Automated Plant Disease Detection Using Convolutional Neural Networks: Enhancing Accuracy in Image Classification

In agriculture, early and accurate detection of plant diseases is crucial for reducing crop loss and ensuring food security.Traditional plant disease identification usually takes a long time and sometimes requires much effort with high possibilities of human errors.This paper addresses the identified challenges through the proposition of an automated approach to detecting plant diseases using the Convolutional Neural Networks-a powerful deep learning technique for carrying out image classification tasks.Accurate classification and diagnosis of a wide range of plant diseases from leaf images have been performed in the proposed model using CNNs, which therefore makes the model quite reliable for the farmers and agronomists.Considering the CNN architecture proposed, it has been designed and trained with a dataset that is highly diverse and includes images of both healthy and diseased leaves of plants.Advanced data augmentation and techniques of tuning hyperparameters have enhanced the model with high scores of classification accuracy as compared to the traditional machine learning methods.The paper further discusses the use of different CNN architectures and their respective performances in terms of accuracy, precision, recall, and F1-score.Experimental results prove that the proposed CNN-based approach increases the accuracy in plant disease classification and provides a solution to deploy real-time diseased plant detection in the field.This system would, therefore, revolutionize agricultural practice upon implementation by way of early intervention and reduction in reliance on manual identification of disease.