A Lightweight Real-Time Rice Blast Disease Segmentation Method Based on DFFANet

Changguang Feng, Minlan Jiang (), Qi Huang, Lingguo Zeng, Changjiang Zhang and Yulong Fan
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Changguang Feng: College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
Minlan Jiang: College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
Qi Huang: College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
Lingguo Zeng: College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
Changjiang Zhang: School of Electronics and Information Engineering, Taizhou University, Taizhou 318000, China
Yulong Fan: Zhejiang Dahua Technology Co., Ltd., Hangzhou 310053, China

Agriculture, 2022, vol. 12, issue 10, 1-12

Abstract: The evaluation of rice disease severity is a quantitative indicator for precise disease control, which is of great significance for ensuring rice yield. In the past, it was usually done manually, and the judgment of rice blast severity can be subjective and time-consuming. To address the above problems, this paper proposes a real-time rice blast disease segmentation method based on a feature fusion and attention mechanism: Deep Feature Fusion and Attention Network (abbreviated to DFFANet). To realize the extraction of the shallow and deep features of rice blast disease as complete as possible, a feature extraction (DCABlock) module and a feature fusion (FFM) module are designed; then, a lightweight attention module is further designed to guide the features learning, effectively fusing the extracted features at different scales, and use the above modules to build a DFFANet lightweight network model. This model is applied to rice blast spot segmentation and compared with other existing methods in this field. The experimental results show that the method proposed in this study has better anti-interference ability, achieving 96.15% MioU, a speed of 188 FPS, and the number of parameters is only 1.4 M, which can achieve a high detection speed with a small number of model parameters, and achieves an effective balance between segmentation accuracy and speed, thereby reducing the requirements for hardware equipment and realizing low-cost embedded development. It provides technical support for real-time rapid detection of rice diseases.

Keywords: spots segmentation; semantic segmentation; feature fusion; attention mechanism; multi-scale (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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