  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.
