  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.
