AI-Enhanced Disease Identification and Yield Optimization in Seasonal Agriculture: A Comparative Analysis

The agricultural industry is essential for global food security, economic stability, and employment, especially in developing countries. Crop diseases represent a substantial risk to productivity, with yearly yield reductions of 20-40%. Traditional illness detection techniques, dependent on eye examination, are frequently sluggish, labour-intensive, and susceptible to mistakes. This research examines the progress in agricultural disease identification, contrasting conventional methods with contemporary technology such as machine learning (ML), deep learning (DL), and remote sensing. Deep learning models, especially Convolutional Neural Networks (CNNs), have exhibited exceptional efficacy in disease detection, attaining accuracy rates of 90% in crops such as tomato, potato, and rice. The amalgamation of hyperspectral imaging, drone surveillance, and AI-powered instruments facilitates early illness identification, continuous monitoring, and scalable solutions. The transition to digital agriculture not only optimizes disease management but also increases crop output, facilitating sustainable agricultural techniques to address the rising global food demands.