstractFrom 1981 to 1984, a low-spray management program was employed against injurious arthropods on the 40 disease-resistant apple trees in my experimental orchard in Massachusetts. The program consisted of an annual early-season application of petroleum oil followed by 2 applications of phosmet (1 at petal fall and another 10–14 days later). Visual traps were used to suppress Rhagoletis pomonella flies. For all years combined, a mean of 89.7% of fruit sampled at harvest in this orchard was free of insect injury compared with 0% uninjured fruit on neighboring unsprayed trees. Populations of foliar-feeding pests never reached injurious levels.

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Percival, Glynn. "The Influence of Inducing Agents Applied by Soil Drenches on Disease Severity of Apple and Pear Scab." Arboriculture & Urban Forestry 46, no. 5 (September 1, 2020): 358–70. http://dx.doi.org/10.48044/jauf.2020.026.

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Apple and pear scab are foliar diseases of ornamental and fruiting apple and pear trees. Unmanaged, yield and aesthetic losses can be severe. Overreliance on synthetic fungicides means novel means of disease management are required. Field trials were conducted using apple (Malus cv. Crown Gold) and pear (Pyrus communis ‘Williams Bon Chrétien’) to assess the efficacy of a range of commercially available inducing resistance (IR) agents (harpin protein, potassium phosphite, salicylic acid derivative, and chitosan) as root drenches against both scab diseases. A synthetic fungicide (penconazole) spray program used within the UK for apple and pear scab control was included for comparison. Each IR agent was applied four times, (i) before the visible appearance of scab (April through June, i.e., preventatively) or (ii) after symptoms of scab were visibly observed (June through August, i.e., curatively). Limited efficacy as scab protectants was demonstrated when IR agents were applied curatively. Likewise, limited efficacy was recorded when IR agents were applied once or twice as a preventative measure. However, when IR agents were applied as root drenches greater or equal to three times, efficacy as scab protectants was confirmed (increased leaf chlorophyll content, increased fruit yield, reduced leaf and fruit scab severity). A synthetic fungicide penconazole spray program provided the greatest protection against apple and pear scab in all trials when sprayed preventatively rather than curatively. Results suggest application of at least three root drenches from April through June with an appropriate IR agent provides a useful addition to existing methods of apple and pear scab management under field conditions.

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Rebel, P., C. Poblete-Echeverría, J. G. van Zyl, J. P. B. Wessels, C. Coetzer, and A. McLeod. "Determining Mancozeb Deposition Benchmark Values on Apple Leaves for the Management of Venturia inaequalis." Plant Disease 104, no. 1 (January 2020): 168–78. http://dx.doi.org/10.1094/pdis-04-19-0873-re.

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Apple scab, caused by Venturia inaequalis, is the most common fruit and foliar disease in commercial apple production worldwide. Early in the production season, preventative contact fungicide sprays are essential for protecting highly susceptible continuously unfolding and expanding young leaves. In South Africa, mancozeb is a key contact fungicide used for controlling apple scab early in the season. The current study developed deposition benchmarks indicative of the biological efficacy of mancozeb against apple scab, using a laboratory-based apple seedling model system. The model system employed a yellow fluorescent pigment that is known to be an effective tracer of mancozeb deposition. A concentration range of mancozeb (0.15 to 1 times the registered dosage) and fluorescent pigment concentrations was sprayed onto seedling leaves, which yielded various fluorescent particle coverage (FPC%) levels. Modeling of the FPC% values versus percent disease control yielded different benchmark values when disease quantification was conducted using two different methods. Thermal infrared imaging (TIRI) disease quantification resulted in a benchmark model where 0.40%, 0.79%, and 1.35 FPC% yielded 50, 75, and 90% apple scab control, respectively. These FPC% values were higher than the benchmarks (0.10, 0.20, and 0.34 FPC%, respectively) obtained with quantitative real-time PCR (qPCR) disease quantification. The qPCR benchmark model is recommended as a guideline for evaluating the efficacy of mancozeb sprays on leaves in apple orchards since the TIRI benchmark model underestimated disease control. The TIRI benchmark model yielded 68% disease control at the lowest mancozeb dosage, yet no visible lesion developed at this dosage. Both benchmark models showed that mancozeb yielded high levels of disease control at very low concentrations; for the qPCR benchmark model the FPC% value of the FPC90 (90% control) corresponded to 0.15 times that of the registered mancozeb concentration in South Africa, i.e., 85% lower than the registered dosage.

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Prokopy, Ronald J., Daniel R. Cooley, Wesley R. Autio, and William M. Coli. "Second-level integrated pest management in commercial apple orchards." American Journal of Alternative Agriculture 9, no. 4 (December 1994): 148–56. http://dx.doi.org/10.1017/s0889189300005890.

