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Journal articles on the topic 'PLANT DISEASE DETECTION'

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Author: Grafiati
Published: 11 September 2023

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1

Manvi, Goutami G., Gayana K N, G. Ramya Sree, K. Divyanjali, and Dr Kirankumari Patil. "Plant Disease Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4538–42. http://dx.doi.org/10.22214/ijraset.2022.43221.

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Abstract: Plant growth is major requirement for framers, as it creates a path for their living, plants getting affected and their growth is related hand in hand. Framers strive to cultivate healthy crops; in spite of it plants getting affected are the major cause of crop failure. Plant disease is now the risk factor not only for framers but also to customers, environment and global economy. Immoderate pesticide usage is the cause for major health issues in plants. Plant disease detection using image processing can be the best way to predict and get accurate results. This project is based on deep convolutional neural networks which enhances the accuracy and training efficiency. This application will help many farmers who are uneducated to get correct information about diseases and help increase their yield. We are fostering a web application that can distinguish plant infection. The objective is to distinguish different plant infection by checking picture out. By utilizing CNN Algorithm we can identify the plant disease precisely. By the results of accuracy it shows this model is better than any traditional framing. Keywords: Plant Diseases, Deep Learning, Convolutional Neural Networks
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2

Shelar, Nishant, Suraj Shinde, Shubham Sawant, Shreyash Dhumal, and Kausar Fakir. "Plant Disease Detection Using Cnn." ITM Web of Conferences 44 (2022): 03049. http://dx.doi.org/10.1051/itmconf/20224403049.

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Plants and crops that are infected by pests have an impact on the country's agricultural production. Usually, farmers or professionals keep a close eye on the plants in order to discover and identify diseases. However, this procedure is frequently time-consuming, costly, and imprecise. Plant disease detection can be done by looking for a spot on the diseased plant's leaves. The goal of this paper is to create a Disease Recognition Model that is supported by leaf image classification. To detect plant diseases, we are utilizing image processing with a Convolution neural network (CNN). A convolutional neural network (CNN) is a form of artificial neural network that is specifically intended to process pixel input and is used in image recognition.
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3

Monigari, Vaishnavi. "Plant Leaf Disease Prediction." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1295–305. http://dx.doi.org/10.22214/ijraset.2021.36582.

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The Indian economy relies heavily on agriculture productivity. A lot is at stake when a plant is struck with a disease that causes a significant loss in production, economic losses, and a reduction in the quality and quantity of agricultural products. It is crucial to identify plant diseases in order to prevent the loss of agricultural yield and quantity. Currently, more and more attention has been paid to plant diseases detection in monitoring the large acres of crops. Monitoring the health of the plants and detecting diseases is crucial for sustainable agriculture. Plant diseases are challenging to monitor manually as it requires a great deal of work, expertise on plant diseases, and excessive processing time. Hence, this can be achieved by utilizing image processing techniques for plant disease detection. These techniques include image acquisition, image filtering, segmentation, feature extraction, and classification. Convolutional Neural Network’s(CNN) are the state of the art in image recognition and have the ability to give prompt and definitive diagnoses. We trained a deep convolutional neural network using 20639 images on 15 folders of diseased and healthy plant leaves. This project aims to develop an optimal and more accurate method for detecting diseases of plants by analysing leaf images.
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S, Dr Baskaran, Sampath P, Sarathkumar P, Sivashankar S, and Vasanth Kumar K. "Advances in Image Processing for Detection of Plant Disease." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 05, no. 02 (April 14, 2017): 08–10. http://dx.doi.org/10.9756/sijcsea/v5i2/05010140101.

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5

Halder, Monishanker, Ananya Sarkar, and Habibullah Bahar. "PLANT DISEASE DETECTION BY IMAGE PROCESSING: A LITERATURE REVIEW." SDRP Journal of Food Science & Technology 3, no. 6 (2018): 534–38. http://dx.doi.org/10.25177/jfst.3.6.6.

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6

Oo, Yin Min, and Nay Chi Htun. "Plant Leaf Disease Detection and Classification using Image Processing." International Journal of Research and Engineering 5, no. 9 (November 2018): 516–23. http://dx.doi.org/10.21276/ijre.2018.5.9.4.

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7

Rani, S. V. Jansi. "Plant Disease Detection using Transfer Learning in Precision Agriculture." AMBIENT SCIENCE 9, no. 3 (November 2022): 34–39. http://dx.doi.org/10.21276/ambi.2022.09.3.ta02.

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8

Save, Apurva, Aksham Gupta, Sarthak Pruthi, Divyanjana Nikam, and Prof Dr Shilpa Paygude. "Plant Disease Detection and Fertilizer Suggestion." International Journal for Research in Applied Science and Engineering Technology 10, no. 2 (February 28, 2022): 351–56. http://dx.doi.org/10.22214/ijraset.2022.40275.

