用于快速准确诊断番茄植物病害的增强型深度学习架构

Shahab Ul Islam, Shahab Zaib, G. Ferraioli, V. Pascazio, Gilda Schirinzi, Ghassan Husnain
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引用次数: 0

摘要

深度神经网络在农业生产中表现出色。农业生产是最重要的部门之一,因为它对任何社会的经济和社会生活都有直接影响。植物病害识别是农业生产面临的一大挑战,为此我们需要一种快速、准确的技术来识别植物病害。随着深度学习技术的不断进步,我们可以开发出一种强大而准确的系统。本研究调查了深度学习在准确、快速识别番茄植物病害方面的应用。在这项研究中,我们使用了患有 10 种疾病(包括健康植物)的番茄植物的单独和合并数据集。这项工作的主要目的是检查现有卷积神经网络模型(如 Visual Geometry Group、Residual Net 和 DenseNet)在番茄植物病害检测方面的准确性,然后设计一个定制的深度神经网络模型,使番茄植物病害检测的准确性达到最佳。我们使用包含 10 个类别的 18,000 多张和 25,000 多张图像的数据集对我们的模型进行了训练和测试。我们的自定义模型达到了 99% 以上的准确率。与其他 CNN 相比,我们用更少的训练时间和更低的计算成本就实现了如此高的准确率。这项研究证明了深度学习在高效、准确地检测番茄植物病害方面的潜力,这将使农民受益,并有助于提高农业产量。定制模型的高效性能使其有望在现实世界的农业环境中得到实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Deep Learning Architecture for Rapid and Accurate Tomato Plant Disease Diagnosis
Deep neural networks have demonstrated outstanding performances in agriculture production. Agriculture production is one of the most important sectors because it has a direct impact on the economy and social life of any society. Plant disease identification is a big challenge for agriculture production, for which we need a fast and accurate technique to identify plant disease. With the recent advancement in deep learning, we can develop a robust and accurate system. This research investigated the use of deep learning for accurate and fast tomato plant disease identification. In this research, we have used individual and merged datasets of tomato plants with 10 diseases (including healthy plants). The main aim of this work is to check the accuracy of the existing convolutional neural network models such as Visual Geometry Group, Residual Net, and DenseNet on tomato plant disease detection and then design a custom deep neural network model to give the best accuracy in case of the tomato plant. We have trained and tested our models with datasets containing over 18,000 and 25,000 images with 10 classes. We achieved over 99% accuracy with our custom model. This high accuracy was achieved with less training time and lower computational cost compared to other CNNs. This research demonstrates the potential of deep learning for efficient and accurate tomato plant disease detection, which can benefit farmers and contribute to improved agricultural production. The custom model’s efficient performance makes it promising for practical implementation in real-world agricultural settings.
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