An Overview of the Application of Machine Learning and Deep Learning Techniques for Agricultural Crop Yield Prediction in Terms of Methods, Data Inputs and Prospects

Correct and timely prediction of crop yields is fundamental to global food security, agricultural policy planning and the equitable management of natural resources in the phase of a rapidly changing climate. The mounting complexity of agro-environmental systems focused by soil variability, extreme weather conditions and environmental interactions has reduced dependency on traditional statistical and process based models. Over the past decade, machine learning (ML) and deep learning (DL) techniques have emerged as transformative alternatives, capable of capturing nonlinear, high-dimensional relationships across heterogeneous data sources. The data sources include remote sensing imagery, meteorological records, soil surveys and crop management accounts. This study examines the application of ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Artificial Neural Networks (ANN) along with major DL techniques, including Convolutional Neural Networks (CNN), Long Short Term Memory networks (LSTM), hybrid CNN-LSTM models and emerging transformer based models. Key input features, such as the Normalized Difference Vegetation Index (NDVI), climatic variables, soil parameters and multi-source remote sensing data are evaluated for their influence on predictive performance. Comparisons across different crops including wheat, rice, maize and soybean reveal that ensemble and hybrid DL models consistently provide superior accuracy, with R² values commonly exceeding 0.85 in many investigations. Critical challenges including data scarcity, model interpretability deficits, geographic transferability limitations and computational demands are addressed in detail. Whereas, the role of Explainable Artificial Intelligence (XAI), transfer learning and multimodal data fusion is considered as a borderline to these limitations.