Innovative Technologies for Plant Disease Monitoring and Control: A Survey of Current Methods and Emerging Trends

Abstract

Food security and agricultural productivity depend on the efficient control of Plant Diseases (PD). New approaches to the Early Detection (ED) and precise PD Prediction (PDP) have been made possible by recent developments in machine learning (ML) and deep learning (DL). This paper presents a hybrid approach combining ML and DL techniques to enhance plant disease prediction. The proposed methodology integrates feature extraction using convolutional neural networks (CNNs) with traditional ML algorithms for classification and prediction. By leveraging the strengths of both paradigms, the model achieves high accuracy in identifying disease patterns from images of plant leaves and other plant parts. A comprehensive dataset of plant images, annotated with disease labels, serves as the basis for training and evaluating the proposed approach. The results demonstrate significant improvements in prediction accuracy and computational efficiency compared to conventional methods. This hybrid model not only facilitates early disease detection but also supports timely intervention, thereby mitigating the impact of plant diseases on crop yields. The integration of DL and ML techniques provides a robust framework for advancing plant disease management and can be extended to various agricultural applications