Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification
<|reference_start|>Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification: 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.<|reference_end|>
@article{patra2024explainable,
    title={Explainable Light-Weight Deep Learning Pipeline for Improved Drought
  Stress Identification},
    author={Aswini Kumar Patra and Lingaraj Sahoo},
    journal={Frontiers in Plant Science, 15(2024)},
    year={2024},
    doi={10.3389/fpls.2024.1476130},
    archivePrefix={arXiv},
    eprint={2404.10073},
    primaryClass={cs.CV}
}