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

Abstract

To meet the needs of a growing world population, we need to increase the global agricultural yields by employing modern, precision, and automated farming methods. 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 image-based machine learning usually do not consider plant stress's progressive or temporal nature. This time-invariant approach also requires images showing severe signs of stress to ensure high confidence detections, thereby reducing this approach's feasibility for early detection and recovery of plants under stress. In order to overcome the problem mentioned above, we propose a temporal analysis of the visual changes induced in the plant due to stress and apply it for the specific case of water stress identification 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 then develop an LSTM-CNN architecture to learn visual-temporal patterns from this dataset and predict the water stress category with high confidence. To establish a baseline context, we also conduct a comparative analysis of the CNN architecture used in the proposed model with the other CNN techniques used for the time-invariant classification of water stress. The results reveal that our proposed LSTM-CNN model has resulted in the ceiling level classification performance of \textbf{98.52\%} on JG-62 and \textbf{97.78\%} on Pusa-372 and the chickpea plant data. Lastly, we perform an ablation study to determine the LSTM-CNN model's performance on decreasing the amount of temporal session data used for training.

Plant phenotyping relevance

植物画像から水ストレス状態を時系列的に推定するLSTM-CNN手法を開発・比較評価しており、表現型取得・抽出が研究の中心である。

Code and data availability

The paper introduces a new chickpea plant shoot image dataset (7,680 images) and a CNN-LSTM pipeline, but no public deposit of the dataset, code, or trained models is stated anywhere in the supplied blocks. The only URL present (https://www.rohanwadhawan.com) is an author biography link, not a paper-specific asset. The

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