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Abstract: Plant disease diagnosis is the foundation for efficient and precise plant disease prevention in today's complicated environment. Plant disease identification has become digitised and data-driven as smart farming has grown, allowing for advanced decision support, smart analysis, and planning. This work provides a deep learning-based mathematical model for detecting and recognising plant diseases, which improves accuracy, generality, and training efficiency. The prevention and control of plant disease have consistently been broadly talked about in light of the fact that plants are presented to the external climate and are profoundly inclined to diseases. Typically, the precise and quick diagnosis of disease assumes a significant part in controlling plant disease, since helpful protection measures are frequently carried out after right diagnosis Identification of the plant diseases is the way to prevent the misfortunes in the yield and amount of the rural item. Early Detection of Plant Leaf Disease is a significant need in a developing horticultural economy like India. Without legitimate recognizable proof of the disease, disease control measures can be an exercise in futility and cash and can prompt further plant misfortunes. Our task proposes a profound learning-based model which will be trained utilizing a dataset containing pictures of healthy and diseased crop leaves. The model will serve its target by ordering pictures of leaves into diseased classes dependent on the example of imperfection. The framework effectively recognizes various sorts of disease found in Tomato Crop. Index Terms: Convolutional Neural Networks (CNN), Deep Learning, Pretrained models, Inceptionv3, Xceptionv3, MobilenetV2.
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Sethi, Mohit. "Plant Disease Detection using Image Segmentation." International Journal of Ayurveda and Herbal Research (IJAHR) 1, no. 1 (2023): 15–18. http://dx.doi.org/10.54060/ijahr.v1i1.3.

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This paper presents a novel approach for detecting plant diseases using image segmentation techniques. The proposed method employs deep learning algorithms to segment images into healthy and infected areas, and then classifies the disease based on the segmented region. The use of image segmentation allows for the automated detection and quantification of diseases in plants, making it a valuable tool for farmers and researchers. Experimental results show that the proposed method achieves high accuracy in detecting various plant diseases, including leaf spot, powdery mildew, and rust. The method's performance was evaluated on a dataset of plant images, demonstrating its effectiveness in real-world applications. The proposed approach has the potential to revolutionize the way plant diseases are detected and managed, improving crop yields and reducing losses due to disease outbreaks.
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Verma, Shivam, Prashant Kumar Choudhary, Suraj Kumar, and Prof Dr Reena Gunjan. "Plant Disease Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1009–13. http://dx.doi.org/10.22214/ijraset.2022.43700.

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Abstract: Crop diseases are a major threat to food security, but their rapid identification remains difficult inmany parts of the world due to the lack of thenecessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased andhealthy plant leaves collected under controlled conditions, we train a deep convolutional neuralnetwork to identify 14 crop species and 26 diseases (or absence thereof). The trained model achieves anaccuracy of 99.35% on a held-out test set, demonstrating the feasibility of this approach. Overall, the approach of training deep learning models on increasingly large and publicly availableimage datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a massive global scale
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11

Fawaiq, Mohammad Nur, Ema Utami, and Dhani Ariatmanto. "Rice Plant Disease Detection with Data Augmentation Using Transfer Learning." International Journal of Research Publication and Reviews 4, no. 4 (April 8, 2023): 2195–99. http://dx.doi.org/10.55248/gengpi.2023.4.4.35530.

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Kulkarni, Ms Pooja. "Rice Plant Disease Detection." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (June 30, 2020): 237–41. http://dx.doi.org/10.22214/ijraset.2020.6033.

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13

Pore, Prof Yogita, Suraj Teli, Swaraj Ghuge, and Nikhil Patil. "Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 1767–70. http://dx.doi.org/10.22214/ijraset.2023.51405.

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Abstract: Early disease identification is crucial for productive crop production in agriculture. illnesses such as bacterial spot, late blight, Septoria leaf spot, and yellow curved leaf the quality of the tomato harvest. Automatic classification techniques of plant diseases also assist in taking action once they are discovered diseased leaf symptoms Presented below is a Convolutional Learning Vector Quantization and Neural Network (CNN) model Method for detecting tomato leaf disease based on the (LVQ) algorithm and categorization. There are 500 tomato photos in the dataset. leaves that display four disease symptoms. We created a model of CNN for feature extraction and categorization automatically. Color Research on plant leaf diseases actively uses information. In our model, three channels based on RGB are subjected to filters.
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14

C, Berin Jones, and Murugamani C. "Plant Disease Detection System based IoT for Agricultural Applications Using Cloud." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 738–50. http://dx.doi.org/10.5373/jardcs/v11/20192628.

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15

Premnath, Akshara, and Mrs V. Manoranjithem. "Plant Disease Detection and Solution System." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 2072–75. http://dx.doi.org/10.22214/ijraset.2023.49888.

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Abstract: For preventing losses in the yield and quantity of the cultural product, Classification is performed, if proper analysis is not taken in this approach or classification, then it produces serious effects on plants and due to ich respective product quality or productivity is affected. Disease classification on the plants is very critical for supportable agriculture. It is very difficult to monitor or treat plant diseases manually. It requires a huge amount of work and also needs excessive processing time, therefore image processing for the detection of plant diseases. Automatic fruit disease detection and grading is a creative strategy.Bacterial Blight, Fruit Spot, Fruit Rot, and Virus Diseases on Fruits are the modules identified in this investigation.For the identification of diseases and their critical processes, including as photosynthesis, transpiration, pollination, fertilisation, germination, and some fruit diseases, molecular methods and profiling of volatile organic chemicals in plants are applied.The classification of plant diseases includes the processes of loading the image, pre-processing, segmenting, feature extraction, and SVM classifier.
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16

Saji, Alby. "Green Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 5360–64. http://dx.doi.org/10.22214/ijraset.2023.52825.

