Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

Abstract

The negative impact of conventional farming on environment and human health make improvements on farming management mandatory. Imaging techniques are implemented in remote sensing for monitoring crop fields and plant phenotyping programs. The increasingly large size and complexity of the data obtained by these techniques, makes the implementation of powerful mathematical tools necessary in order to identify informative parameters and to apply them in precision agriculture. Multicolor fluorescence imaging is a useful approach for the study of plant defense responses to stress factors at bench scale. However, it has not been fully applied to plant phenotyping. This work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft rot, caused by Dickeya dadantii. Several statistical models -based on logistic regression analysis (LRA) and artificial neural networks (ANN)- were obtained for the experimental system zucchini-D. dadantii, which classify new samples as “healthy” or “infected”. The LRA worked best in identifying high dose-infiltrated leaves (in infiltrated and non-infiltrated areas) whereas ANN offered a higher accuracy at identifying low dose-infiltrated areas. To assess the applicability of these results to cucurbits in a more general way, these models were validated for melon infected by the same pathogen, achieving accurate predictions for the infiltrated areas. The values of accuracy achieved are comparable to those found in the literature for classifiers identifying other infections based on data obtained by different techniques. Thus, MCFI in combination with thermography prove useful at providing data at lab scale that can be analyzed by machine learning. This approach could be scaled up to be applied in plant phenotyping.

Plant phenotyping relevance

マルチカラー蛍光画像と熱画像を用いて感染植物の健康・感染状態を分類し、統計モデルとANNを構築・検証しており、植物病害状態の取得・推定手法が中心である。

Code and data availability

The article describes multicolor fluorescence imaging and thermography of zucchini/melon leaves with LRA and ANN models built in R, but contains no data availability statement, no public dataset or image deposit, and no author code repository or URL. Analysis was done with proprietary software (Excel, SigmaPlot, FLIR,,

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