CNN-based Approaches for Weed Detection

A. Tlebaldinova, M. Karmenova, E. Ponkina, A. Bondarovich
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引用次数: 1

Abstract

Seasonal monitoring of cultivated land for weeds with automatic classification of crops and weeds provides farmers with a significant return on investment, as it saves fertilizer, herbicides and time. This paper discusses the problem of semantic image segmentation using Convolutional Neural Network (CNN) architectures. In the implementation of the CNN architectures RGB format images are used. A comparative analysis of the application of the proposed approaches and their estimated performance indicators obtained as a result of experimental studies is carried out.
基于cnn的杂草检测方法
对耕地杂草进行季节性监测,并对作物和杂草进行自动分类,为农民提供了可观的投资回报,因为它节省了化肥、除草剂和时间。本文讨论了基于卷积神经网络(CNN)架构的语义图像分割问题。在CNN架构的实现中,使用了RGB格式的图像。对所提出的方法的应用及其通过实验研究获得的估计性能指标进行了比较分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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