ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1558349

This article is part of the Research TopicPrecision Information Identification and Integrated Control: Pest Identification, Crop Health Monitoring, and Field ManagementView all 19 articles

IMNM: Integrated Multi-Network Model for Identifying Pepper Leaf Diseases

Provisionally accepted
Zhaopeng  CaiZhaopeng Cai1,2*Nadia  FarhanaNadia Farhana3*Asif  Mahbub KarimAsif Mahbub Karim4*Fengyan  ZhaiFengyan Zhai5Wenwen  HuangWenwen Huang6Meng  GuoMeng Guo1,2
  • 1Research Center of Smart City and Big Data Engineering of Henan Province, Research Center of Intelligent Campus Application Engineering of Henan Province, Henan University of Urban Construction, Pingdingshan, China
  • 2School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, Henan Province, China
  • 3Department of Business Administration, Stamford University Bangladesh, Dhaka, Bangladesh
  • 4Binary Graduate School, Binary University of Management & Entrepreneurship, Puchong Selangor, Malaysia
  • 5Department of Plant Pathology, Henan Province Engineering Research Center of Biological Pesticide & Fertilizer Development and Synergistic Application, Henan Institute of Science and Technology, Xinxiang, China
  • 6School of Resources and Environment Science, Henan Institute of Science and Technology, Xinxiang, Henan Province, China

The final, formatted version of the article will be published soon.

Pepper is one of the most valuable vegetable crops. However, pepper leaf diseases have a significant impact on the growth of pepper yield. Since the spots of these diseases were complex in color and texture. Yet, their manual prevention and identification require much time and energy. It is critical to design a more efficient pepper leaf disease identification framework for improving the yield and quality of pepper. This study combines an improved ResNet, a dynamic convolution network (DCN), and a progressive prototype network (PPN) to create an integrated multi-network model (IMNM). This study concentrated on five different types of pepper leaf samples: healthy, virus, leaf blight, brown spot, and phyllosticta.Experimental results demonstrated that the IMNM yielded an identification accuracy score of 98.55%, superior to the outstanding Inception-V4, ShuffleNet-V3, and Efficient-B7. In addition, more experiments were carried out to confirm its generalization capabilities. The IMNM was used to identify the apple, wheat, and rice leaf diseases and achieved an average identification accuracy of 99.81%, and specificity, precision, sensitivity, and accuracy, all four indicators are all much greater than 98%. It has been confirmed that the IMNM can identify many agricultural leaf diseases, demonstrating its strong generalization capabilities. The proposed technique is a suitable starting point for promoting the intelligent identification of crop diseases and pests, and it includes suggestions for transferring deep learning models to field disease mobile identification equipment.

Keywords: Integrated, deep learning, multi-network Model, Identifying, pepper leaf diseases

Received: 10 Jan 2025; Accepted: 06 Jun 2025.

Copyright: © 2025 Cai, Farhana, Karim, Zhai, Huang and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Zhaopeng Cai, Research Center of Smart City and Big Data Engineering of Henan Province, Research Center of Intelligent Campus Application Engineering of Henan Province, Henan University of Urban Construction, Pingdingshan, China
Nadia Farhana, Department of Business Administration, Stamford University Bangladesh, Dhaka, Bangladesh
Asif Mahbub Karim, Binary Graduate School, Binary University of Management & Entrepreneurship, Puchong Selangor, Malaysia

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