AI-Driven Approaches to Enhance Plant Disease Detection and Monitoring: A Focus on Machine Learning in Agriculture

Abstract

The integration of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL) techniques, has emerged as a transformative solution for enhancing plant disease detection and monitoring in agriculture. Addressing the urgent challenges of food security and sustainable farming, this paper reviews recent advancements in AI-driven approaches to accurately identify and manage plant diseases. Key methodologies, including convolutional neural networks (CNNs) and object detection models like YOLO V5, have demonstrated high accuracy in image-based disease identification, enabling automated and scalable solutions. The literature highlights ensemble methods that combine algorithms like support vector machines (SVM), random forests, and k-nearest neighbors (KNN) to improve detection across diverse environmental conditions. Additionally, remote sensing technology using drones and satellite imagery has advanced large-scale monitoring capabilities. To overcome limitations in training data, data augmentation techniques, such as GAN-generated synthetic images, enhance model robustness across various crop and disease types. This paper presents a comprehensive review of these technologies, proposes a systematic methodology combining Plant Village datasets with real-time drone surveillance, and offers insights into the potential for predictive disease analytics. The findings underscore AI’s critical role in achieving precision agriculture, supporting timely interventions, and promoting sustainable farming practices.