Detecting stress caused by nitrogen deficit using deep learning techniques applied on plant electrophysiological data.

Abstract

Plant electrophysiology carries a strong potential for assessing the health of a plant. Current literature for the classification of plant electrophysiology generally comprises classical methods based on signal features that portray a simplification of the raw data and introduce a high computational cost. The Deep Learning (DL) techniques automatically learn the classification targets from the input data, overcoming the need for precalculated features. However, they are scarcely explored for identifying plant stress on electrophysiological recordings. This study applies DL techniques to the raw electrophysiological data from 16 tomato plants growing in typical production conditions to detect the presence of stress caused by a nitrogen deficiency. The proposed approach predicts the stressed state with an accuracy of around 88%, which could be increased to over 96% using a combination of the obtained prediction confidences. It outperforms the current state-of-the-art with over 8% higher accuracy and a potential for a direct application in production conditions. Moreover, the proposed approach demonstrates the ability to detect the presence of stress at its early stage. Overall, the presented findings suggest new means to automatize and improve agricultural practices with the aim of sustainability.

Plant phenotyping relevance

植物の電気生理データから窒素欠乏ストレス状態を深層学習で推定する手法の開発・性能比較が中心であり、植物の生理状態を直接対象とするため採用。

Code and data availability

The supplied blocks describe electrophysiological recordings from 16 tomato plants and DL classification, but contain no data availability statement, no public deposit of the plant signal dataset, and no authors' code repository. The only GitHub link (hfawaz/dl-4-tsc) is a generic third-party time-series classification

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