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Abstract: Because it feeds humanity, creates jobs, and directly supports national economic progress, agriculture is the backbone of the country. Identification of plant diseases is very crucial in agriculture. The increasing use of pesticides and sprays nowadays has led to a wide range of diseases affecting plants. Early disease detection would help farmers save more harvests if the infections could be stopped. Plants can be saved if rotting spots are discovered early. Automatic plant disease detection not only saves time but also provides greater accuracy. Plant production is decreased by improper disease detection. Here, we use image processing techniques to identify a few common plant illnesses. First, we take the image of the plant anduse image processing to identify it. This project is being implemented using Python.
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17

Khan, Adiba, and Atul Srivastava. "PlantDoc-Plant Disease Detection using AI." Journal of Informatics Electrical and Electronics Engineering (JIEEE) 4, no. 1 (2023): 1–10. http://dx.doi.org/10.54060/jieee.v4i1.86.

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Gardening is a hobby which requires dedication and consistency. It is something more than just watering a plant. Taking care of Garden plants is very important as most of the plants are prone to diseases frequently. Plant Diseases ruin the plant and ultimately may kill it with time so timely identification and treatment of the disease is required for a healthy plant. This also helps to preserve many threatened species of plants. PlantDoc uses Artificial Intelligence model created on Convolution Neural Network algorithm of Deep Learning to solve this problem. The model is trained with images of different plant leaves to identify defected plants. PlantDoc helps in disease detection. It uses computer vision concept of AI to find the disease of plant and provide solution for that automatically. PlantDoc uses MERN stack. PlantDoc web application successfully helps to identify plant diseases of various plants by analyzing plant leaf image and suggests cure to treat it. This helps in treatment of plants timely which helps to stop the further spread of dis-ease and provides cure.
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18

., Shishira. "Plant Disease Detection Using Leaf Images." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 15, 2021): 600–602. http://dx.doi.org/10.22214/ijraset.2021.37429.

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Identification of the plant diseases is that the key to prevent the losses within the yield and quantity of the agricultural product. The studies of the plant diseases mean the studies of visually observable patterns seen on the plant. Health monitoring and disease detection on plant is incredibly critical for sustainable agriculture. It’s very difficult to watch the plant diseases manually. It requires tremendous amount of labor, expertise within the plant diseases, and also require the excessive quantity. Hence, image processing is used for the detection of plant diseases by capturing the pictures of the leaves and comparing it with the data sets. The data sets comprise of different plant within the image format. Except detection users are directed to an e-commerce website where different pesticides with its rate and usage directions are displayed. This website is efficiently used for comparing the MRP’s of varied pesticides and buy the desired one for the detected disease. This paper aims to support and help the green house farmers in an efficient way.
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Sundari, S. Sivakama, Dr Sampath AK, and Dr M. Islabudeen. "Plant Disease Detection using CNN Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 2618–20. http://dx.doi.org/10.22214/ijraset.2023.51267.

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Abstract: Plants are the meals supply of the earth. Plant infections and illnesses are consequently a first-rate threat, however the maximum not un-usual place prognosis is basically to study flora for the presence or absence of visible symptoms. The agricultural production of the country gets affected majorly due to pests as they affect the plants and crops. The detection and identification of disease is been observed by farmers and experts through their naked eyes. Based on the leaf image classification, an approach of plant disease recognition model is being developed with the help of deep convolutional networks. Early detection of diseases to which plants are exposed is very important, especially in a country like India with a large population. The diseases caused by bacteria, virus and fungus results on lowering the crop yield in a huge aspect. The loss can be prevented by predicting the plant disease at the earliest. With the help of Deep Learning concepts, the performance and accuracy of disease detection can be improved. It uses image processing concepts for noise reduction, ML and DL concepts i.e., CNN for Problem Solving. This project captures plants and leaf disease and helps farmers to identify and detect the solutions for the problem that is being infected.
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Joshi, Bhoopendra, Abhinav Kumar, Satyam Kashyap, Nooruddin Nagdi, Sukhdarshan Vinayak, and Dinesh Verma. "Smart Plant Disease Detection System." International Journal of Electrical, Electronics and Computers 6, no. 4 (2021): 13–16. http://dx.doi.org/10.22161/eec.64.4.

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21

Tugrul, Bulent, Elhoucine Elfatimi, and Recep Eryigit. "Convolutional Neural Networks in Detection of Plant Leaf Diseases: A Review." Agriculture 12, no. 8 (August 10, 2022): 1192. http://dx.doi.org/10.3390/agriculture12081192.

