Study on the Recognition of Maize Leaf Disease Based on Deep Learning with Small Sample Size

LI Ting-ting; WANG Qing-qing; TANG Qi; ZHANG Hao; HUI Xiang-hui

doi:10.3969/j.issn.2096-6091.2023.24.001

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Study on the Recognition of Maize Leaf Disease Based on Deep Learning with Small Sample Size

RESEARCH PAPER | 更新时间：2025-03-26

- Study on the Recognition of Maize Leaf Disease Based on Deep Learning with Small Sample Size
- New Generation of Information Technology Vol. 6, Issue 24, Pages: 1-5(2023)
- 作者机构：
  
  河南农业大学信息与管理科学学院，河南郑州 450046
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2023.24.001
  CLC： TP391.41;TP18
- Published：31 December 2023
- 稿件说明：
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李婷婷, 王晴晴, 唐琦, 等. 深度学习下的小样本玉米叶片病害识别研究[J]. 新一代信息技术, 2023, 6(24): 01-05

LI Ting-ting, WANG Qing-qing, TANG Qi, et al. A Study on the Recognition of Maize Leaf Disease Based on Deep Learning with Small Sample Size[J]. New Generation of Information Technology, 2023, 6(24): 01-05
李婷婷, 王晴晴, 唐琦, 等. 深度学习下的小样本玉米叶片病害识别研究[J]. 新一代信息技术, 2023, 6(24): 01-05 DOI： 10.3969/j.issn.2096-6091.2023.24.001.

LI Ting-ting, WANG Qing-qing, TANG Qi, et al. A Study on the Recognition of Maize Leaf Disease Based on Deep Learning with Small Sample Size[J]. New Generation of Information Technology, 2023, 6(24): 01-05 DOI： 10.3969/j.issn.2096-6091.2023.24.001.

摘要

通过深度学习网络模型来实现玉米叶片病害识别分类已成为主流，但深度学习的模型需要拥有较多的数据集，然而实际情况是人工获得的样本种类和数据是有限的，且小样本数据集下的模型容易出现过度拟合从而丧失泛化能力。基于此背景，本文提出了一种迁移学习下的小样本玉米叶片病害识别方法，一方面从迁移学习角度出发，解决了因少样本而导致模型泛化差的问题；另一方面从深度学习的方向出发，采用并训练AlexNet、ResNet50和MobileNetV2模型，并对比三种模型在基于迁移学习下的病害识别准确率。研究结果表明，迁移学习有助于提高小样本泛化能力，MobileNetV2模型更适合小样本玉米叶片病害的识别。

Abstract

It is the main method to recognize maize leaf disease by deep learning network model

but the deep learning model needs more data sets

but in fact

the sample types and data obtained artificially are limited

moreover

the model with small sample data set is prone to over-fitting and loss of generalization ability. Based on this background

this paper proposes a method of identifying maize leaf disease with small samples by migration learning. On the one hand

it solves the problem of poor generalization caused by small samples from the perspective of migration learning

On the other hand

Alexnet

resnet 50 and mobilenet V2 models were adopted and trained from the direction of deep learning to compare the accuracy of disease recognition based on the three models. The results showed that the mobility learning could improve the generalization ability of small samples

and the mobilenet V2 model was more suitable for the identification of leaf diseases in small samples .

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references

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