SOIL YIELD FORECASTING

This research project serves as a comprehensive meta-analysis in the field of agricultural science, specifically focusing on the prediction of crop yields. This endeavor involves collating and synthesizing findings from a variety of studies and articles that have explored different methodologies and models for forecasting agricultural outputs. The objective of this comprehensive review is to identify trends, methodologies, and key factors that consistently influence crop yield predictions across different studies.It synthesizes methodologies from various studies, emphasizing machine learning (ML) techniques like Support Vector Machines (SVM), Random Forest (RF), and Convolutional Neural Networks (CNN). These studies integrate high-resolution satellite imagery with environmental indices such as NDVI, EVI, and LAI. Soil chemical properties (pH, nutrients) and satellite-derived data were used to enhance the prediction of crop yields for diverse crops. The findings highlight the comparative effectiveness of different models in handling the spatial and temporal variability of both above-ground and below-ground data, improving prediction accuracy under varying environmental and soil conditions.Through this theoretical analysis, the research underscores the potential of advanced analytical models to transform agricultural monitoring and prediction, providing critical insights that can aid in the optimization of agricultural policies and resource management.