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Rapid improvements in deep learning (DL) techniques have made it possible to detect and recognize objects from images. DL approaches have recently entered various agricultural and farming applications after being successfully employed in various fields. Automatic identification of plant diseases can help farmers manage their crops more effectively, resulting in higher yields. Detecting plant disease in crops using images is an intrinsically difficult task. In addition to their detection, individual species identification is necessary for applying tailored control methods. A survey of research initiatives that use convolutional neural networks (CNN), a type of DL, to address various plant disease detection concerns was undertaken in the current publication. In this work, we have reviewed 100 of the most relevant CNN articles on detecting various plant leaf diseases over the last five years. In addition, we identified and summarized several problems and solutions corresponding to the CNN used in plant leaf disease detection. Moreover, Deep convolutional neural networks (DCNN) trained on image data were the most effective method for detecting early disease detection. We expressed the benefits and drawbacks of utilizing CNN in agriculture, and we discussed the direction of future developments in plant disease detection.
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Srivastava, Prakanshu, Kritika Mishra, Vibhav Awasthi, Vivek Kumar Sahu, and Pawan Kumar Pal. "PLANT DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK." International Journal of Advanced Research 9, no. 01 (January 31, 2021): 691–98. http://dx.doi.org/10.21474/ijar01/12346.

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When plants and crops are suffering from pests it affects the agricultural production of the country. Usually, farmers or experts observe the plants with eye for detection and identification of disease. But this method is often time processing, expensive and inaccurate. Automatic detection using image processing techniques provide fast and accurate results. This paper cares with a replacement approach to the development of disease recognition model, supported leaf image classification, by the utilization of deep convolutional networks. Advances in computer vision present a chance to expand and enhance the practice of precise plant protection and extend the market of computer vision applications within the field of precision agriculture. a completely unique way of training and therefore the methodology used facilitate a fast and straightforward system implementation in practice. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images to make a database, assessed by agricultural experts, a deep learning framework to perform the deep CNN training. This method paper may be a new approach in detecting plant diseases using the deep convolutional neural network trained and finetuned to suit accurately to the database of a plants leaves that was gathered independently for diverse plant diseases. The advance and novelty of the developed model dwell its simplicity healthy leaves and background images are in line with other classes, enabling the model to distinguish between diseased leaves and healthy ones or from the environment by using CNN. Plants are the source of food on earth. Infections and diseases in plants are therefore a big threat, while the foremost common diagnosis is primarily performed by examining the plant body for the presence of visual symptoms [1]. As an alternative to the traditionally time-consuming process, different research works plan to find feasible approaches towards protecting plants. In recent years, growth in technology has engendered several alternatives to traditional arduous methods [2]. Deep learning techniques are very successful in image classification problems.
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Mandal, Surajit. "Survey on Plant Disease Detection using Deep Learning based Frameworks." International Journal of Medical, Pharmacy and Drug Research 7, no. 2 (2023): 27–36. http://dx.doi.org/10.22161/ijmpd.7.2.2.

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Early identification of plant diseases is crucial as they can hinder the growth of their respective species. Although many machine learning models have been utilised for detecting and classifying plant diseases. The advent of deep Learning, a subset of machine learning, has revolutionised this field by offering greater accuracy. Therefore, deep learning has the potential to greatly enhance the accuracy of plant disease detection and classification. Recent research progress on the use of deep learning technology in the identification of crop leaf diseases is reviewed in this article. The current trends and challenges in plant leaf disease detection using advanced imaging techniques and deep learning are presented. This survey aims to provide a valuable resource for the researchers investigating the detection of plant diseases and detection of those using state of the art models for ease of saving time and cost. Additionally, the article also addresses some of the current challenges and issues in the detection process that need to be resolved.
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24

Kumar, Kushal, Khushboo Tripathi, and Rashmi Gupta. "Plant Disease Detection from Image Using CNN." International Journal of Innovative Research in Computer Science and Technology 11, no. 4 (July 7, 2023): 24–27. http://dx.doi.org/10.55524/ijircst.2023.11.4.5.

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The increasing threat of plant diseases poses a significant challenge to global food security. Rapid and accurate identification of plant diseases is crucial for effective disease management and prevention. In recent years, deep learning techniques have shown great promise in automating the process of plant disease identification through image analysis. This report presents a comprehensive study on image-based plant disease classification using deep learning techniques. The report begins by providing an overview of plant diseases and their impact on agriculture. It discusses the limitations of traditional disease identification methods and highlights the potential of deep learning algorithms in revolutionizing the field. The importance of image-based approaches is emphasized due to their non-destructive and scalable nature. Next, the report delves into the methodology of deep learning for plant disease classification. It explores various architectures such as convolutional neural networks (CNNs) and their variants, including transfer learning and ensemble methods. The training process, data augmentation techniques, and hyperparameter tuning are discussed in detail.
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Nihar, Fatema, Nazmun Nahar Khanom, Syed Sahariar Hassan, and Amit Kumar Das. "Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms." Journal of Engineering Advancements 2, no. 01 (March 12, 2021): 48–57. http://dx.doi.org/10.38032/jea.2021.01.007.

