用于检测玉米致命坏死病和玉米条斑病毒病的深度学习方法

Tony O’Halloran , George Obaido , Bunmi Otegbade , Ibomoiye Domor Mienye
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引用次数: 0

摘要

玉米是撒哈拉以南非洲地区种植的重要作物,对粮食安全至关重要。然而,由于玉米致命坏死病(MLN)和玉米条斑病毒(MSV)等会导致严重减产的病害,玉米种植面临着巨大挑战。传统的植物病害诊断方法往往耗时且容易出错,因此需要更高效的方法。本研究探索了深度学习,特别是卷积神经网络(CNN)在玉米病害自动检测和分类中的应用。我们研究了六种架构:基本 CNN、EfficientNet V2 B0 和 B1、LeNet-5、VGG-16 和 ResNet50,使用由 MSV、MLN 和健康玉米叶片组成的 15344 张图像数据集。此外,我们还进行了超参数调整以提高模型的性能,并使用梯度加权类激活映射(Gradient-weighted Class Activation Mapping,Grad-CAM)来提高模型的可解释性。研究结果表明,高效网络 V2 B0 模型在区分健康植物和受疾病感染植物方面的准确率高达 99.99%。这项研究的结果有助于促进人工智能在农业领域的应用,尤其是在撒哈拉以南非洲地区诊断玉米疾病方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A deep learning approach for Maize Lethal Necrosis and Maize Streak Virus disease detection

Maize is an important crop cultivated in Sub-Saharan Africa, essential for food security. However, its cultivation faces significant challenges due to debilitating diseases such as Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV), which can lead to severe yield losses. Traditional plant disease diagnosis methods are often time-consuming and prone to errors, necessitating more efficient approaches. This study explores the application of deep learning, specifically Convolutional Neural Networks (CNNs), in the automatic detection and classification of maize diseases. We investigate six architectures: Basic CNN, EfficientNet V2 B0 and B1, LeNet-5, VGG-16, and ResNet50, using a dataset of 15344 images comprising MSV, MLN, and healthy maize leaves. Additionally, We performed hyperparameter tuning to improve the performance of the models and Gradient-weighted Class Activation Mapping (Grad-CAM) for model interpretability. Our results show that the EfficientNet V2 B0 model demonstrated an accuracy of 99.99% in distinguishing between healthy and disease-infected plants. The results of this study contribute to the advancement of AI applications in agriculture, particularly in diagnosing maize diseases within Sub-Saharan Africa.

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来源期刊
Machine learning with applications
Machine learning with applications Management Science and Operations Research, Artificial Intelligence, Computer Science Applications
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