AI-Driven Crop Yield Prediction and Disease Detection in Agroecosystems

Abstract

Agricultural sustainability is crucial for ensuring global food security. However, traditional farming methods are often inefficient and susceptible to crop diseases, impacting yield and resource utilization. The chapter discusses the potential of Artificial Intelligence (AI) in agriculture, particularly in crop yield prediction and disease detection. AI models can analyze vast agricultural data, such as weather patterns, soil conditions, and historical yield information, to predict crop yields with improved accuracy. AI can also detect crop diseases early, allowing for timely interventions and minimizing yield losses. Integrating plant science enhances these models, providing deeper insights into plant health and disease progression. It emphasizes the role of IoT in collecting real-time data and creating a connected ecosystem. Real-world case studies demonstrate the effectiveness of AI-powered approaches. The chapter calls for collaboration among farmers, researchers, technology developers, and business leaders to implement AI-driven crop yield prediction and disease detection.