基于深度学习的黑克兰植物叶片病害分类的比较研究

Elham Tahsin Yasin, Ramazan Kursun, Murat Koklu
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引用次数: 0

摘要

植物病害发病率不断上升,给农业领域造成了相当大的障碍,导致作物产量大幅下降,并对粮食安全构成威胁。为了解决黑革兰植物叶片病害(BPLD)的紧迫问题,本研究试图通过应用深度学习方法来解决疾病分类问题。该方法利用了一个全面的数据集,包括炭疽病、叶皱病、白粉病和黄花叶病,所有这些疾病都会影响黑克作物。通过采用这种先进的技术,我们的目标是为有效打击BPLD提供有价值的见解。本研究应用深度学习模型Darknet-53、ResNet-101、GoogLeNet和EfficientNet-B0对植物病害进行分类。Darknet-53的准确率为98.51%,其次是ResNet-101(97.51%)、GoogLeNet(96.52%)和effentnet - b0(77.61%)。这些发现证明了深度学习在准确识别疾病方面的潜力,有利于农业。该研究对黑革兰氏植物叶病(BPLD)分类的深度学习模型进行了比较分析,发现Darknet-53和ResNet-101表现较好。在实际农业情景中实施这些模型有望实现疾病的早期检测和干预,减少潜在的作物损失。取得的高准确度标志着自动化疾病识别的重大进展,使农业部门受益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning-Based Classification of Black Gram Plant Leaf Diseases: A Comparative Study
The escalating incidence of plant diseases presents considerable obstacles to the agricultural domain, resulting in substantial reductions in crop yield and posing a threat to food security. To address the pressing concern of Black Gram Plant Leaf Diseases (BPLD), this research endeavors to tackle disease classification through the application of a deep learning methodology. The approach leverages a comprehensive dataset that encompasses Anthracnose, Leaf Crinkle, Powdery Mildew, and Yellow Mosaic diseases, all of which affect the black gram crop. By employing this advanced technique, we aim to contribute valuable insights to combat BPLD effectively. Our research applies deep learning models, including Darknet-53, ResNet-101, GoogLeNet, and EfficientNet-B0, to classify plant diseases. Darknet-53 achieved 98.51% accuracy, followed by ResNet-101 (97.51%), GoogLeNet (96.52%), and EfficientNet-B0 (77.61%). These findings demonstrate the potential of deep learning for accurate disease identification, benefiting agriculture. The study provides a comparative analysis of deep learning models for Black Gram Plant Leaf Disease (BPLD) classification, revealing Darknet-53 and ResNet-101 as superior performers. Implementing these models in real-world agricultural scenarios holds promise for early disease detection and intervention, reducing potential crop losses. The high accuracy achieved signifies significant progress in automating disease recognition, benefiting the agricultural sector.
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