Мақола

Multivariate analysis and machine learning prediction of Sorghum cultivar traits under nitrogen regulation

Muhammad Tanveer AltafDepartment of Field Crops, Faculty of Agriculture, Recep Tayyip Erdoğan University, Pazar, Rize, 53300, Türkiye. [email protected]Waqas LiaqatDepartment of Field Crops, Faculty of Agriculture, Recep Tayyip Erdoğan University, Pazar, Rize, 53300, Türkiye. [email protected]Mehmet BedirFaculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, 58140, TürkiyeGönül CömertpayEastern Mediterranean Agricultural Research Institute, Adana, 01370, TürkiyeSeyid Amjad AliDepartment of Information Systems and Technologies, Bilkent University, Ankara, 06800, TürkiyeAasim MuhammadFaculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, 58140, TürkiyeMuhammad Azhar NadeemDepartment of Biotechnology, Faculty of Science, Mersin University, Yenişehir, Mersin, 33343, TürkiyeFaheem Shehzad BalochDepartment of Biotechnology, Faculty of Science, Mersin University, Yenişehir, Mersin, 33343, Türkiye

BMC Plant Biologyjournal2026en

ABI

Аннотация

BACKGROUND: Genotypic differences in nitrogen use efficiency strongly influence sorghum growth and yield, highlighting the need for precise and reliable prediction of cultivar responses to nitrogen (N) availability. This study investigates the impact of two N treatments on sorghum cultivars, using artificial intelligence (AI) models for prediction. RESULTS: values ranging from 0.759 to 0.966 for RF and 0.729 to 0.980 for LightGBM, indicating strong predictive accuracy. CONCLUSION: values greater than 0.92 for key traits, such as stomatal conductance, panicle width, and grain yield, demonstrating its potential to optimize N management. Gustav performed best under high N, whereas cultivar responses to low N were genotype-specific, captured effectively by the machine learning models. These findings highlight the role of AI models in predicting cultivar performance and supporting sustainable agricultural decisions.

Ҳали таржима қилинмаган

Мавзулар

Plant nutrient uptake and metabolism Data Mining Algorithms and Applications Crop Yield and Soil Fertility

Идентификаторлар

DOI: 10.1186/s12870-026-08434-9

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