An intelligent deep learning framework for crop disease prediction using convolutional neural networks

Abstract

Early and precise identification of crop diseases plays a pivotal role in maintaining agricultural productivity, food security and sustainable agricultural systems. Conventional approaches to crop disease diagnosis involve visual inspection, which can be slow, labor-intensive, and prone to human error, particularly in fluctuating environmental conditions. This article introduces a smart deep learning model for automatic detection of crop diseases with Convolutional Neural Networks (CNNs). Our approach takes advantage of the superior image recognition and feature extraction abilities of CNN models to predict the presence of disease and health in plants based on leaf images. A comprehensive dataset of crop leaf images is used, including various disease and healthy classes. Leaf images are preprocessed and augmented to improve model generalization and resilience to lighting, pose and background clutter. The CNN model is trained with fine-tuned hyperparameters and performance is assessed using metrics like accuracy, precision, recall and F1-score. The experimental findings show that the proposed model produces high classification accuracy and is capable of detecting complex disease patterns, even in the early disease stage. The proposed system offers a scalable, affordable, and non-invasive method for monitoring diseases in the field. Moreover, it can be incorporated with mobile or Internet of Things (IoT) systems to facilitate precision farming and help farmers make informed decisions for disease control. This research is a step towards the creation of autonomous agricultural systems and the use of deep learning for improving crop health assessment and productivity.