Publication

Abstract : Agriculture’s pivotal role in sustaining livelihoods and driving economic growth is widely recognized, yet various challenges like the adverse effects of climate change and limited resource availability hinder its productivity. Notably, plants are susceptible to various viruses and bacteria, impacting yield and food security. The emergence of deep learning, particularly convolutional neural networks (CNNs), has transformed agriculture by facilitating tasks such as disease detection. However, a significant challenge arises from the often unrealistic assumption that training and testing data share the same distribution. To address this, domain adaptation and transfer learning techniques have been employed, bridging the gap between different data distributions. Therefore, a novel framework named ‘Zero-Shot Transfer Learning’ is introduced. This addresses the challenge of improving classifier performance when trained on a source domain with different classes and tested on a target domain, exemplified by tomato and potato datasets. More specifically, in this framework, we include different CNN models along with techniques such as data augmentation, synthetic data generation, and robust discriminative losses, enhancing classifier performance in zero-shot scenarios. Extensive experiments on plant leaf disease classification under the zero-shot Transfer Learning assumption demonstrate the superiority of the proposed framework for effective disease classification. Ultimately, this framework holds the potential to promote crop yield optimization and ensure food security.