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.

翻译：为了满足不断增长的世界人口的需求,我们需要通过现代、精密和自动化耕作方法提高全球农业产量。近十年来,成功应用高通量工厂口味技术,将非侵入图像分析和机器学习结合起来,以识别和量化植物健康和疾病。然而,这些基于图像的机器学习通常不考虑植物压力的渐进或时间性质。这种时间变化性办法还要求显示压力严重的迹象,以确保高信任度检测,从而降低这一方法在早期发现和恢复受压力的植物方面的可行性。为了克服上述问题,我们提议对工厂因压力而引发的视觉变化进行时间分析,并将其应用于奇皮亚工厂拍摄的水压力识别具体案例。为此,我们考虑了两种鸡皮亚品种JG-62和Pusa-372的图像数据集,在三种水压力模型中,控制、幼苗和在开花前五个月内采集。我们随后开发了一个LSCN-CN-N架构,以学习因压力而引发的视觉-3-3-39的图像变化。我们用高端数据模型来测量了S的直观-直观-直径数据,我们使用了该模型的直径数据,我们使用的直观-直径分析运用了S-直径数据。