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AbstractAs historical background helpful to understanding current concepts and practices of apple pest management, we review the origin and rise of key pests of apple in North America and the evolution of approaches to their management, culminating with the concept of integrated pest management (IPM). We propose four levels of integration of orchard pest management practices. First-level IPM integrates chemically based and biologically based management tactics for a single class of pests, such as arthropods, diseases, weeds or vertebrates. Second-level IPM, the focus of our effort here, integrates multiple management tactics across all classes of pests. We describe components of second-level IPM for Massachusetts apple orchards, which are threatened each year by an exceptionally broad range of injurious pests. We illustrate the tentative advantages and shortcomings of second-level IPM using 1993 data from six commercial orchard test blocks. Our predominant approach was to use chemically based tactics for controlling arthropods, diseases and weeds early in the growing season, and afterwards to rely exclusively (for insects) or largely (for other pests) on biologically based tactics, such as cultural, behavioral, and biological controls. Compared with nearby first-level IPM blocks, insecticide use in 1993 was reduced substantially (about 30%), with only slightly more insect injury to fruit and little difference in populations of foliar insect pests. The results for mite pests and diseases were less encouraging although summer pruning significantly reduced disease injury caused by flyspeck. We discuss how second-level IPM poses special biological or operational challenges to apple pest management practitioners. The concept has merit, but refinements are necessary before it can be recommended broadly to commercial apple growers in Massachusetts as an economical and reliable alternative to first-level IPM.

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Більше джерел

Дисертації з теми "APPLE FOLIAR DISEASE"

Furlan, Carla Regina Costa. "Avaliação da diversidade genética e da resistência a mancha foliar da gala em acessos de macieira do banco ativo de germoplasma da E.E.Epagri/Caçador." Universidade do Estado de Santa Catarina, 2008. http://tede.udesc.br/handle/handle/1117.

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Made available in DSpace on 2016-12-08T16:44:38Z (GMT). No. of bitstreams: 1 PGPV08MA071.pdf: 1225933 bytes, checksum: cac05331f9f8408e3041c9d8b75dd641 (MD5) Previous issue date: 2008-06-27
In this work were used 3 isolates of C. gloeosporioides from different apple producer places, and 245 access belong to the Active Germoplasm Banc Apple Tree Estação Experimental da EPAGRI de Caçador, Santa Catarina . Among the tested access, 185 were resistant and 32 susceptible. From those susceptible, there was not difference in the severity degree of the MFG attack. The data obtained in this study can help in the choice of resistant access, what could be used like parentage in the genetic breeding programs of the apple tree, in order to obtain the resistance to this pathogen.The genetic diversity characterization of the access by means of molecular marker studies, has been used like an important tool to maximize the maintenance work of the germoplasm banks. In this way, the objective of this study was characterize genetically the active germoplasm bank of the apple tree making use of 12 primers of SSR. The analysis of genetic diversity was realized through the DNA extraction of the 169 access, using young leafs. The extraction protocol used was the CTAB, and the estimation of the DNA concentration was done in agarose gel 0,8% coloured with bromide ethidium (0,3 μg/ml), with DNA Fago Lambda (20, 50, 100 e 200 ng/μl) like pattern. A total of 197 alleles were encountered, and the medium number of alleles by locus of SSR was 16,4. The medium heterozigosity expectation was 84%, and the revealed polimorphism by number of the alleles by locus had a high percentage (82%). The genetic similarity analysis showed two distinct groups. The gotten results had demonstrated the existence of high genetic variability in the bank of germoplasma of apple trees, salient for the raised number of alelos for I lease and high level of heterozigosidade. This knowledge will contribute with the improvement in the efficiency of the identification of combinations that will serve of base for the genetic improvement of the apple tree
No presente trabalho foram utilizados 245 genótipos pertencentes ao Banco de Germoplasma de macieira BGM, da Epagri / Estação Experimental de Caçador - EECd, SC. Para identificar os genótipos portadores de resistência à mancha foliar de glomerela - MFG, doença essa causada principalmente pelo fungo Colletotrichum gloeosporioides. Os genótipos foram inoculados com 3 isolados de C. gloeosporioides provenientes de diferentes regiões produtoras de maçã. Entre os genótipos testados, 187 (76,3%) manifestaram resistência e 58 (23,7%) manifestaram suscetibilidade à MFG. Entre as cultivares suscetíveis, não houve diferença no grau de severidade de ataque da MFG. Os resultados obtidos neste estudo têm, dentre outras, a finalidade de subsidiar os programas de melhoramento genético da macieira na seleção de parentais, objetivando a incorporação de resistência genética à MFG nas futuras novas cultivares. Para caracterizar os genótipos do Banco de Germoplasma de macieiras foram utilizados 12 primers de SSR. A análise de diversidade genética foi realizada através da extração do DNA de 169 genótipos, utilizando-se folhas jovens. O método de extração utilizado foi CTAB e a estimativa da concentração de DNA extraído foi feita em gel de agarose 0,8% com brometo de etídeo (0,3 μg/ml), tendo como padrão DNA Fago Lambda (20, 50, 100 e 200 ng/μl). Foi obtido um total de 197 alelos, sendo que o número médio de alelos por loco SSR foi de 16,4. A expectativa média de heterozigosidade foi de 84%, o polimorfismo revelado pelo número de alelos por loco teve uma percentagem alta de 82%. Pela análise de similaridade genética foram observados dois grupos. Os resultados obtidos demonstraram a existência de alta variabilidade genética no banco de germoplasma de macieiras, ressaltado pelo elevado número de alelos por loco e alto nível de heterozigosidade. Esse conhecimento contribuirá com a melhora na eficiência da identificação de combinações que servirão de base para o melhoramento genético da macieira

