使用卷积神经网络进行葡萄植物病害分类

Cemal İhsan Sofuoğlu, Derya Birant
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引用次数: 0

摘要

植物病害分类是利用机器学习技术,根据输入的植物叶片图像的某些特征来确定病害类型。这是一个重要的研究领域,因为早期识别和治疗植物病害对于挽救农作物、预防农业灾害和提高农业生产率至关重要。本研究提出了一种新的卷积神经网络模型,可对农业部门的植物叶片病害进行准确分类。通过设计深度学习架构,该模型特别适用于从图像中对葡萄叶片的植物病害进行分类。此外,还开发了一个网络应用程序来帮助农业工作者。在真实世界图像上进行的实验表明,与最先进的模型(89.84%)相比,所提出的模型(98.53%)在准确率方面平均取得了显著提高(8.7%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evrişimli Sinir Ağının Üzüm Bitkisi Hastalık Sınıflandırması için Kullanılması
Plant disease classification is the use of machine learning techniques for determining the type of disease from the input leaf images of the plants based on certain features. It is an important research area since early identification and treatment of plant disease is critical for saving crops, preventing agricultural disasters, and improving productivity in agriculture. This study proposes a new convolutional neural network model that accurately classifies the diseases on the plant leaves for the agriculture sectors. It especially works on the classification of plant diseases for grape leaves from images by designing a deep-learning architecture. A web application was also implemented to help the agricultural workers. The experiments carried out on real-world images showed that a significant improvement (8.7%) on average was achieved by the proposed model (98.53%) against the state-of-the-art models (89.84%) in terms of accuracy.
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