AI and Deep Learning for Crop and Soil Health Monitoring

Artificial Intelligence (AI) and Deep Learning (DL) are transforming agricultural monitoring by enabling precise, data-driven assessments of crop and soil health. This chapter provides an overview of the evolution of agricultural monitoring systems, tracing the shift from traditional observation-based methods to contemporary digital agriculture supported by remote sensing, Internet of Things (IoT) sensors, and advanced computational analytics. Major agricultural data sources, including satellite imagery, Unmanned Aerial Vehicles (UAVs), ground-based sensors, laboratory soil analyses, weather datasets, and farmer-generated information, are examined within the framework of AI-enabled decision systems. Key DL architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and transformer-based models, are analyzed for their applications in disease detection, nutrient stress diagnosis, irrigation optimization, crop phenotyping, and yield forecasting. This chapter also addresses challenges related to data availability, infrastructure, interpretability, and adoption, and outlines future directions that emphasize explainable AI, integrated digital platforms, and farmer-centric decision-support systems to promote sustainable and resilient agriculture.