VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant

Joshi, Rakesh Chandra; Kaushik, Manoj; Dutta, Maitreyee; Srivastava, Ashish; Choudhary, Nandlal

doi:10.1016/j.ecoinf.2020.101197

Cited by 67 publications

(13 citation statements)

References 16 publications

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“…The latter involves the cultivation and microscopic observation of pathogens. This method has a high diagnostic accuracy rate, but it is time consuming, and the operational process is cumbersome, making it not suitable for field detection [2,3].…”

Section: Introductionmentioning

confidence: 99%

Plant Disease Recognition Model Based on Improved YOLOv5

Wu²,

et al. 2022

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To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection over Union’. These proposed methods were used to improve the target recognition effect of the network model. In the experimental phase, to verify the effectiveness of the model, sample images were randomly selected from the constructed rubber tree disease database to form training and test sets. The test results showed that the mean average precision of the improved YOLOv5 network reached 70%, which is 5.4% higher than that of the original YOLOv5 network. The precision values of this model for powdery mildew and anthracnose detection were 86.5% and 86.8%, respectively. The overall detection performance of the improved YOLOv5 network was significantly better compared with those of the original YOLOv5 and the YOLOX_nano network models. The improved model accurately identified plant diseases under natural conditions, and it provides a technical reference for the prevention and control of plant diseases.

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Section: Introductionmentioning

confidence: 99%

Plant Disease Recognition Model Based on Improved YOLOv5

Wu²,

et al. 2022

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“…On plant leaves, a hybrid analytic model [ 47 ] obtains 95.1 percent precision, while other models achieve 92.01% precision [ 48 ]. The classification of images of coffee leaves is improved by texture image analysis [ 49 ]. In addition, our approach is a fast method that has less computational complexity than other similar methods in leaf disease classification.…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring

Anari

2022

Computational Intelligence and Neuroscience

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As possible diseases develop on plant leaves, classification is constantly hampered by obstacles such as overfitting and low accuracy. To distinguish healthy products from defective ones, the agricultural industry requires precise and error-free analysis. Deep convolutional neural networks are an efficient model of autonomous feature extraction that has been shown to be fairly effective for detection and classification tasks. However, deep convolutional neural networks often require a large amount of training data, cannot be translated, and need a number of parameters to be specified and tweaked. This paper proposes a highly effective structure that can be applied to classifying multiple leaf diseases of plants and fruits during the feature extraction step. It uses a deep transfer learning model that has been modified to serve this purpose. In summary, we use model engineering (ME) to extract features. Multiple support vector machine (SVM) models are employed to enhance feature discrimination and processing speed. The kernel parameters of the radial basis function (RBF) are determined based on the selected model in the training step. PlantVillage and UCI datasets were used to analyze six leaf image sets containing healthy and diseased leaves of apple, corn, cotton, grape, pepper, and rice. The classification process resulted in approximately 90,000 images. During the experimental implementation phase, the results show the potential of a powerful model in classification operations, which will be beneficial for a variety of future leaf disease diagnostic applications for the agricultural industry.

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“…Deep transfer learning is gaining tremendous popularity in plant leaf disease identification. In [ 58 ], the authors contributed an automatic disease identification method on the Vigna Mungo plant using three CNN architectures, videlicet, VirLeafNet-1, VirLeafNet-2, and VirLeafNet-3. Experimental evaluations on a self-made data set revealed 97.40% accuracy using VirLeafNet-3.…”

Section: Comparative Analysismentioning

confidence: 99%

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

Meena

Dhaka

Sinwar

et al. 2021

Sensors

212

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In the modern era, deep learning techniques have emerged as powerful tools in image recognition. Convolutional Neural Networks, one of the deep learning tools, have attained an impressive outcome in this area. Applications such as identifying objects, faces, bones, handwritten digits, and traffic signs signify the importance of Convolutional Neural Networks in the real world. The effectiveness of Convolutional Neural Networks in image recognition motivates the researchers to extend its applications in the field of agriculture for recognition of plant species, yield management, weed detection, soil, and water management, fruit counting, diseases, and pest detection, evaluating the nutrient status of plants, and much more. The availability of voluminous research works in applying deep learning models in agriculture leads to difficulty in selecting a suitable model according to the type of dataset and experimental environment. In this manuscript, the authors present a survey of the existing literature in applying deep Convolutional Neural Networks to predict plant diseases from leaf images. This manuscript presents an exemplary comparison of the pre-processing techniques, Convolutional Neural Network models, frameworks, and optimization techniques applied to detect and classify plant diseases using leaf images as a data set. This manuscript also presents a survey of the datasets and performance metrics used to evaluate the efficacy of models. The manuscript highlights the advantages and disadvantages of different techniques and models proposed in the existing literature. This survey will ease the task of researchers working in the field of applying deep learning techniques for the identification and classification of plant leaf diseases.

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VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant

Cited by 67 publications

References 16 publications

Plant Disease Recognition Model Based on Improved YOLOv5

Plant Disease Recognition Model Based on Improved YOLOv5

A Hybrid Model for Leaf Diseases Classification Based on the Modified Deep Transfer Learning and Ensemble Approach for Agricultural AIoT-Based Monitoring

A Survey of Deep Convolutional Neural Networks Applied for Prediction of Plant Leaf Diseases

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