Application of Non-Contact Detection and Artificial Intelligence Techniques to Estimate Quality and Longevity of Horticultural Plants

Dear Colleagues,

Rapid and accurate horticultural product quality assessment is becoming increasingly important as e-commerce continues to expand globally. Recently, non-contact detection (NCD) techniques, including hyperspectral photography, thermography, and NIR spectrometry, have become widely applied for assessing crop quality attributes. Artificial intelligence (AI) techniques, including deep learning based on algorithms, have also been employed to develop assessment methods because they can predict specific factors based on images obtained using NCD methods. Converging interdisciplinary research involving plant science and AI techniques is becoming increasingly important as it provides creative solutions to develop rapid and accurate tools that can classify horticultural products for quality assurance systems. This Special Issue of Plants deals with the application of NCD methods and AI models to assess as well as predict external and internal quality factors in horticultural plants.

Dr. Byung-Chun In
Guest Editor

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