AI-Driven Crop Disease Detection and Prediction Using Convolutional Neural Networks (CNNS) for Sustainable Agriculture

Abstract

Crop diseases are a major challenge to agricultural productivity in which the end result is normally very little yield and loss of money. Conventional disease identification techniques, such as hand inspections and pesticide spraying, are slowly becoming ineffective, particularly in large-scale agriculture. The given paper introduces an AI-powered solution based on Convolutional Neural Networks (CNNs) to detect and predict agricultural diseases at the initial stage, which will help enhance the sustainability of agriculture. The new model is innovative because it will work on the principle of identifying diseases at an early-stage using the capabilities of CNNs to process high-resolution images of crops and predict the spread of the disease successfully. The methodology will involve gathering images of the publicly available crop disease datasets, then the data will go through the data preprocessing strategies that include image augmentation, image normalization, and image labeling to make the data ready to train the model. The CNN model is trained to classify the images into healthy and diseased and also gives a prediction on whether the disease will spread in the future on the basis of the current state of the image. Accuracy, precision, recall, and F1-score are some of the key metrics that are used to evaluate the performance of the model. The findings indicate that performance is high and the model obtains 95% accuracy, 92% precision, and 90% recall, which is much better compared to the traditional methods. Being an AI-based system, this solution is efficient, reliable, and eco-friendly to manage crop diseases, allowing farmers to take timely measures and minimize the use of pesticides. The paper closes by pointing out the possibility of real-world implementation with respect to large-scale agriculture and outlining the need to conduct additional research to enhance the model's generalization to various crops and environmental conditions. Also, the incorporation of real-time information may improve the predictive ability of the model.