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Zero-Shot Transfer Learning Framework for Plant Leaf Disease Classification
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DOI：10.1109/ACCESS.2023.3343759.png)
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摘要
En 中文
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.
Keyword:
Transfer learning
Task analysis
Support vector machines
Agriculture
Training
Image segmentation
Feature extraction
Classification algorithms
Deep learning
Diseases
Zero-shot learning
Economics
Market research
Climate change
classification
deep learning
zero-shot transfer learning
disease identification
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Unsupervised image translation using adversarial networks for improved plant disease recognition
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