Overview

Plant leaf diseases can cause severe damage to crops, leading to yield loss and economic impact. In recent years, deep learning techniques have been increasingly used in plant disease diagnosis due to their high accuracy and efficiency. This study by J. Arunpandian focuses on using a Convolutional Neural Network (CNN) for plant leaf disease classification. The CNN is a type of deep neural network that has been successful in many computer vision tasks. The CNN is trained on a large dataset of labeled images of healthy and diseased plant leaves. During training, the CNN learns to extract important features from the images and to use these features to classify the images into different classes. Various techniques such as feature extraction, feature selection, and data augmentation are employed to improve the performance of the CNN. Transfer learning, which involves using a pre-trained model and fine-tuning it on the dataset, is also used to improve the accuracy of the model. The performance of the CNN is evaluated using various metrics such as accuracy, precision, recall, and F1-score. The confusion matrix is also used to evaluate the performance of the model. The study also highlights the importance of optimizing hyperparameters such as learning rate, batch size, and regularization to improve the performance of the model. Overfitting and underfitting are common problems in deep learning, and techniques such as dropout and regularization are used to address these issues. The proposed CNN-based approach shows promising results in plant leaf disease classification, which can help in early detection and control of plant diseases. The study can be useful in precision agriculture, crop management, and crop yield prediction.

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