About the journal

• Home page
• Contact information
• Aims and scope
• Indexing information
• Editorial policies
• ComSIS consortium
• Journal boards
• Managing board

For authors

• Information for contributors
• Article submission
• Accepted papers
• Copyright transfer form
• Download section

For readers

• Forthcoming articles
• Current issue
• Archive

For reviewers

• Review submissions
• Review submissions OJS

News

• Journal's Facebook page
• Special issue proposal
• Calls for special issues

FNNMFF: Crop Pests and Diseases Detection Based on Fuzzy Neural Network and Multilevel Feature Fusion in Remote Sensing Images

Shoulin Yin¹, Hang Li², Mirjana Ivanovic³, Tao Chen¹ and Lin Teng¹

1. College of Information and Communication Engineering, Harbin Engineering University
   Harbin 150001, China
2. College of Artificial Intelligence, Shenyang Normal University
   Shenyang 110034 China
   lihang@synu.edu.cn
3. Faculty of Sciences, University of Novi Sad
   Novi Sad, Serbia
   mira@dmi.uns.ac.rs

Abstract

To solve the problem that the detection effect of crop pests and diseases is not ideal due to the complicated image background and the interference of irrelevant factors, this paper proposes a novel crop pests and diseases detection based on fuzzy neural network and multilevel feature fusion in remote sensing images. Firstly, the model is based on YOLOv5 and extracts the semantic level information of different depth features from the convolutional neural network, and then combines the weight aggregation module to learn the weight of each layer feature adaptively. Then the learned weights are loaded to the segmentation graphs obtained by sampling on each feature layer to obtain the final segmentation results. In this model, a fuzzy learning module is added to the skip connection part to remove noise features and alleviate the uncertainty between classes. The traditional cross entropy loss involves activating the output value with the Softmax function and calculating a weighted cross entropy loss with the label. If the weight of the cross entropy loss term is not adjusted, the model will tend to update the weight related to the background, which makes it difficult to deal with the category imbalance in remote sensing images. Therefore, we use focus loss to alleviate the problem of class imbalance in images. The results on public data sets show that the accuracy rate of the proposed model in this paper is over 95%, the recall rate is over 85%, and the average accuracy is 91.2%. In terms of F1, compared with other advanced methods, the presented method has achieved improvements of 6.6%, 8.5%, and 7.7% respectively. It shows that the new model has strong robustness and generalization for crop pest detection.

Key words

Remote sensing images, fuzzy neural network, crop pests and diseases detection, multilevel feature fusion, focus loss

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS251109012Y

Publication information

Volume 23, Issue 1 (January 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

Available in PDF
Portable Document Format

How to cite

Yin, S., Li, H., Ivanovic, M., Chen, T., Teng, L.: FNNMFF: Crop Pests and Diseases Detection Based on Fuzzy Neural Network and Multilevel Feature Fusion in Remote Sensing Images. Computer Science and Information Systems, Vol. 23, No. 1, 369-396. (2026), https://doi.org/10.2298/CSIS251109012Y