Patra Aswini Kumar, Sahoo Lingaraj
Department of Computer Science and Engineering, North Eastern Regional Institute of Science and Technology (NERIST), Itanagar, India.
Department of Bio-Science and Bio-Engineering, Indian Institute of Technology (IIT) Guwahati, Guwahati, Assam, India.
Front Plant Sci. 2024 Nov 28;15:1476130. doi: 10.3389/fpls.2024.1476130. eCollection 2024.
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 that aims to identify drought stress. While these approaches yield favorable results, real-time field applications require 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 unmanned aerial vehicles (UAV) in natural settings. The novelty lies in the synergistic combination of a pre-trained network with carefully designed custom layers. This architecture leverages the pre-trained network's feature extraction capabilities while the custom layers enable targeted dimensionality reduction and enhanced regularization, ultimately leading to improved performance. A key innovation of our work is the integration of gradient-based visualization inspired by Gradient-Class Activation Mapping (Grad-CAM), an explainability technique. This visualization approach sheds light on the internal workings of the deep learning model, often regarded as a "black box". By revealing the model's focus areas within the images, it enhances interpretability and fosters trust in the model's decision-making process.
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 achieving higher precision and accuracy. Thus, our explainable deep learning framework offers a powerful approach to drought stress identification with high accuracy and actionable insights.
早期识别作物中的干旱胁迫对于实施有效的缓解措施和减少产量损失至关重要。非侵入性成像技术通过捕捉水分亏缺条件下植物的细微生理变化具有巨大潜力。基于传感器的成像数据是机器学习和深度学习算法丰富的信息来源，有助于旨在识别干旱胁迫的进一步分析。虽然这些方法产生了良好的结果，但实时现场应用需要专门针对自然农业条件的复杂性设计的算法。
我们的工作提出了一种新颖的深度学习框架，用于对在自然环境中由无人机（UAV）拍摄的马铃薯作物中的干旱胁迫进行分类。其新颖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。这种架构利用预训练网络的特征提取能力，而自定义层实现有针对性的降维和增强正则化，最终提高性能。我们工作的一项关键创新是集成了受梯度类激活映射（Grad-CAM）启发的基于梯度的可视化，这是一种可解释性技术。这种可视化方法揭示了深度学习模型的内部工作方式，而深度学习模型通常被视为一个“黑匣子”。通过揭示模型在图像中的关注区域，它增强了可解释性并促进了对模型决策过程的信任。
我们提出的框架取得了卓越的性能，特别是使用DenseNet121预训练网络时，识别受胁迫类别的精度达到97%，总体准确率为91%。对现有最先进目标检测算法的比较分析表明，我们的方法在实现更高精度和准确性方面具有优越性。因此，我们的可解释深度学习框架提供了一种强大的方法，可高精度地识别干旱胁迫并提供可操作的见解。
