Integrating experimental data and machine learning models for wheat yield estimation

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dc.creatorSreenivasa Reddy, Kadapa
dc.creatorShivay, Yashbir Singh
dc.creatorPrasanna, Radha
dc.creatorKumar, Dinesh
dc.creatorPeramaiyan, Panneerselvam
dc.creatorKashyap, Suryakanta
dc.creatorMandi, Sunil
dc.creatorNayak, Somanath
dc.creatorBaral, Kirttiranjan
dc.creatorBorate, Rohit Bapurao
dc.date2026-04-07
dc.date2026-04-24T06:04:38Z
dc.date.accessioned2026-06-27T04:13:03Z
dc.descriptionWheat is a crucial staple crop for ensuring food security in India, yet improving its productivity remains essential to meet the demands of a growing population. This study aims to optimize nitrogen (N) and zinc (Zn) fertilization strategies, specifically by reducing N inputs through nano-urea foliar applications, while leveraging machine learning (ML) approaches to improve the accuracy of wheat yield prediction. A field dataset was collected during the rabi seasons of 2021–22 and 2022–23. The study assessed the integration of key yield attributes, nutrient concentrations, and nutrient uptake into MLbased prediction models. Six ML algorithms were employed: k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Gradient Boosting Machine (GBM), and an ensemble model (GBM+SVR). Model performance was assessed using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 values. Hyperparameter tuning was performed to enhance model accuracy. Among the models tested, MARS and RF delivered the best performance. The MARS model achieved the highest prediction accuracy with MAE values of 0.025 and 0.007 t ha−1 , RMSE values of 0.013 and 0.009 t ha−1 , and R2=0.99 for both years. These models also revealed the most influential factors affecting wheat yield, with ML-based models, particularly MARS and RF, demonstrating strong potential in predicting wheat yield and optimizing N and Zn fertilization. These models provide valuable insights for improving sustainable intensification and optimizing nutrient management in wheat cultivation.
dc.identifierhttps://hdl.handle.net/10568/182617
dc.identifier.urihttp://hdl.handle.net/123456789/25434
dc.languageen
dc.publisherSpringer Nature
dc.rightsLimited Access
dc.sourceSreenivasa Reddy, Kadapa, Yashbir Singh Shivay, Radha Prasanna, Dinesh Kumar, Panneerselvam Peramaiyan, Suryakanta Kashyap, Sunil Mandi, Somanath Nayak, Kirttiranjan Baral, and Rohit Bapurao Borate. "Integrating experimental data and machine learning models for wheat yield estimation." Proceedings of the Indian National Science Academy (2026): 1-18.
dc.subjectwheat
dc.subjectcrop yield
dc.subjectprecision agriculture
dc.subjectnitrogen fertilizers
dc.subjectmachine learning
dc.subjectnutrient management
dc.subjectforecasting
dc.subjectsustainable agriculture
dc.titleIntegrating experimental data and machine learning models for wheat yield estimation
dc.typeJournal Article

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Instituto Internacional de Investigación del Arroz (IRRI)