Enhancing plant pathology discovery and application development through automated, high-throughput hyperspectral imaging

Abstract

Hyperspectral sensing has emerged in recent years as a powerful tool for discovery in plant pathology. However, bottlenecks in reflectance data collection, including throughput, data handling, and volume, hinder the speed at which this technology can transition from niche to widespread use, and its downstream applications from proof-of-concept to proof-of-practice. We developed an automated high-throughput hyperspectral imaging (AHHI) platform to address this challenge. Our system includes a push broom hyperspectral camera (MSV500, Middleton Spectral Vision; 400-1000 nm, 8nm spatial and 0.65 nm spectral resolution) and a sample positioning automatic system inherited from “Blackbird,” a microscopic imaging robotic platform. The system acquires line images at 100 frames per sec, equivalent to about 40 sec per 10-mm leaf disc sample (4 hours per 351 samples). The output of the system is 2.5 GB *.raw and *.hdr files, which are processed by an in-house Python script. Our system can collect 9 TB of high-quality hyperspectral images in a single day without external variation in imagery. We demonstrate three use cases for scaling established hyperspectral reflectance applications to the imaging scale via the AHHI: pre-symptomatic disease detection, fungicide detection, and grapevine breeding family lineage discrimination. PERMANOVA and Random Forest analysis all yielded significant p-values and AUC ranging from 77.8 to 99.9%, mirroring prior accuracies from handheld spectrometers when accounting for a loss of spectral resolution in going from VSWIR to VNIR sensing. Access to large amounts of high-resolution hyperspectral data with systems such as AHHI can ease and accelerate translation of plant pathology discoveries from niche to mainstream use.

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