Early identification of drought stress in crops is vital for implementing effective mitigation measures and reducing yield loss. Non-invasive imaging techniques hold immense potential by capturing subtle physiological changes in plants under water deficit. Sensor based imaging data serves as a rich source of information for machine learning and deep learning algorithms, facilitating further analysis aimed at identifying drought stress. While these approaches yield favorable results, real-time field applications requires algorithms specifically designed for the complexities of natural agricultural conditions. Our work proposes a novel deep learning framework for classifying drought stress in potato crops captured by UAVs in natural settings. The novelty lies in the synergistic combination of a pre-trained network with carefully designed custom layers. This architecture leverages feature extraction capabilities of the pre-trained network while the custom layers enable targeted dimensionality reduction and enhanced regularization, ultimately leading to improved performance. A key innovation of our work involves the integration of Gradient-Class Activation Mapping (Grad-CAM), an explainability technique. Grad-CAM sheds light on the internal workings of the deep learning model, typically referred to as a black box. By visualizing the focus areas of the model within the images, Grad-CAM fosters interpretability and builds trust in the decision-making process of the model. Our proposed framework achieves superior performance, particularly with the DenseNet121 pre-trained network, reaching a precision of 97% to identify the stressed class with an overall accuracy of 91%. Comparative analysis of existing state-of-the-art object detection algorithms reveals the superiority of our approach in significantly higher precision and accuracy.

翻译：早期识别作物干旱胁迫对于实施有效缓解措施及减少产量损失至关重要。非侵入式成像技术通过捕捉植物在水分亏缺条件下的细微生理变化，展现出巨大潜力。基于传感器的成像数据为机器学习和深度学习算法提供了丰富的信息源，有助于进一步开展干旱胁迫识别分析。尽管这些方法取得了良好效果，但实时田间应用需要专门针对自然农业条件复杂性的算法。本研究提出了一种新颖的深度学习框架，用于在自然环境中对无人机拍摄的马铃薯作物进行干旱胁迫分类。其创新之处在于将预训练网络与精心设计的自定义层进行协同组合。该架构利用了预训练网络的特征提取能力，而自定义层则实现了针对性的降维与增强正则化，最终提升了性能。本研究的关键创新在于集成梯度加权类激活映射（Grad-CAM）这一可解释性技术。Grad-CAM揭示了通常被称为黑箱的深度学习模型内部工作机制。通过可视化模型在图像中的关注区域，Grad-CAM增强了可解释性，并提升了模型决策过程的信任度。我们提出的框架取得了优异性能，特别是采用DenseNet121预训练网络时，对胁迫类别的识别精确率达到97%，总体准确率达91%。与现有最先进目标检测算法的对比分析表明，本方法在显著更高的精确率和准确率方面具有优越性。