IEEE Access · 2023 · 113 citations · 30 references
ComputingComputer SciencePlant PathologyComputer VisionMachine LearningMachine VisionAgricultural Machine VisionCrop ClassificationDeep LearningRepresentation LearningClassification AccuracyClassification Accuracy AnalysisClassification Accuracy AssessmentDeep Learning DetectionDeep Learning-based ClassificationDeep Learning-based DetectionGenerative Adversarial NetworksMachine Learning Crop Classification
The advancement of Deep Learning and Computer Vision in the field of agriculture has been found to be an effective tool in detecting harmful plant diseases. Classification and detection of healthy and diseased crops play a very crucial role in determining the rate and quality of production. Thus the present work highlights a well-proposed novel method of detecting Tomato leaf diseases using Deep Neural Networks to strengthen agro-based industries. The present novel framework is utilized with a combination of classical Machine Learning model Principal Component Analysis (PCA) and a customized Deep Neural Network which has been named as PCA DeepNet. The hybridized framework also consists of Generative Adversarial Network (GAN) for obtaining a good mixture of datasets. The detection is carried out using the Faster Region-Based Convolutional Neural Network (F-RCNN). The overall work generated a classification accuracy of 99.60% with an average precision of 98.55%; giving a promising Intersection over Union (IOU) score of 0.95 in detection. Thus the presented work outperforms any other reported state-of-the-art.
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Plant Disease Detection and Classification by Deep Learning—A Review
Shujuan Zhang, Bin Wang · IEEE Access · 2021 · 951 citations · Full text