DiffuCNN: Tobacco Disease Identification and Grading Model in Low-Resolution Complex Agricultural Scenes

Huizhong Xiong, Xiaotong Gao, Ningyi Zhang, Haoxiong He, Weidong Tang, Yingqiu Yang, Yuqian Chen, Yang Jiao, Yihong Song () and Shuo Yan ()
Additional contact information
Huizhong Xiong: China Agricultural University, Beijing 100083, China
Xiaotong Gao: China Agricultural University, Beijing 100083, China
Ningyi Zhang: China Agricultural University, Beijing 100083, China
Haoxiong He: China Agricultural University, Beijing 100083, China
Weidong Tang: China Agricultural University, Beijing 100083, China
Yingqiu Yang: China Agricultural University, Beijing 100083, China
Yuqian Chen: China Agricultural University, Beijing 100083, China
Yang Jiao: China Agricultural University, Beijing 100083, China
Yihong Song: Tsinghua University, Beijing 100083, China
Shuo Yan: China Agricultural University, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 2, 1-21

Abstract: A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model significantly enhances the accuracy of detecting tobacco lesions under low-resolution conditions. After detecting lesions, the grading of the disease severity is achieved through counting. The key features of DiffuCNN include a resolution enhancement module based on diffusion, an object detection network optimized through filter pruning, and the employment of the CentralSGD optimization algorithm. Experimental results demonstrate that DiffuCNN surpasses other models in precision, with respective values of 0.98 on precision, 0.96 on recall, 0.97 on accuracy, and 62 FPS. Particularly in counting tobacco lesions, DiffuCNN exhibits an exceptional performance, attributable to its efficient network architecture and advanced image processing techniques. The resolution enhancement module based on diffusion amplifies minute details and features in images, enabling the model to more effectively recognize and count tobacco lesions. Concurrently, filter pruning technology reduces the model’s parameter count and computational burden, enhancing the processing speed while retaining the capability to recognize key features. The application of the CentralSGD optimization algorithm further improves the model’s training efficiency and final performance. Moreover, an ablation study meticulously analyzes the contribution of each component within DiffuCNN. The results reveal that each component plays a crucial role in enhancing the model performance. The inclusion of the diffusion module significantly boosts the model’s precision and recall, highlighting the importance of optimizing at the model’s input end. The use of filter pruning and the CentralSGD optimization algorithm effectively elevates the model’s computational efficiency and detection accuracy.

Keywords: tobacco disease identification; DiffuCNN; object detection; deep learning (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:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/2/318/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/2/318/ (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:jagris:v:14:y:2024:i:2:p:318-:d:1340377

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

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