AI-Powered Aerial Multispectral Imaging for Forage Crop Maturity Assessment: A Case Study in Northern Kazakhstan
Baidalin M. Rakhimzhanova T. Akhet A. Baidalina S. Myrzakhanov A. Bogapov I. Salikova Z. Varol H.A.
December 2025Multidisciplinary Digital Publishing Institute (MDPI)
Agronomy
2025#15Issue 12
Forage crops play a vital role in ensuring livestock productivity and food security in Northern Kazakhstan, a region characterized by highly variable weather conditions. However, traditional methods for assessing crop maturity remain time-consuming and labor-intensive, underscoring the need for automated monitoring solutions. Recent advances in remote sensing and artificial intelligence (AI) offer new opportunities to address this challenge. In this study, unmanned aerial vehicle (UAV)-based multispectral imaging was used to monitor the development of forage crops—pea, sudangrass, common vetch, oat—and their mixtures under field conditions in Northern Kazakhstan. A multispectral dataset consisting of five spectral bands was collected and processed to generate vegetation indices. Using a ResNet-based neural network model, the study achieved a high predictive accuracy ((Formula presented.) = 0.985) for estimating the continuous maturity index. The trained model was further integrated into a web-based platform to enable real-time visualization and analysis, providing a practical tool for automated crop maturity assessment and long-term agricultural monitoring.
artificial intelligence , crop maturity assessment , deep learning , forage crop mixtures , precision agriculture , UAV multispectral imaging , vegetation indices
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Department of Agriculture and Bioresources, Shokan Ualikhanov Kokshetau University, 76 Abay Str., Kokshetau, 020000, Kazakhstan
Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana, 010000, Kazakhstan
Department of Agriculture and Bioresources
Institute of Smart Systems and Artificial Intelligence
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