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In the era of artificial systems, disease detection is becoming easier. For detecting disease, monitoring the plants 24 hours, visiting the agricultural office, or asking for help from a specialist seem difficult. This situation demands a user-friendly plant disease detection system, which allows people to detect whether the plant is diseased or not in an easier way. If the plant is diseased, a treatment plan will also be notified. In this way, people can easily save time, money, and, most importantly, plants. In this study, the researchers have collected data of vegetables from a field and applied multiple diversified Neural Network Algorithms such as CNN, MCNN, FRCNN, and, along with that, also proposed a new modified neural network architecture (ModCNN), which has produced 97.69% accuracy. The authors have also classified the bean leaf diseases into four categories according to their symptoms, which will help to identify diseases accurately.
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Alpyssov, Akan, Nurgul Uzakkyzy, Ayazbaev Talgatbek, Raushan Moldasheva, Gulmira Bekmagambetova, Mnyaura Yessekeyeva, Dossym Kenzhaliev, Assel Yerzhan, and Ailanysh Tolstoy. "Assessment of plant disease detection by deep learning." Eastern-European Journal of Enterprise Technologies 1, no. 2 (121) (February 28, 2023): 41–48. http://dx.doi.org/10.15587/1729-4061.2023.274483.

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Plant disease and pest detection machines were originally used in agriculture and have, to some extent, replaced traditional visual identification. Plant diseases and pests are important determinants of plant productivity and quality. Plant diseases and pests can be identified using digital image processing. According to the difference in the structure of the network, this study presents research on the detection of plant diseases and pests based on three aspects of the classification network, detection network, and segmentation network in recent years, and summarizes the advantages and disadvantages of each method. A common data set is introduced and the results of existing studies are compared. This study discusses possible problems in the practical application of plant disease and pest detection based on deep learning. Conventional image processing algorithms or manual descriptive design and classifiers are often used for traditional computer vision-based plant disease and pest detection. This method usually uses various characteristics of plant diseases and pests to create an image layout and selects a useful light source and shooting angle to produce evenly lit images. The purpose of this work is to identify a group of pests and diseases of domestic and garden plants using a mobile application and display the final result on the screen of a mobile device. In this work, data from 38 different classes were used, including diseased and healthy leaf images of 13 plants from plantVillage. In the experiment, Inception v3 tends to consistently improve accuracy with an increasing number of epochs with no sign of overfitting and performance degradation. Keras with Theano backend used to teach architectures
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Bande, Priyanka, and Mr Kranti Dewangan. "Plant Disease Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 858–65. http://dx.doi.org/10.22214/ijraset.2022.43900.

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Abstract: Farming is critical to the nation's economy and progress. Precision Farming (PA), on the other hand, is still in its development when it comes to technology-driven growth. Various plant diseases have caused pain to untold millions of people around the world over the years, with an estimated annual yield loss of 14% globally. Computerized disease segmentation and diagnosis from based on leaf photos has the potential to be more effective than the current method. Image capture, preprocessing, and segmentation are followed by augmentation, feature extraction, and classification using models for automatic plant disease diagnosis. This project employs VGG-16, ResNet-50, AlexNet, DenseNet-169, and InceptionV3 Deep Learning models to identify plant illnesses from photos in the Plant Village Dataset and reliably classify them into two classes. The results of the experiment revealed that the ResNet-50 has achieved highest accuracy of 97.80 % as compare to other applied deep learning models for disease classification. Keywords: VGG16, ResNet50, Inception V3, CNN, GoogleNet, AlexNet
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Gupta, Sahil, Vivek Pandey, Pravesh Pandey, Mukul Verma, and Hasib Shaikh. "Leaf Disease Detection System." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1603–12. http://dx.doi.org/10.22214/ijraset.2023.50439.

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Abstract: Agricultural productivity is something on which economy highly depends. This is the one of the reasons that disease detection in plants plays an important role in agriculture field, as having disease in plants are quite natural. If proper care is not taken in this area then it causes serious effects on plants and due to which respective product quality, quantity or productivity is affected. For instance a disease named little leaf disease is a hazardous disease found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself it detects the symptoms of diseases i.e. when they appear on plant leaves. This paper introduces an efficient approach to identify healthy and diseased or an infected leaf using image processing and machine learning techniques. Various diseases damage the chlorophyll of leaves and affect with brown or black marks on the leaf area. These can be detected using image prepossessing, image segmentation. Support Vector Machine (SVM) is one of the machine learning algorithms is used for classification. The Convolutional Neural Network (CNN) resulted in a improved accuracy of recognition compared to the SVM approach.
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Joshi, Kalpesh, Rohan Awale, Sara Ahmad, Sanmit Patil, and Vipul Pisal. "Plant Leaf Disease Detection Using Computer Vision Techniques and Machine Learning." ITM Web of Conferences 44 (2022): 03002. http://dx.doi.org/10.1051/itmconf/20224403002.

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Agriculture production is extremely important in today’s economy because disease development in plants is relatively common, early detection of disease in plants is critical in the agriculture field. The automatic finding of such early-stage disease detection is helpful as it decreases a great effort of supervising in large farmhouses of yields. Using digital image processing and machine learning algorithms, this paper presents a method for detecting plant disease. The disease detection is done on the yields’ various leaves. The presented system for plant disease detection is simple and computationally efficient which requires less time for prediction than other deep learning-based approaches. The accuracies for the various plant and leaf diseases are calculated and presented in this paper.
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Sushma, Prof Ksn, Nishant Upadhyay, Ajeet Singh, Prasenjeet Kr Singh, and Tanzeelah Firdaus. "Plant Disease Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1099–101. http://dx.doi.org/10.22214/ijraset.2022.41451.

