DenseNet Model Based on Transfer Learning in Plant Leaf Pest Classification

LIU Yi; SUN Yan-bin; ZHAI Feng-guo; LIANG Xin

doi:10.3969/j.issn.2096-6091.2023.12.005

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DenseNet Model Based on Transfer Learning in Plant Leaf Pest Classification

RESEARCH PAPER | 更新时间：2025-03-26

- DenseNet Model Based on Transfer Learning in Plant Leaf Pest Classification
- New Generation of Information Technology Vol. 6, Issue 12, Pages: 24-30(2023)
- 作者机构：
  
  1.牡丹江医学院，黑龙江牡丹江 157011
  2.牡丹江医学院附属红旗医院，黑龙江牡丹江 157011
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2023.12.005
  CLC： TP391.41;TP18
- Accepted：2023-06-26，
  
  Published：30 June 2023
- 稿件说明：
移动端阅览
刘艺, 孙延斌, 翟凤国, 等. 基于迁移学习的DenseNet模型在植物叶片病虫害分类研究[J]. 新一代信息技术, 2023, 6(12): 24-30

LIU Yi, SUN Yan-bin, ZHAI Feng-guo, et al. DenseNet Model Based on Transfer Learning in Plant Leaf Pest Classification[J]. New Generation of Information Technology, 2023, 6(12): 24-30
刘艺, 孙延斌, 翟凤国, 等. 基于迁移学习的DenseNet模型在植物叶片病虫害分类研究[J]. 新一代信息技术, 2023, 6(12): 24-30 DOI： 10.3969/j.issn.2096-6091.2023.12.005.

LIU Yi, SUN Yan-bin, ZHAI Feng-guo, et al. DenseNet Model Based on Transfer Learning in Plant Leaf Pest Classification[J]. New Generation of Information Technology, 2023, 6(12): 24-30 DOI： 10.3969/j.issn.2096-6091.2023.12.005.

摘要

病虫害的发生会对农作物的品质和产量产生不利影响，因此进行病害诊断和识别对提升农作物生产质量和经济效益至关重要。本研究旨在开发一种基于迁移学习的DenseNet模型，实现多种植物病虫害的高效、准确识别。以预训练模型作为基础模型，构建了一个新的模型，并对其进行了两轮训练。最终，实现了96%的识别准确率，成功分类了几种植物叶片病虫害。这项研究为植物病害诊断和识别任务提供了有价值的参考。

Abstract

The occurrence of pests and diseases has a negative impact on the quality and yield of crops

so the diagnosis and identification of diseases is very important to improve the quality and economic benefits of crop production. The aim of this study is to develop a DenseNet model based on transfer learning to realize efficient and accurate identification of various plant diseases and pests. By using the pre-training model as the basic model

a new model was constructed and two rounds of training were conducted on it. Finally

the recognition accuracy of 96% was achieved

and several plant leaf diseases and insect pests were successfully classified. This study provides a valuable reference for plant disease diagnosis and identification tasks.

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references

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