Advancing Agriculture through Artificial Intelligence, Plant Disease Detection Methods, Applications, and Limitations

Abstract

In recent years, the integration of artificial intelligence (AI) into agriculture has transformed traditional farming practices. One area of significant advancement is in the detection of plant diseases, where AI-driven technologies offer innovative solutions to mitigate crop losses and enhance agricultural productivity. This paper explores the latest methodologies, applications, and challenges in utilizing AI for plant disease detection. We review various AI techniques, including machine learning, computer vision, and deep learning, that have been deployed to accurately identify and diagnose plant diseases. Additionally, we discuss the practical applications of these technologies in real-world agricultural settings, highlighting their potential to revolutionize crop management practices. Despite the promising developments, we also address the limitations and obstacles faced in implementing AI-based plant disease detection systems, including issues related to data quality, model generalization, and scalability. By critically examining the current landscape of AI-driven plant disease detection, this paper aims to provide insights for researchers, practitioners, and policymakers to further advance the integration of AI technologies in agriculture.