Factors Influencing Agricultural Yield, Quality, and Profitability: A Data-Driven Analysis of Major Crops in Maharashtra
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Abstract
This research provides an integrated climate-soil-crop model using big data in relation to agricultural yield, crop
quality, and crop profit from Maharashtra. A composite Total Yield Index was created to evaluate the productivity
of different crops across the region. Machine Learning (ML) classifiers, including Linear Regression, Random
Forest models, and XGBoost classifiers, were used in conjunction with statistical analysis to identify the main
variables affecting crop yield. Results show that temperature and nitrogen have a high degree of effect on crop
yield while rainfall does not have much of an effect. Random Forest provided the best overall prediction accuracy,
demonstrating a higher level of accuracy in capturing the non-linear relationships associated with the dataset.
Utilizing Explainable AI (using Shapley values) was critical in providing interpretability for the ML methods. A
data-driven integrated model or systemic approach provides improved decision-making capabilities and insight
into sustainable approaches to operate in agriculture.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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