High throughput phenotyping of tomato spot wilt disease in peanuts using unmanned aerial systems and multispectral imaging

Abstract

The amount of visible and near infrared light reflected by plants varies depending on their health. In this study, multispectral images were acquired by a quadcopter for high throughput phenotyping of tomato spot wilt disease resistance among twenty genotypes of peanuts. The plants were visually assessed to acquire ground truth ratings of disease incidence. Multispectral images were processed into several vegetation indices. The vegetation index image of each plot has a unique distribution of pixel intensities. The percentage and number of pixels above and below varying thresholds were extracted. These features were correlated with manually acquired data to develop a model for assessing the percentage of each plot diseased. Ultimately, the best vegetation indices and pixel distribution feature for disease detection were determined and correlated with manual ratings and yield. The relative resistance of each genotype was then compared. Image-based disease ratings effectively ranked genotype resistance as early as 93 days from seeding.

Plant phenotyping relevance

マルチスペクトル画像と植生指数から、落花生の病害率という植物状態を推定する画像ベースの表現型計測モデルを開発・検証しており、手法が研究の中心である。

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