Abstract
Efficient management of water resources is essential for sustaining the global food supply amidst growing populations and climate change. Traditional irrigation methods are often plagued by inefficiencies, leading to significant water wastage. This paper presents the development and validation of an autonomous drone-based irrigation system that leverages advanced image processing and machine learning techniques to optimize water usage in agriculture. The system employs standard low-cost cameras to capture high-resolution aerial images, which are processed to accurately predict the water needs of the plants and inform irrigation decisions in real-time also it can do autonomous watering by controlling the electrical water valve in the specified irrigation areas. Comprehensive field tests conducted on pepper crops demonstrate the system's ability to enhance water use efficiency and improve crop yields. By integrating state-of-the-art technologies such as TensorFlow techniques for machine lear-nig, image analysis and autonomous navigation capabilities, the proposed solution represents a significant advancement in precision agriculture. The results indicate that the autonomous drone-based irrigation system can substantially reduce water consumption while maintaining or enhancing crop productivity, thereby promoting sustainable agricultural practices.
Published Version
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