A Semi-supervised approach to cluster symptomatic and asymptomatic leaves in root lesion nematode infected walnut trees

Abstract

Breeding strategies for many crops require quantitative evaluations of many genotypes from within as large of a diverse breeding pool as possible. In selecting pathogen tolerant genotypes, accurate and fast phenotyping to investigate genetic responses to pathogen infection and reproduction is crucial. Analyzing leaf tissues with spectral tools along with ground truth data offers potentially large gains in screening efficiency. However, ground truth labels per plant may not capture the effects of asymptomatic leaves and heterogeneous canopy responses to stress. We explored a semi-supervised clustering-based technique in which spectral patterns unique to and common among leaves from nematode infected plants are distinguished from patterns with no relationship to infection; we utilize these spectral patterns in ranking genotype tolerances to infection as a secondary objective. Proximal hyperspectral leaf scans (360 nm–1700 nm) of three walnut rootstock genotypes (MS1 122, VX211, MS1 127) were used in an agglomerative clustering procedure based on spectral angle mapper (SAM) distances to choose a spectral endmember representing the root lesion nematode, Pratylenchus vulnus, stress symptom on leaf per genotype. The histogram of SAM distances between control samples and the endmember was calculated. Next, the histogram of SAM distances between infected samples and the endmember was calculated. The shift between these histograms was then found using the minimum difference of pair assignments (MDPA) measure. The MDPA measures were 4.88, 3.14, and 5.48 for MS1 122, VX211, and MS1 127, respectively. This meant a genotype ranking in the order VX211, MS1 122 and, MS1 127 from the least affected by nematode infection to most impacted, which agreed with classification by nematological examinations of the plants. Clustering leaves based on their spectral response has the potential to overcome the limitation of heterogeneous canopy responses to stress in high-throughput phenotyping and other applications.

Plant phenotyping relevance

葉の近接ハイパースペクトル測定と半教師ありクラスタリングを用いて、線虫感染ストレスの症状を抽出・分類し、遺伝子型耐性を順位付けする方法が研究の中心である。植物病害状態の表現型推定を技術的に検証している。

Code and data availability

The supplied blocks describe hyperspectral leaf scans and semi-supervised clustering analysis of walnut rootstocks, but contain no data availability statement, public dataset deposit, author code repository, or supplement reference. No paper-specific public asset is identified.

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