Disease prediction models in plant pathology: Leveraging ai and machine learning for early intervention

Abstract

Plant diseases significantly threaten global food security, agricultural productivity, and sustainability. Early detection and accurate prediction of plant diseases are crucial for effective management and timely intervention. Traditional methods of disease identification, including manual scouting and laboratory-based diagnostics, are often time-consuming and subject to human error. The advent of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized plant pathology by offering innovative solutions for disease prediction and management. This review explores the applications of AI and ML in disease prediction models, emphasizing their role in early intervention and decision-making processes. Techniques such as Convolutional Neural Networks (CNNs), Random Forests, and Support Vector Machines (SVMs) have shown exceptional performance in analysing images, integrating environmental data, and predicting disease outbreaks. Furthermore, advancements in deep learning enable the development of robust and scalable models. Despite challenges like data scarcity, scalability issues, and adoption barriers, AI and ML hold immense potential to transform plant disease management. Future research should focus on improving model generalizability, data quality, and user-friendly tools for farmers. This review provides valuable insights into leveraging AI-driven disease prediction models to enhance agricultural sustainability and resilience.