Detection of citrus Huanglongbing (HLB) based on the HLB-induced leaf starch accumulation using a home-made computer vision system

Abstract

Citrus Huanglongbing (HLB) is a highly destructive and transmissible citrus disease spread by insects. As it has no effective treatment, there is an urgent need for a rapid HLB detection method that can be applied in orchards to remove the diseased trees. In this research, the contribution of two symptoms, based on HLB-induced starch accumulation, were evaluated to HLB detection using a home-made computer vision system with two imaging modes. The reflection imaging mode was used to detect blotchy mottles symptoms. A 660 nm light source was used and the absorption of different wavelengths by chlorophyll and lutein was measured. The transmission imaging was designed to detect abnormal starch accumulation within the leaf. An image was taken with 590 nm polarised light penetrating the leaf, then the angle of linear polarisation (AoLP) was calculated by the Stokes vector. The AoLP reflects the ability of the internal leaf components to rotate polarised light. The polarisation angle reflects the starch content of the leaves and determines their disease status. Multi-layer perceptron (MLP), random forest (RF), and logistic regression (LR) classifiers were then evaluated. The RF classifier performed better in the reflection experiment, with a classification accuracy of 96.67%. In the transmission experiment, the LR classifier had 88.33% recognition rate. The results show that HLB-induced starch accumulation contributes to the visual detection of HLB. This research supports the high accuracy and portability of HLB detection equipment and it has great practical importance for preventing and controlling of HLB disease.

Plant phenotyping relevance

柑橘葉の症状・デンプン蓄積を画像および偏光計測で取得し、HLB病状態を分類するコンピュータビジョン手法と装置を開発・評価しており、植物表現型取得が研究の中心である。

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