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

TL;DR

This paper presents a deep learning approach using CNN-LSTM networks for early detection of water stress in chickpea plants through temporal image analysis, achieving high accuracy and robustness.

Contribution

It introduces a novel CNN-LSTM based pipeline for temporal plant stress analysis, outperforming existing time-invariant methods in early water stress detection.

Findings

Achieved over 98% classification accuracy on chickpea datasets.

Outperformed previous methods by at least 14%.

Demonstrated robustness to noisy inputs with minimal accuracy loss.

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 spatio-temporal 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.