Advancing Crop Health: The Role of Artificial Intelligence in Disease Management

Plant pathogens pose significant challenges to global agriculture, threatening crop yields, food security, and economic sustainability. Traditional management strategies often fall short due to the unpredictable nature of diseases and resistance to chemical treatments. The advent of artificial intelligence (AI) offers transformative solutions in this domain. This review explores the integration of AI in detecting, identifying, and managing plant pathogens with unprecedented precision. Key AI methodologies such as machine learning, deep learning, and reinforcement learning are analyzed, highlighting their role in disease diagnosis, pathogen classification, and decision support systems. The potential of precision agriculture, driven by AI tools, to optimize resource use and mitigate environmental impact is also discussed. Despite its promise, AI adoption faces challenges such as inadequate data quality, high computational requirements, and limited trust in automated systems. Emerging technologies and interdisciplinary approaches are paving the way for more effective, scalable, and sustainable plant disease management systems. This review highlights AI's transformative potential in plant pathology, offering insights into its capabilities, limitations, and the innovations required to build a resilient and sustainable agricultural future.