Volume 8 • Issue 2 • PP: 28–33 • 2026
A Hybrid Computational Intelligence Framework Integrating XGBoost and Residual Neural Networks for Multi-Crop Yield Prediction
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Reliable crop-yield prediction is essential for strategic food planning, input allocation, and precision-agriculture analytics, yet a single predictive model must accommodate substantial heterogeneity across crop species, regions, seasons, and management conditions. This paper proposes a hybrid computational-intelligence framework that couples extreme gradient boosting with an ensemble of residual neural networks in order to model both dominant nonlinear structure and systematic residual error. A leakage-controlled benchmark was constructed from a recent public agricultural data release, yielding 2,698 observations spanning eight major crops, 30 Indian states and union territories, six agricultural seasons, and ten complete years from 2010 to 2019. The predictor space combines climatic and agronomic descriptors, including annual rainfall, cultivated area, fertilizer and pesticide intensities, crop identity, state, season, temporal trend, and rainfall–management interaction terms; the production variable was removed to prevent algebraic target leakage. The empirical study compares the proposed framework with nine competitive baselines under a strict chronological design in which 2010–2017 are used for training, 2018 for model selection, and 2019 for independent testing. On the holdout year, the proposed hybrid attains R2 = 0.974, RMSE = 3.146 t ha−1, and MAE = 1.080 t ha−1, outperforming standalone XGBoost by 20.3% in RMSE and a direct deep neural network by 32.5%. Bootstrap analysis places the hybrid RMSE within 2.042–4.112 t ha−1 at the 95% confidence level. Grouped permutation analysis shows that crop identity, geographic location, and cultivated area contribute most strongly to predictive performance. Overall, the results support residual error correction as an effective strategy for strengthening tabular yield prediction while also underscoring the importance of leakage control, temporal validation, and competitive baseline design.
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