基于深度神经网络的多类干豆分类

M. Hasan, Muhammad Usama Islam, M. Sadeq
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引用次数: 4

摘要

技术爆炸为农业的迅猛发展铺平了道路,从而通过机器学习、物联网和农业机械系统的帮助,提高了农作物的产量。在我们的研究工作中,我们研究了各种类型的干豆,然后采用基于深度神经网络的方法对豆类进行自动分类。结果表明,在7个干豆品种的数据集上,我们的方法准确率为93.44%,F-1得分为94.57%。与传统的机器学习方法相比,我们的结果表现得更好,这有助于我们在农业机器学习领域设计进一步的研究范围。
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 Neural Network for Multi-class Dry Beans Classification
The technological explosion has paved the way for agriculture to flourish exponentially thus contributing to better yield of crops through the aid of machine learning, the Internet of things, mechanical systems in agriculture. In our research work, we have investigated various types of dry beans followed by a deep neural network based approach to classify the beans automatically. The results shows that our approach had an accuracy of 93.44%, and an F-1 score of 94.57%, with the dataset that consisted of 7 varieties of dry beans. Our results, which performed substantially better in comparison to traditional machine learning approaches aided us to devise further research scopes in the field of agricultural machine learning.
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