AI-Driven Crop Disease Prediction and Management System: Enhancing Agricultural Sustainability Through Intelligent Analysis

Abstract

Agriculture, the pillar of global food security, is increasingly under threat from crop diseases, resulting in tremendous economic and productivity losses. Conventional disease detection mechanisms are generally slow, labor- intensive, and lacking the precision necessary for timely intervention. This paper discusses the revolutionary potential of AI-based crop disease prediction and management systems, with a focus on their implementation in contemporary agricultural practices. Leveraging deep learning architectures like MobileNetV2, the systems allow accurate image-based disease detection, complemented by environmental factors like temperature, humidity, and weather to improve predictive accuracy. Innovations like real-time alerts, multilingual interfaces, and adaptive treatment advice for crops like cotton, sugarcane, and soybean are discussed. We also cover existing architectures, data-driven approaches, and deployment issues to estimate scalability across various agricultural ecosystems. The paper further discusses integrating a subscription-based agronomist service and an AI-based e-marketplace to improve accessibility and sustainability. By bridging technological advancements with pragmatic agricultural applications, this study highlights the potential of AI to improve crop resilience and empower farmers with smart, data-driven disease management solutions.