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Abstract: Early diagnosis of plant diseases is critical since they have a substantial impact on the growth of their unique species. Many Machine Learning (ML) models have been used to detect and categorize plant diseases, but recent breakthroughs in a subset of ML called Deep Learning (DL) look to hold a lot of promise in terms of improved accuracy. A variety of developed/modified DL architectures, as well as several visualization techniques, are utilized to recognize and identify the symptoms of plant ailments. In addition, a number of performance measurements are used to evaluate various architectures/techniques. This article explains how to use DL models to display a variety of plant diseases. Furthermore, several research gaps are identified, allowing for improved efficiency in detecting plant illnesses even before issues emerge. Keywords: Plant disease; deep learning; convolutional neural networks (CNN), Google Net Architecture, Tensorflow, and PyTorch are some of the tools that can be used;
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31

Barthare, Nanda. "Different Plant Disease Detection and Pest Detection Techniques Using Image Processing." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1486–92. http://dx.doi.org/10.22214/ijraset.2022.40003.

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Abstract: In India, agriculture is a significant industry. Our Indian economy is also heavily reliant on agriculture; given that agriculture employs near about 70% of the population, it is critical to boost crop/plant productivity. Farmers have struggled to achieve higher productivity and better market prices due to many sorts of crop diseases. As a result, early detection of plant diseases becomes an essential strategy for avoiding losses in an agricultural production system. The disease's symptoms are mostly noticeable on leaves. It's difficult to keep track of each plant manually across a large area. As a result, image processing techniques are used to observe and diagnose plant diseases, which may be a better option for detecting diseases fast and accurately. This paper provides the review of different plant disease and pest control techniques using image processing in the recent years. Keywords: Image Processing, Agricultural, Disease Techniques, Pest Control.
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32

H.C, Ravikumar, Dimpal Raj V, Sai Shreyas G H, Karthik S, and Keerthana S. "Detection of Diseased Plant Leaf Using Deep Learning." Journal of Signal Processing 9, no. 1 (February 17, 2023): 43–47. http://dx.doi.org/10.46610/josp.2023.v09i01.005.

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The detection of illness in crops is one of the laborious and crucial duties in agricultural activities. It requires a lot of time and requires specialized labor. Detection of plant disease by use of the automatic technique is beneficial and it requires a huge amount of work, monitoring in the large farm of crops, and at an early stage itself to detect symptoms of diseases affected means where they appear on the plant leaves. A more recent, cutting-edge method for processing images that produce exact outcomes is deep learning. Leaf detection of disease and categorization employ a variety of deep learning and image processing approaches. For disease detection, image processing methods including image pre-processing, segment, feature extraction, etc. are employed along with deep learning methods like CNN, Fast RCNN, Faster RCNN, and Mask RCNN. Furthermore, deep learning technologies offer greater precision than image processing technology. Application areas for the detection of plant leaf diseases include biological research and agricultural institutes, among many others. The economy heavily depends on agricultural output. In this study, an effective method for crop disease identification by the use of machine learning and image processing techniques is proposed. This proposed approach has a 75% accuracy rate for detecting 20 distinct illnesses in 4 popular types of plants. We are using integrated techniques to improve the accuracy above 75% through this model.
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Dong, Jiuqing, Alvaro Fuentes, Sook Yoon, Taehyun Kim, and Dong Sun Park. "Towards Improved Performance on Plant Disease Recognition with Symptoms Specific Annotation." Korean Institute of Smart Media 11, no. 4 (May 31, 2022): 38–45. http://dx.doi.org/10.30693/smj.2022.11.4.38.

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Object detection models have become the current tool of choice for plant disease detection in precision agriculture. Most existing research improves the performance by ameliorating networks and optimizing the loss function. However, the data-centric part of a whole project also needs more investigation. In this paper, we proposed a systematic strategy with three different annotation methods for plant disease detection: local, semi-global, and global label. Experimental results on our paprika disease dataset show that a single class annotation with semi-global boxes may improve accuracy. In addition, we also studied the noise factor during the labeling process. An ablation study shows that annotation noise within 10% is acceptable for keeping good performance. Overall, this data-centric numerical analysis helps us to understand the significance of annotation methods, which provides practitioners a way to obtain higher performance and reduce annotation costs on plant disease detection tasks. Our work encourages researchers to pay more attention to label quality and the essential issues of labeling methods.
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Bhattania, Yugam, Prashant Singhal, and Tanish Agarwal. "Plant Leaf Disease Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 2518–1523. http://dx.doi.org/10.22214/ijraset.2022.42892.

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Abstract: Approximately 58% of Indian population is involved in agriculture directly or indirectly, which contributed about 19.9% to the GDP of India in 2020-2021 F.Y. According to a report published by ICAR (Indian Council of Agricultural Research) about 30-35% of annual crop yield are wasted because of pests and diseases which affects the income and livelihood of the farmers. With the advancement in deep learning and computer vision it is now possible to detect the plant disease effectively by observing the disease pattern of leaves of plants. Which will help farmers to classify the disease in their plant. In this study about 12500 images of healthy and infected plant leaves which are available in public domain were used to train deep learning model, which can classify the respected disease. Keywords: Crop disease, Deep learning, Convolutional neural networks
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35

Rama Rao, A., Anji Reddy V, and G. V. Narasimha Raju. "Plant Disease Detection using Machine Learning Algorithms." YMER Digital 21, no. 02 (February 17, 2022): 425–30. http://dx.doi.org/10.37896/ymer21.02/42.

