基于Keras框架的高光谱图像深度学习LSTM方法

N. Gayatri, B. Vamsi, P. Vidyullatha
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引用次数: 2

摘要

棉花是埃塞俄比亚最具商业价值的农作物之一,尽管它在叶面积上面临着各种挑战。这些限制大多是由难以用肉眼发现的疾病和害虫造成的。利用深度学习方法CNN,本研究旨在开发一个模型来提高昆虫对棉花叶病的检测。研究人员使用了常见的棉花叶片疾病和昆虫,如细菌性枯萎病、蜘蛛螨和叶螨。采用K方差验证方法对数据库进行分类,从整体上提高了CNN模型的性能。在这项研究中,大约2400个标本,每类600张图像被检索。
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 LSTM Approach on Hyperspectral Images using Keras Framework
Cotton is one of Ethiopia's most commercially important agricultural crops, although it faces a variety of challenges in the leaf area. The bulk of these limitations are caused by diseases and pests that are difficult to spot with the naked eye. Using the deep learning approach CNN, this research aimed at developing a model to improve the detection of cotton leaf disease by insects. Researchers have done this using common diseases of the cotton leaf and insects such as bacterial blight, spider mite, and leaf miner. K -variance verification method was used to classify the databases and improved the performance of the CNN model as a whole. In this study, approximately 2400 specimens of 600 images per class were present retrieved.
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