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

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.

Automated summary

Breeding strategies for many crops require evaluating diverse genotypes from a large breeding pool, with accurate and fast phenotyping being crucial in selecting pathogen tolerant genotypes. Analyzing leaf tissues with spectral tools offers potential gains in screening efficiency. Utilizing semi-supervised clustering techniques based on spectral patterns unique to infected plants can help rank genotype tolerances to infection effectively.