Machine learning parameter optimization for water stress detection in Khasi mandarin orange using an electronic nose
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Abstract
Water stress is a critical factor affecting crop yield and quality in agriculture. Timely detection and management of water stress can significantly enhance crop productivity and resource efficiency. This study employs a custom-designed E-nose system to distinguish between four stages of water stress in Khasi mandarin orange plants. The research underscores the significant potential of machine learning in addressing water stress within agricultural practices, emphasizing the crucial role of integrating advanced technologies like E-nose and machine learning to tackle agricultural challenges. The optimized machine learning parameters demonstrate improved performance, achieving 99% accuracy in detecting water stress, enabling timely and precise interventions for potential crop damage mitigation and water usage optimization.
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