Enhancing Crop Prediction Using Artificial Intelligence for Smart Agriculture

Abstract: The food security situation all over the world has never faced such problems like the population growth, limited use of agricultural resources and climate variability. In response to these challenges, another technology has surfaced—Artificial Intelligence (AI)—which offers potential to tackle the problems by helping to create accurate systems to predict crops based on data.But there is another technology that has come to terms to address these challenges: Artificial Intelligence (AI). The current state of the art on AI application-based crop prediction in smart agriculture is summarized and presented through this review paper by collating information from peer-reviewed publications published from 2015 to 2024. In the context of predicting crop yield, disease incidence, water needs and optimal sowing windows, we analyze the use of machine learning (ML) algorithms, deep learning architectures such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks and Transformer-based models. Theoretically, we developed a uniform taxonomy for the classification of different AI approaches to crop prediction and studied the model understandable versus model interpretability trade-offs. Real-world challenges, including the scarcity of data in smallholder environments and limitations on computing resources, are discussed in detail, along with the important role of extension services providing actionable guidance to farmers based on these AI technologies. In the case study carried out in the agro-rich region of Punjab, India, the hybrid CNN-LSTM models demonstrate accuracy levels of up to 93.2% for wheat and rice yield prediction using multi-modal data, which combines satellite imagery, IoT soil sensor data, meteorological data and government agronomic database data. The paper ends with a comprehensive research roadmap that includes federated learning techniques for data sharing without compromising privacy, explainable AI (XAI) for fostering farmer trust, edge computing for deployment in remote areas, and pipelines for retraining AI systems in the face of climate change. Overall, the study reveals the significant potential of AI in the agricultural sector to transform farming processes and the societal issues and enablers that need to be addressed to achieve equitable adoption.

Keywords: Artificial Intelligence, Crop Prediction,Smart agriculture, Machine learning, Precision farming , Agriculture data analysis.