Improved Potato Crop Disease Classification Using Ensembled Convolutional Neural Network

IF 2.3 3区 农林科学 Q1 AGRONOMY
Gurpreet Singh, Geeta Kasana, Karamjeet Singh
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Abstract

Potatoes are an essential crop cultivated in numerous regions around the globe, but they frequently get impacted by diseases that lower their production and quality. To ensure the crop reaches its maximum potential, controlling the diseases in the initial or early stages is necessary. Recent developments in deep learning algorithms have demonstrated significant improvements in predicting agricultural diseases at various stages. However, contemporary deep learning models frequently exhibit real-world performance and generalization capabilities limitations. This study proposes an ensemble convolutional neural network model that combines the three most widely used models, VGG16, MobileNetV2, and ResNet50, to increase generalizability and improve accuracy in the classification of potato crop diseases. The proposed model is trained on a large dataset containing 6644 images of potato leaves, which is constructed by merging three different publicly available datasets. These datasets are originally collected from three distinct locations around the globe (the USA, Ethiopia, and Pakistan). The model aims to achieve improvement in accuracy and maintain generalizability for classifying potato fungal diseases. The proposed ensemble architecture achieved an accuracy of 98.49%, surpassing the individual models. In this study, a web-based interface is developed for the evaluation of the model. The proposed model is tested on this web interface with the images obtained through the Google Image Search Engine. A plant pathologist supervised the selection of images and the pre-processing of the dataset. The results of the evaluation indicate that the model will perform better when deployed in real-world situations.

Abstract Image

利用集合卷积神经网络改进马铃薯作物病害分类
马铃薯是全球许多地区都在种植的重要作物,但它们经常受到病害的影响,导致产量和质量下降。为确保作物发挥最大潜力,有必要在初期或早期阶段控制病害。深度学习算法的最新发展表明,在预测不同阶段的农业病害方面取得了显著进步。然而,当代深度学习模型经常表现出现实世界性能和泛化能力的局限性。本研究提出了一种集合卷积神经网络模型,该模型结合了 VGG16、MobileNetV2 和 ResNet50 这三种最广泛使用的模型,以增强泛化能力并提高马铃薯作物病害分类的准确性。提出的模型是在一个包含 6644 张马铃薯叶片图像的大型数据集上进行训练的,该数据集是通过合并三个不同的公开数据集而构建的。这些数据集最初是从全球三个不同地点(美国、埃塞俄比亚和巴基斯坦)收集的。该模型旨在提高马铃薯真菌疾病分类的准确性,并保持其通用性。提议的集合架构达到了 98.49% 的准确率,超过了单个模型。本研究开发了一个基于网络的界面,用于对模型进行评估。通过谷歌图像搜索引擎获得的图像在该网络界面上对所提出的模型进行了测试。一名植物病理学家监督了图像的选择和数据集的预处理。评估结果表明,该模型在实际应用中会有更好的表现。
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来源期刊
Potato Research
Potato Research AGRONOMY-
CiteScore
5.50
自引率
6.90%
发文量
66
审稿时长
>12 weeks
期刊介绍: Potato Research, the journal of the European Association for Potato Research (EAPR), promotes the exchange of information on all aspects of this fast-evolving global industry. It offers the latest developments in innovative research to scientists active in potato research. The journal includes authoritative coverage of new scientific developments, publishing original research and review papers on such topics as: Molecular sciences; Breeding; Physiology; Pathology; Nematology; Virology; Agronomy; Engineering and Utilization.
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