Enhancing Rice Yield Prediction Using UAV-Based Multispectral Imaging and Machine Learning Algorithms

Abstract

This study investigates the integration of Unmanned Aerial Vehicle (UAV) technology into rice yield prediction to address the limitations of conventional methods that rely on time-consuming and labor-intensive manual field assessments. UAV-captured multispectral imagery was utilized to generate vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), providing accurate and timely indicators of crop health, growth stages, and productivity. Collected data underwent systematic preprocessing and analysis to estimate yield outputs, ensuring precision through the use of established statistical evaluation metrics. The developed system was assessed in accordance with ISO/IEC 25010 software quality standards and ISO/IEC 30141:2018 hardware architecture guidelines, receiving high scores in functional suitability, maintainability, and interoperability. Validation through consultations with farmers and agricultural technology experts confirmed its potential to improve decision-making processes, particularly in irrigation scheduling, pest and disease management, and harvest planning. The findings demonstrate that UAV-based monitoring systems offer a practical, data-driven approach to optimizing rice production. By enabling timely interventions and efficient resource allocation, the study underscores the role of UAV technology as a valuable tool in advancing sustainable and precision agriculture practices.