Explainable Light-Weight Deep Learning Pipeline for Improved Drought
  Stres
Aswini Kumar Patra Lingaraj Sahoo
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 pretrained 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 98% to identify the stressed class with an overall
accuracy of 90%. Comparative analysis of existing state-of-the-art object
detection algorithms reveals the superiority of our approach in significantly
higher precision and accuracy.