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DAS, SARADINDU. "APPLE FOLIAR DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK BASED APPROACH." Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19825.

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Agriculture is the process of growing crops and raising livestock and cultivating other forms of food or fiber. It has been a fundamental activity for human societies through out history, providing food and other resources necessary for survival. It also provides employment, economic growth and environmental conservation. Some farming practices, such as sustainable agriculture, can promote environmental conservation and biodiver sity. Hence, plant illness can have a substantial effect on the economy, particularly in agricultural-dependent countries. Crop yield loss, trade restrictions, increased production costs, reduced agricultural productivity, and research and development costs are some of the ways plant diseases can affect the economy. It is essential to prevent and manage plant diseases to ensure food security and maintain a healthy agricultural sector. Farmers visually inspect their crops for symptoms of diseases, such as discoloration, spots, wilting, and deformities. Farmers can also use their sense of touch and smell to detect diseases, such as the sticky or slimy feel of plant leaves infected with fungal diseases and the foul smell of rotting or decaying plant material. However, traditional methods of plant disease detection do have limitations. Visual inspection and other traditional methods may not always detect diseases at an early stage, and there is a risk of misdiagnosis. Additionally, traditional methods may not be able to detect diseases that are not visible to the naked eye. The other alternatives are the use of Artificial Intelligence (AI) which includes training computers to detect plant diseases using image recognition technology. AI can analyze thousands of images to detect subtle changes in plant health that may indicate the pres ence of diseases. Some of the regular AI techniques used for plant disease detection are Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), Random Forest (RF), Deep Belief Networks (DBNs), Transfer Learning. These AI methods are increasingly being used for plant disease detection because they offer a fast, accurate, and cost-effective way to diagnose plant diseases, which can help prevent crop losses and iv increase yields. Here in this research work, a Multi-layered CNN model is introduced which is inspired from InceptionNet. The proposed model is trained on the “Plant Pathology 2020: FGVC7 dataset” and “Plant Pathology 2021: FGVC8 dataset”. This proposed model is compared with pertained models: InceptionV3. According to the findings, the suggested model outperforms other pre-existing models in terms of accuracy or performance and it’s able to detect the disease with a low error rate.

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Тези доповідей конференцій з теми "APPLE FOLIAR DISEASE"

Dhivya Praba., R., R. Vennila., G. Rohini., S. Mithila., and K. Kavitha. "Foliar Disease Classification in Apple Trees." In 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2021. http://dx.doi.org/10.1109/icaeca52838.2021.9675529.

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Biswas, Barsha, and Rajesh Kumar Yadav. "Multilayer Convolutional Neural Network Based Approach to Detect Apple Foliar Disease." In 2023 2nd International Conference for Innovation in Technology (INOCON). IEEE, 2023. http://dx.doi.org/10.1109/inocon57975.2023.10101125.

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Haruna, Ahmed Abba, Ibrahim Ahmed Badi, L. J. Muhammad, Albaraa Abuobieda, and Abdulaziz Altamimi. "CNN-LSTM Learning Approach for Classification of Foliar Disease of Apple." In 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC). IEEE, 2023. http://dx.doi.org/10.1109/icaisc56366.2023.10085039.

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Ahmed, Md Rayhan, Adnan Ferdous Ashrafi, Raihan Uddin Ahmed, and Tanveer Ahmed. "MCFFA-Net: Multi-Contextual Feature Fusion and Attention Guided Network for Apple Foliar Disease Classification." In 2022 25th International Conference on Computer and Information Technology (ICCIT). IEEE, 2022. http://dx.doi.org/10.1109/iccit57492.2022.10055790.

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Sujatha, Kamepalli, Ketepalli Gayatri, M. Srikanth Yadav, N. Chandra Sekhara Rao, and Bandaru Srinivasa Rao. "Customized Deep CNN for Foliar Disease Prediction Based on Features Extracted from Apple Tree Leaves Images." In 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC). IEEE, 2022. http://dx.doi.org/10.1109/iihc55949.2022.10060555.

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