Machine Vision and Intelligent Systems in Horticultural Production

The lack of knowledge on the integration of advanced technologies for precise monitoring and management in horticultural production has led to inefficiencies in plant breeding, disease detection, and growth monitoring. Traditional methods often prove inadequate in addressing the complex challenges of modern agriculture. Based on the foregoing, it is considered that incorporating machine vision, spectral imaging, phenotyping, deep learning, and machine learning into intelligent systems can significantly enhance decision-making processes. These technologies collectively provide detailed insights into plant health, nutrient levels, genetic traits, and growth patterns, enabling predictive analytics for disease outbreaks and optimized growth conditions. The objective of this Special Issue is to explore how these intelligent systems can revolutionize horticultural practices by offering data-driven solutions that improve sustainability, efficiency, and productivity in plant cultivation.

Dr. Xiu Jin
Dr. Long Zhao
Dr. Danyan Chen
Guest Editors

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