AI in Agriculture: Reviewing Deep Learning Techniques for Price Prediction and Cultivation Planning

Abstract Artificial Intelligence (AI), particularly deep learning, is transforming the agricultural sector by offering novel approaches for crop price prediction and cultivation optimization. This paper presents a comprehensive review of deep learning techniques applied in agricultural scenarios, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and hybrid architectures. The review focuses on the integration of multi-source agricultural data—such as market prices, meteorological data, soil parameters, and satellite imagery—to build predictive and prescriptive models. It highlights real world applications, technical challenges, and future directions, emphasizing the potential of AI to enhance sustainable and data-driven farming.