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One of the most exiting research areas in agriculture is the identification of plant diseases from images. Today, the term 'machine learning' is a hot topic. It is possible to apply the principles of machine learning to a wide range of fields. plant disease detection can also benefit from machine learning and deep learning. For the detection and classification of plant diseases, this paper uses several types of plant leaves as a model.
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36

Kurzadkar, Shailesh, Achal Meshram, Aman Barve, Kajal Dhargave, Mruganayani Alone, and Vijaya Bhongale. "Plant Leaves Disease Detection System Using Machine Learning." International Journal of Computer Science and Mobile Computing 11, no. 2 (February 28, 2022): 27–30. http://dx.doi.org/10.47760/ijcsmc.2022.v11i02.004.

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Plants and crops are one of the major human needs in day to day life. And detection of plant disease is also essential through some automatic disease detection technique. It will reduce a lot of work of monitoring crops in big farms and at early stage disease gets detected through the symptoms on leaves. The earlier disease detection may lead to longer survival of crops and plants. Data sets will be created for identifying diseased and healthy plants. The created datasets of diseased and healthy leaves will be trained to classify both types of images collectively. Overall using machine learning to train the large data sets available publicly gives us a clear way to detect the disease present in plants in a colossal scale.
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37

B, Kowshik, Savitha V, Nimosh madhav M, Karpagam G, and Sangeetha K. "Plant Disease Detection Using Deep Learning." International Research Journal on Advanced Science Hub 3, Special Issue ICARD 3S (March 20, 2021): 30–33. http://dx.doi.org/10.47392/irjash.2021.057.

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38

Roy, Riya. "Plant Leaf Disease Detection using SVM." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (May 31, 2021): 1066–74. http://dx.doi.org/10.22214/ijraset.2021.34077.

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Yousuf, Aamir, and Ufaq Khan. "Ensemble Classifier for Plant Disease Detection." International Journal of Computer Science and Mobile Computing 10, no. 1 (January 18, 2021): 14–22. http://dx.doi.org/10.47760/ijcsmc.2021.v10i01.003.

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40

Malvankar, Shubham. "Plant Disease Detection using Image Processing." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 2685–90. http://dx.doi.org/10.22214/ijraset.2020.5450.

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41

Flood, Julie. "Plant Pathogen Detection and Disease Diagnosis." Phytochemistry 62, no. 5 (March 2003): 813. http://dx.doi.org/10.1016/s0031-9422(02)00609-x.

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42

Fox, Roland. "Plant Pathogen Detection and Disease Diagnosis." Plant Pathology 47, no. 5 (October 1998): 681–82. http://dx.doi.org/10.1046/j.1365-3059.1998.00296.x.

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43

Rafay, Syed Abdul. "Leaf Disease Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 2086–89. http://dx.doi.org/10.22214/ijraset.2022.45667.

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Abstract: Diseases in plants cause major production and economic losses as well as reduction in both quality and quantity of agricultural products. Now a days, plant diseases detection has received increasing attention in monitoring large field of crops. Farmers experience great difficulties in switching from one disease control policy to another. The naked eye observation of experts is the traditional approach adopted in practice for detection and identification of plant diseases. In this project, we study the need of simple plant leaves disease detection system that would facilitate advancements in agriculture. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies. This technique will improves productivity of crops.
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Suhaman, Jali, Tia Sari, Kamandanu Kamandanu, Dwy Aulianti, Muhammad Adhi, and Utih Amartiwi. "Smart Plant: A Mobile Application for Plant Disease Detection." GMPI Conference Series 2 (January 31, 2023): 52–57. http://dx.doi.org/10.53889/gmpics.v2.173.

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Indonesia is one of the big producers of agricultural products in the world. Agriculture sector plays an important role in the national economic development structure. However, the proportion of young farmers (ages 20 to 30 years old) is only 8% of the farmer population (BPS, 2019). Majority proportion comes from old people with age interval from 50 to 60 years old. (Taufik Leoni, 2020). Based on our case study in Purwokerto, the problem that is often found by old age farmers is the reduced ability to see and recognize plant diseases. Furthermore, they also face the difficulty to follow the development of agricultural science so that some of their knowledge is outdated. That encourages us to make a mobile application to identify plant disease and connect them with scientists. Since the majority of farmers in Purwokerto plant tomatoes, we limit this research for tomato disease only. After studying some related previous research, we found most of them used a deep structure of Convolutional Neural Network (CNN) to reach a high accuracy. However, since our aim is to make daily use technology for old people, a high complexity model does not fit for this case. Therefore, we proposed our own CNN model with less complexity but got 89% accuracy. For future works, we plan to develop it for the other plants and hope it will help all farmers to do quality control, especially for the old age farmers.
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Hasika S, Kaviya Priya R, and Dr.B.Shridar. "Advanced Plant Disease Detection Using Neural Network." international journal of engineering technology and management sciences 7, no. 3 (2023): 304–10. http://dx.doi.org/10.46647/ijetms.2023.v07i03.040.

