Hyperspectral Imaging and Machine Learning for Huanglongbing Detection on Leaf-Symptoms.

Abstract

Huanglongbing is one of the most destructive diseases of citrus worldwide. Infected trees die due to the absence of practical cures. Thus, the removal of HLB-infected trees is one of the principal HLB managements for the regulation of disease spread. Here, we propose a non-destructive HLB detection method based on hyperspectral leaf reflectance. In total, 72 hyperspectral leaf images were collected in an HLB-invaded citrus orchard in Thailand and each image was visually distinguished into either any HLB symptom appearance (symptomatic) or no symptoms (asymptomatic) on the leaf. Principal component analysis was applied on the hyperspectral data and revealed 16 key wavelengths at red-edge to near-infrared regions (715, 718, 721, 724, 727, 730, 733, 736, 930, 933, 936, 939, 942, 945, 957, and 997 nm) that were characteristically differentiated in the symptomatic group. Seven models learnt on the spectral data at these 16 wavelengths were examined for the potential to separate these two image groups: random forest, decision tree, support vector machine, k-nearest neighbor, gradient boosting, logistic regression, linear discriminant. F1-score was employed to select the best-fit model to distinguish the two categories: random forest achieved the best score of 99.8%, followed by decision tree and k-nearest neighbor. The reliability of the visual grouping was evaluated by nearest neighbor matching and permutation test. These three models separated the two image categories as precisely as PCR results, indicating their potential as alternative tool instead of PCR.

Plant phenotyping relevance

葉のハイパースペクトル画像からHLB症状を非破壊的に検出・分類する手法を提案し、複数の機械学習モデルを比較・評価しているため、植物病徴の取得・推定が研究の中心です。

Code and data availability

The paper's hyperspectral leaf images, spectral pixel dataset, and custom Python preprocessing/analysis software are not publicly deposited; the Data Availability Statement states they are available only on request from the corresponding author, and no public repository or URL is provided.

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