Maize leaf disease classification using convolutional neural networks and hyperparameter optimization

Author:

Da Rocha Erik Lucas,Rodrigues Larissa,Mari João Fernando

Abstract

Maize is an important food crop in the world, but several diseases affect the quality and quantity of agricultural production. Identifying these diseases is a very subjective and time-consuming task. The use of computer vision techniques allows automatizing this task and is essential in agricultural applications. In this study, we assess the performance of three state-of-the-art convolutional neural network architectures to classify maize leaf diseases. We apply enhancement methods such as Bayesian hyperparameter optimization, data augmentation, and fine-tuning strategies. We evaluate these CNNs on the maize leaf images from PlantVillage dataset, and all experiments were validated using a five-fold cross-validation procedure over the training and test sets. Our findings include the correlation between the maize leaf classes and the impact of data augmentation in pre-trained models. The results show that maize leaf disease classification reached 97% of accuracy for all CNNs models evaluated. Also, our approach provides new perspectives for the identification of leaf diseases based on computer vision strategies.

Publisher

Sociedade Brasileira de Computação - SBC

Cited by 19 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification;Scientific Reports;2024-09-14

2. Corn Leaf Disease Detection using Deep Learning Techniques;2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS);2024-07-10

3. Identification of the cultivars of the wheat crop from their seed images using deep learning: convolutional neural networks;Genetic Resources and Crop Evolution;2024-06-24

4. Harvesting Insights: An Innovative Framework for Maize Leaf Disease Detection through CNN and Random Forest Integrations;2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC);2024-01-27

5. VGG16-Based MaizeLeafNet Model for an Efficient Multiclass Classification of Maize Leaf Diseases;2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT);2024-01-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3