Attention-enhanced corn disease diagnosis using few-shot learning and VGG16

Abstract

Plant Disease Detection in the early stage is paramount. Traditionally, it was done manually by the farmers, which is a laborious and time-intensive task. With the advent of AI, Machine Learning and Deep Learning methods are used to detect and categorize plant diseases. However, they rely on extensive datasets for accurate prediction, which is impracticable to acquire and annotate. Thus, Few Shot Learning is the state-of-the-art model in machine learning, which requires minimum examples to train the model for generalization. As humans need a few examples to recognize things, Few-shot Learning mimics the same human brain process. The proposed work uses a pre-trained convolution neural network, VGG16, as the backbone, fine-tuned on the corn disease dataset. An attention module is integrated with the backbone, and further, prototypical few-shot learning is used for corn disease prediction and classification with an accuracy of 98.25 %.

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  The proposed model intends to identify the diseases early, so the insights generated would be relevant for farmers, and probable losses can be reduced.
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  By applying Few-Shot Learning, the system avoids the significant challenges of requiring extensively annotated datasets, making it feasible for real-world agricultural applications.
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  Incorporating a fine-tuned VGG16 backbone along with an attention mechanism and prototypical Few-Shot Learning results in a robust and scalable solution with high accuracy for classifying corn diseases.