The identification of water stress is a major challenge for timely and effective irrigation to ensure global food security and sustainable agriculture. Several direct and indirect methods exist for identification of crop water stress, but they are time consuming, tedious and require highly sophisticated sensors or equipment. Image processing is one of the techniques which can help in the assessment of water stress directly. Machine learning techniques combined with image processing can aid in identifying water stress beyond the limitations of traditional image processing. Deep learning (DL) techniques have gained momentum recently for image classification and the convolutional neural network based on DL is being applied widely. In present study, comparative assessment of three DL models: AlexNet, GoogLeNet and Inception V3 are applied for identification of water stress in maize (Zea mays), okra (Abelmoschus esculentus) and soybean (Glycine max) crops. A total of 1200 digital images were acquired for each crop to form the input dataset for the deep learning models. Among the three models, performance of GoogLeNet was found to be superior with an accuracy of 98.3, 97.5 and 94.1% for maize, okra and soybean, respectively. The onset of convergence in GoogLeNet models commenced after 8 epochs with 22 (maize), 31 (okra) and 15 (soybean) iterations per epoch with error rate of less than 7.5%.

识别水分胁迫是及时有效灌溉以确保全球粮食安全和可持续农业的主要挑战。存在几种直接和间接的方法来识别农作物水分胁迫，但是它们费时，繁琐并且需要高度复杂的传感器或设备。图像处理是可以直接帮助评估水分胁迫的技术之一。机器学习技术与图像处理相结合可以帮助识别水分压力，这超出了传统图像处理的局限性。深度学习（DL）技术最近在图像分类中获得了发展动力，基于DL的卷积神经网络得到了广泛的应用。在本研究中，对三种DL模型进行了比较评估：AlexNet，玉米，秋葵（Abelmoschus esculentus）和大豆（Glycine max）作物。每个作物共获取了1200个数字图像，以形成深度学习模型的输入数据集。在这三个模型中，发现GoogLeNet的性能优越，对玉米，黄秋葵和大豆的准确度分别为98.3％，97.5％和94.1％。GoogLeNet模型的收敛始于8个时期后，每个时期进行22次（玉米），31次（秋葵）和15次（大豆）迭代，错误率低于7.5％。