Crop Health Monitoring Using Convolutional Neural Network (CNN)

Abstract

Crop diseases threaten global food security, requiring rapid and accurate detection to maintain agricultural productivity. Traditional methods for monitoring crop health are costly and inefficient. This research explores the use of convolutional neural network (CNN), a type of deep learning algorithm, to automate plant disease detection and classification through image analysis. Leveraging publicly available datasets of plant leaf images, a robust model is developed and trained to recognize different stages of crop diseases. The CNN model is improved using techniques such as data augmentation and evaluated through metrics such as precision, accuracy, recall, and F1 score. The results demonstrate the potential of CNN to improve the accuracy and effectiveness of disease detection, contributing to sustainable agriculture and improved crop management practices. Limitations and future work include expanding the dataset, improving real-time data processing, using CNN fast Fourier transform, increasing the used layers for training, and generalizing the model to a wider range of crops and diseases