Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification

• URL: http://arxiv.org/abs/2404.10073v3
• Date: Wed, 31 Jul 2024 15:22:39 GMT
• Title: Explainable Light-Weight Deep Learning Pipeline for Improved Drought Stress Identification
• Authors: Aswini Kumar Patra, Lingaraj Sahoo,
• Abstract summary: Early identification of drought stress in crops is vital for implementing effective mitigation measures and reducing yield loss. Our work proposes a novel deep learning framework for classifying drought stress in potato crops captured by UAVs in natural settings. A key innovation of our work involves the integration of Gradient-Class Activation Mapping (Grad-CAM), an explainability technique.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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