Cotton Disease Recognition Method in Natural Environment Based on Convolutional Neural Network

Yi Shao, Wenzhong Yang (), Jiajia Wang, Zhifeng Lu, Meng Zhang and Danny Chen
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Yi Shao: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China
Wenzhong Yang: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China
Jiajia Wang: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China
Zhifeng Lu: School of Information Science and Technology, Xinjiang Teacher’s College, Urumqi 830043, China
Meng Zhang: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China
Danny Chen: School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China

Agriculture, 2024, vol. 14, issue 9, 1-21

Abstract: As an essential component of the global economic crop, cotton is highly susceptible to the impact of diseases on its yield and quality. In recent years, artificial intelligence technology has been widely used in cotton crop disease recognition, but in complex backgrounds, existing technologies have certain limitations in accuracy and efficiency. To overcome these challenges, this study proposes an innovative cotton disease recognition method called CANnet, and we independently collected and constructed an image dataset containing multiple cotton diseases. Firstly, we introduced the innovatively designed Reception Field Space Channel (RFSC) module to replace traditional convolution kernels. This module combines dynamic receptive field features with traditional convolutional features to effectively utilize spatial channel attention, helping CANnet capture local and global features of images more comprehensively, thereby enhancing the expressive power of features. At the same time, the module also solves the problem of parameter sharing. To further optimize feature extraction and reduce the impact of spatial channel attention redundancy in the RFSC module, we connected a self-designed Precise Coordinate Attention (PCA) module after the RFSC module to achieve redundancy reduction. In the design of the classifier, CANnet abandoned the commonly used MLP in traditional models and instead adopted improved Kolmogorov Arnold Networks-s (KANs) for classification operations. KANs technology helps CANnet to more finely utilize extracted features for classification tasks through learnable activation functions. This is the first application of the KAN concept in crop disease recognition and has achieved excellent results. To comprehensively evaluate the performance of CANnet, we conducted extensive experiments on our cotton disease dataset and a publicly available cotton disease dataset. Numerous experimental results have shown that CANnet outperforms other advanced methods in the accuracy of cotton disease identification. Specifically, on the self-built dataset, the accuracy reached 96.3%; On the public dataset, the accuracy reached 98.6%. These results fully demonstrate the excellent performance of CANnet in cotton disease identification tasks.

Keywords: CANnet; cotton disease identification; precision agriculture; leaf diagnosis; artificial intelligence (AI) (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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