A single-parameter sufficiency principle for machine learning-based agricultural quality prediction
Abstract
Accurate pre-processing prediction of sugar yield from sugarcane is critical for reducing economic losses in India’s jaggery industry, where inadequate quality assessment results in estimated annual losses of approximately INR (Indian Rupee) 1000 crores nationally. Existing prediction frameworks depend on complex multi-parameter monitoring systems that are prohibitively expensive for small-scale producers. This study introduces the Single-Parameter Sufficiency Principle (SPSP) and provides empirical evidence that a single biochemical parameter— Brix value —can be predictively sufficient for accurate sugar yield prediction under the studied experimental conditions. Using 2001 sugarcane samples from 18 geographic locations within the Erode region of Tamil Nadu, India, a Voting Ensemble model comprising XGBoost, Extra Trees, and Gradient Boosting—trained solely on Brix—achieved R2 = 0.9622 , RMSE = 2.67 kg/ton, and MAE = 1.21 kg/ton, demonstrating performance comparable to the published eleven-parameter baseline ( R2 = 0.9503 ) despite 90.9% parameter reduction. The sufficiency principle is supported through four converging lines of evidence: mathematical validation of parameter redundancy ( r = − 1.000 between Brix and water content, confirming zero independent information contribution); algorithm-robust performance (eight of nine algorithms, including all tree-based and ensemble models, exceeded the baseline, while the linear ElasticNet model ( R2 = 0.9489 ) fell slightly below); within-region spatial generalization via leave-one-location-out cross-validation (mean R2 = 0.9517 , SD = 0.0264 , across 18 locations); and practical equivalence supported through paired cross-validation comparison with the eleven-parameter baseline (paired t -test: t(9) = − 0.115 , p = 0.911 ; TOST equivalence test: p = 0.0013 , Δ R2 = ± 0.01 )—collectively demonstrating that substantial parameter reduction preserves prediction accuracy. Robustness was further confirmed through repeated holdout validation (50 independent random splits; mean R2 = 0.9543, SD = 0.0099) and calibration analysis (slope = 1.008, MACE = 0.37 kg/ton). The system operates on a standard handheld digital refractometer (INR 8000–25,000), delivers procurement decisions within 60 s, achieves 80–85% cost reduction relative to laboratory methods, and demonstrates annual savings of INR 2.7–7.7 lakhs per jaggery unit with a payback period of 2–3 weeks.
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BibTeX
@article{narayanasamy2026singleparameter,
title = {{A single-parameter sufficiency principle for machine learning-based agricultural quality prediction}},
author = {Kathirvel Narayanasamy and Ilayaraja Venkatachalam},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-73530-7},
url = {https://www.nature.com/articles/s41598-026-73530-7},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A single-parameter sufficiency principle for machine learning-based agricultural quality prediction
AU - Kathirvel Narayanasamy
AU - Ilayaraja Venkatachalam
PY - 2026
DA - 2026/10/05
JO - Scientific Reports
DO - 10.1038/s41598-026-73530-7
UR - https://www.nature.com/articles/s41598-026-73530-7
AB - Accurate pre-processing prediction of sugar yield from sugarcane is critical for reducing economic losses in India’s jaggery industry, where inadequate quality assessment results in estimated annual losses of approximately INR (Indian Rupee) 1000 crores nationally. Existing prediction frameworks depend on complex multi-parameter monitoring systems that are prohibitively expensive for small-scale producers. This study introduces the Single-Parameter Sufficiency Principle (SPSP) and provides empirical evidence that a single biochemical parameter— Brix value —can be predictively sufficient for accurate sugar yield prediction under the studied experimental conditions. Using 2001 sugarcane samples from 18 geographic locations within the Erode region of Tamil Nadu, India, a Voting Ensemble model comprising XGBoost, Extra Trees, and Gradient Boosting—trained solely on Brix—achieved R^2 = 0.9622 , RMSE = 2.67 kg/ton, and MAE = 1.21 kg/ton, demonstrating performance comparable to the published eleven-parameter baseline ( R^2 = 0.9503 ) despite 90.9% parameter reduction. The sufficiency principle is supported through four converging lines of evidence: mathematical validation of parameter redundancy ( r = − 1.000 between Brix and water content, confirming zero independent information contribution); algorithm-robust performance (eight of nine algorithms, including all tree-based and ensemble models, exceeded the baseline, while the linear ElasticNet model ( R^2 = 0.9489 ) fell slightly below); within-region spatial generalization via leave-one-location-out cross-validation (mean R^2 = 0.9517 , SD = 0.0264 , across 18 locations); and practical equivalence supported through paired cross-validation comparison with the eleven-parameter baseline (paired t -test: t(9) = − 0.115 , p = 0.911 ; TOST equivalence test: p = 0.0013 , Δ R^2 = ± 0.01 )—collectively demonstrating that substantial parameter reduction preserves prediction accuracy. Robustness was further confirmed through repeated holdout validation (50 independent random splits; mean R^2 = 0.9543, SD = 0.0099) and calibration analysis (slope = 1.008, MACE = 0.37 kg/ton). The system operates on a standard handheld digital refractometer (INR 8000–25,000), delivers procurement decisions within 60 s, achieves 80–85% cost reduction relative to laboratory methods, and demonstrates annual savings of INR 2.7–7.7 lakhs per jaggery unit with a payback period of 2–3 weeks.
SN - 2045-2322
ER -