Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different Environments

Convolution Self-Guided Transformer for Diagnosis and Recognition of Crop Disease in Different Environments

Accurately diagnosing crop diseases is crucial for agricultural productivity and food safety. This study addresses the challenge by developing an AI crop disease diagnosis platform, leveraging the strengths of Convolution Neural Networks (CNNs) and Vision Transformers. The proposed Convolution Self-...

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Bibliographic Details
Main Authors: Huinian Li, Nannan Li, Wenmin Wang, Chengcheng Yang, Ningxia Chen, Fuqin Deng
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Vision transformer
crop protection
crop disease diagnosis
self-guided attention
AI for agriculture
Online Access:https://ieeexplore.ieee.org/document/10749803/
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Summary:Accurately diagnosing crop diseases is crucial for agricultural productivity and food safety. This study addresses the challenge by developing an AI crop disease diagnosis platform, leveraging the strengths of Convolution Neural Networks (CNNs) and Vision Transformers. The proposed Convolution Self-Guided Transformer (CSGT) model integrates CNN’s local feature extraction with the Self-Guided Transformer’s(SGT) global information processing, enhancing the precision and efficiency of agricultural diagnostics. We selected two datasets with characteristics from Jiangsu and Guangdong provinces to validate our model, respectively representing controlled and uncontrolled agricultural environments. The CSGT model demonstrates exceptional performance in diagnosing crop diseases across diverse backgrounds, overcoming the limitations of current models, especially in complex settings. The CSGT model’s CNN layer, hybrid-scale, and self-guided attention mechanisms ensure accurate diagnoses, even in the presence of background clutter. CSGT has an accuracy of 96.9%(Apple),95.8%(Corn), 96.1%(Grape), and 96.5%(Tomato) when classifying crop diseases in a stable environment, and 95.8%(Rice) when classifying diseases in a complex environment. The research results are anticipated to enhance the effectiveness and stability of natural crop disease recognition applications.
ISSN:2169-3536

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