Maize Disease Classification System Design Based on Improved ConvNeXt

Han Li, Mingyang Qi, Baoxia Du, Qi Li, Haozhang Gao, Jun Yu, Chunguang Bi, Helong Yu, Meijing Liang, Guanshi Ye () and You Tang ()
Additional contact information
Han Li: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Mingyang Qi: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Baoxia Du: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Qi Li: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Haozhang Gao: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
Jun Yu: School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, China
Chunguang Bi: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Helong Yu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Meijing Liang: Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA
Guanshi Ye: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
You Tang: Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China

Sustainability, 2023, vol. 15, issue 20, 1-16

Abstract: Maize diseases have a great impact on agricultural productivity, making the classification of maize diseases a popular research area. Despite notable advancements in maize disease classification achieved via deep learning techniques, challenges such as low accuracy and identification difficulties still persist. To address these issues, this study introduced a convolutional neural network model named Sim-ConvNeXt, which incorporated a parameter-free SimAM attention module. The integration of this attention mechanism enhanced the ability of the downsample module to extract essential features of maize diseases, thereby improving classification accuracy. Moreover, transfer learning was employed to expedite model training and improve the classification performance. To evaluate the efficacy of the proposed model, a publicly accessible dataset with eight different types of maize diseases was utilized. Through the application of data augmentation techniques, including image resizing, hue, cropping, rotation, and edge padding, the dataset was expanded to comprise 17,670 images. Subsequently, a comparative analysis was conducted between the improved model and other models, wherein the approach demonstrated an accuracy rate of 95.2%. Notably, this performance represented a 1.2% enhancement over the ConvNeXt model and a 1.5% improvement over the advanced Swin Transformer model. Furthermore, the precision, recall, and F1 scores of the improved model demonstrated respective increases of 1.5% in each metric compared to the ConvNeXt model. Notably, using the Flask framework, a website for maize disease classification was developed, enabling accurate prediction of uploaded maize disease images.

Keywords: CNN; SimAM attention; transfer learning; data augmentation; maize disease classification (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/20/14858/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/20/14858/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:20:p:14858-:d:1259315

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().