Analyzing the Frontier of AI-Based Plant Disease Detection: Insights and Perspectives

Plant diseases (PDs) are a significant risk to agriculture all over the world and constitute a threat not only to economic security but also to nutritional safety. The utilization of artificial intelligence (AI), to be more precise, machine learning (ML) and computer vision, has recently emerged as a potentially useful technique for the early and accurate detection of a PD. The prime objective of this survey is to provide readers with an in-depth look at the cutting edge on AI-based plant disease detection (PDD). In this chapter, we explore a number of AI- and ML-based approaches that have the potential to assist with PDD. In addition, we have shed light on the potential for AI-driven solutions to be utilized in agricultural contexts, and we have identified research gaps and difficulties. This study tries to fulfill the expectation that it will be of use to other researchers, agricultural professionals, and policymakers in their search for disease-control strategies that are both more successful and more permanent. This study not only identifies the AI methods that hold the most promise for PDD but also brings to light some of the challenges and problems that remain unresolved that could lead to additional developments in the field.