A novel mechanistic model for diagnosing the general health status of kimchi cabbage using the discrete-pixel data

Abstract

Traditionally, destructive analysis of the internal chemical components of plants is necessary to assess their overall health. This study proposes a novel approach to non-destructively estimate and classify organic and inorganic components associated with the general health of kimchi cabbage by integrating spectral imaging and system dynamic modeling techniques. Existing vegetation indices rely on constant values, which limits their ability to classify plants with similar measured constant values ​​but different internal component contents. This problem is addressed by systematically approaching the vegetation indices and extracting and using intrinsic steady-state value and response-velocity parameters. It uses the principle that healthy plant pixel data have low-pass filter characteristics, and reflectance data from stressed or damaged plant areas exhibit high-pass filter characteristics. The proposed dynamic model identifies the relationship between red and near-infrared wavelength reflectances as time series data, and this framework can transform constant-based vegetation indices into dynamic system-based models. The mechanistic model improved accuracy by 33 % for Lutein, 10.5 % for beta-carotene, 8 % for Chlorophyll-a, and 15.8 % for Chlorophyll-b compared to the existing method. In addition, differences in calcium content between treatment groups, which are difficult to resolve using traditional vegetation indices, were identified. The dynamic model provides a solution for simultaneous, non-destructive analysis of pigments and calcium components, which traditionally relied on destructive testing. This scalable and efficient technology has great potential to bridge the gap between precision agriculture and conventional agriculture and contribute to sustainable agricultural realization.

Plant phenotyping relevance

スペクトルイメージングと動的モデルを用いて、植物の健康状態や色素・カルシウム含量を非破壊推定する手法の開発が中心であり、植物フェノタイピング手法に該当する。

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