A Machine Learning–Driven Framework for HighPrecision Crop Yield Prediction and Agricultural Decision Support

Prediction of the actual crop yield has
become one of the core problems of precision agriculture
because of the interaction of environmental and soil
factors alongside management factors. This paper
involves a machine learning-focused analytical
framework as a systematic review of 50 high-quality
research articles out of an original sample of 567 studies.
Some of the main predictive factors incorporated in the
proposed approach include the temperature, rainfall,
soil features, and previous yield. Ensemble learning
techniques and Artificial Neural Networks are listed as
the best mechanisms of capturing nonlinear relationship
and interaction of features. Also, the models such as
Random Forest, Gradient Boosting, and Support Vector
machines are relatively compared based on their
robustness and generalization. The research has added a
hybrid predictive approach to the existing body of
knowledge which improves scalability and accuracy
besides hypothesis issues of data heterogeneity and
interpretability in real agricultural systems.