Intelligent Monitoring of Stress Induced by Water Deficiency in Plants Using Deep Learning

Abstract

In the recent decade, high-throughput plant phenotyping techniques, which combine non-invasive image analysis and machine learning, have been successfully applied to identify and quantify plant health and diseases. However, these techniques usually do not consider the progressive nature of plant stress and often require images showing severe signs of stress to ensure high confidence detection, thereby reducing the feasibility for early detection and recovery of plants under stress. To overcome the problem mentioned above, we propose a deep learning pipeline for the temporal analysis of the visual changes induced in the plant due to stress and apply it to the specific water stress identification case in Chickpea plant shoot images. For this, we have considered an image dataset of two chickpea varieties JG-62 and Pusa-372, under three water stress conditions; control, young seedling, and before flowering, captured over five months. We have employed a variant of Convolutional Neural Network -Long Short Term Memory (CNN-LSTM) network to learn spatiotemporal patterns from the chickpea plant dataset and use them for water stress classification. Our model has achieved ceiling level classification performance of 98.52% on JG-62 and 97.78% on Pusa-372 chickpea plant data and has outperformed the best reported time-invariant technique by at least 14% for both JG-62 and Pusa-372 species, to the best of our knowledge. Furthermore, our CNN-LSTM model has demonstrated robustness to noisy input, with a less than 2.5 % dip in average model accuracy and a small standard deviation about the mean for both species. Lastly, we have performed an ablation study to analyze the performance of the CNN-LSTM model by decreasing the number of temporal session data used for training.

Plant phenotyping relevance

植物画像の時系列変化から水分ストレス状態を推定するCNN-LSTM手法を開発・評価しており、植物フェノタイピング手法が研究の中心です。

Code and data availability

The paper introduces a chickpea plant shoot image dataset (7,680 images) and CNN-LSTM models, but no public deposit, availability statement, or authors' URL for the dataset, code, or trained models appears in the supplied blocks. The only URL (https://www.rohanwadhawan.com) is a co-author's biographical homepage, not a

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