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The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of tomato plant disease recognition model, based on leaf image classification, by the use of deep convolutionalnetworks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize different types of tomato plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the project, starting from gathering images in order to create a database, assessed by agricultural experts. Neural network, was used to perform the disease detection. The experimental results on the developed model achieved detection between 85% and 95%.
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Saxena, Niharika, and Neha Sharma. "TOMATO LEAF DISEASE PREDICTION USING TRANSFER LEARNING." International Journal of Engineering Technologies and Management Research 9, no. 6 (June 21, 2022): 1–14. http://dx.doi.org/10.29121/ijetmr.v9.i6.2022.1177.

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Tomatoes are the most extensively planted vegetable crop in India's agricultural lands. Although the tropical environment is favorable for its growth, specific climatic conditions and other variables influence tomato plant growth. In addition to these environmental circumstances and natural disasters, plant disease is a severe agricultural production issue that results in economic loss. Therefore, early illness detection can provide better outcomes than current detection algorithms. As a result, deep learning approaches based on computer vision might be used to detect diseases early. This study thoroughly examines the disease categorization and detection strategies used to identify tomato leaf diseases. The pros and limitations of the approaches provided are also discussed in this study. Finally, employing hybrid deep-learning architecture, this research provides an early disease detection approach for detecting tomato leaf disease.
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47

Singh, Shivangi. "Leaf Disease Detection." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3324–29. http://dx.doi.org/10.22214/ijraset.2021.36836.

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Agriculture is a key source of livelihood. Agriculture provides employment opportunities for village people on a large scale in developing countries like India. India's agriculture consists of the many crops and consistent with survey nearly 70% population is depends on agriculture. Most of Indian farmers are adopting manual cultivation thanks to lagging of technical knowledge. Farmers are unaware of what quite crops that grows well on their land. When plants are suffering from heterogeneous diseases through their leaves which will effect on the production of agriculture and profitable loss, also reduction in both quality and quantity of agricultural production. Leaves are important for fast growing of plant and to extend production of crops. Identifying diseases in plant leaves is challenging for farmers and also for researchers. Currently farmers are spraying pesticides to the plants but it affects humans directly or indirectly by health or also economically. To detect these plant diseases many fast techniques got to be adopt. In this paper, we have done surveys on different leaf diseases and various advanced techniques to detect these diseases. As said by Mahatma Gandhi, "Agriculture is the backbone of the Indian Economy". Hence the detection of leaf diseases is an important aspect in increasing the yield of a crop. By detecting the leaf disease farmer can increase the crop yield which leads in growth of country’s economy.
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48

Uppal, Ashima, Mahaveer Singh Naruka, and Gaurav Tewari. "Image Processing based Plant Disease Detection and Classification." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 1s (January 16, 2023): 52–56. http://dx.doi.org/10.17762/ijritcc.v11i1s.5993.

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Generally, it has been observed that due to lack of proper knowledge of disease intensity, the farmer is not able to use the pesticide in proper quantity to treat the diseases. The use of pesticide mostly becomes more than necessary, due to which there is not only a loss of money, but also it causes soil and environmental pollution. If diseases severity-wise labelled data sets are available, it can be used to develop pesticide recommendation systems. Images with least infection severity can be used to train and validate a DL model to capture the plant diseases at very initial stage. Classification techniques may be viewed as variations of detection systems; however, instead of attempting to identify only one particular illness among several diseases, classification methods detect and name the diseases harming the plant. This presents various classification and plant disease detection methods based on image processing with results.
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Sivasangari, A., M. Sai Kishore, M. Poornesh, R. M. Gomathi, and D. Deepa. "Plant Disease Detection and Classification Using Image Processing and Neural Networks." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 4920–24. http://dx.doi.org/10.1166/jctn.2020.9189.

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Plant disease is a major problem for food security, but in many parts of the world their rapid prediction remains difficult because of lack of the infrastructure required. New advances in machine vision achieved via deep learning have paved the way for diagnosis of AI-assisted diseases. To help determine the extent of plant disease, provide agricultural specialists with a digital archive with photos of diseased leaves. Estimate depends on Standard Area Diagrams (SADs), a collection of diseased leaf images, each of which includes an incrementally more diseased leaf compared to the previous one. Every SAD shows seriousness of the disease in terms of the percentage of the diseased leaf. Users then turn to the field for a leaf. For eg, equate it to SADs and use it to measure the severity of the disease. “This app is useful for crop consultants and research scientists looking to cut costs and improve the time and accuracy for assessing disease severity in plants.”
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S, Swathi. "Plant Disease Detection Using Convolutional Neural Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 3396–99. http://dx.doi.org/10.22214/ijraset.2023.54303.

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Abstract: Agriculture must complete a huge effort that involves finding plant diseases. This is something that the economy is extremely dependent on. Due to the prevalence of plant illnesses, finding infections inplants is a crucial task in the agriculture industry. It takes constant examination of the plants to spot infections in the leaves. To put it simply, monitoring the plants requires some form of planned method. The detection of damaged leaves is facilitated by program-based disease identification, which also saves time and human effort. Compared to current methods, the suggested algorithm distinguishes between plant diseases and accurately classifies them. Constant plant monitoring is labor-intensive and expensive to do by humans and it is time- consuming too
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