Image Information Contribution Evaluation for Plant Diseases Classification via Inter-Class Similarity

Jiachen Yang, Yue Yang, Yang Li, Shuai Xiao () and Sezai Ercisli
Additional contact information
Jiachen Yang: School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
Yue Yang: School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
Yang Li: School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
Shuai Xiao: School of Electrical Automation and Information Engineering, Tianjin University, Tianjin 300072, China
Sezai Ercisli: Department of Horticulture, Faculty of Agriculture, Ataturk University, 25240 Erzurum, Turkey

Sustainability, 2022, vol. 14, issue 17, 1-13

Abstract: Combineingplant diseases identification and deep learning algorithm can achieve cost-effective prevention effect, and has been widely used. However, the current field of intelligent plant diseases identification still faces the problems of insufficient data and inaccurate classification. Aiming to resolve these problems, the present research proposes an image information contribution evaluation method based on the analysis of inter-class similarity. Combining this method with the active learning image selection strategy can provide guidance for the collection and annotation of intelligent identification datasets of plant diseases, so as to improve the recognition effect and reduce the cost. The method proposed includes two modules: the inter-classes similarity evaluation module and the image information contribution evaluation module. The images located on the decision boundary between high similarity classes will be analysis as high information contribution images, they will provide more information for plant diseases classification. In order to verify the effectiveness of this method, experiments were carried on the fine-grained classification dataset of tomato diseases. Experimental results confirm the superiority of this method compared with others. This research is in the field of plant disease classification. For the detection and segmentation, further research is advisable.

Keywords: plant disease identification; smart agriculture; few-shot learning; fine-grained classification; inter-class similarity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/17/10938/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/17/10938/ (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:14:y:2022:i:17:p:10938-:d:904373

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 ().