用神经网络识别杂草中的豆科植物

M. Aparicio, Tetyana Baydyk, E. Kussul, G. Velasco, Carlos Vera
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引用次数: 0

摘要

提出了一种随机子空间分类器(RSC)神经网络在杂草中识别豆类植物的方法。RSC神经分类器基于单层训练连接的多层感知器,可以实现高速训练。该分类器的输入可以考虑多种模式,例如微轮廓的亮度直方图、对比度直方图和方向直方图。RSC神经分类器已被开发用于识别,并应用于不同的任务,如微观力学,组织识别和金属纹理识别。RSC应用程序可以帮助实现农业工业过程的自动化。为此,可以使用基于神经网络的计算机视觉。
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Recognition of Bean Plants in Weeds Using Neural Networks
The implementation of a random subspace classifier (RSC) neural network for the recognition of bean plants in weeds was proposed. The RSC neural classifier is based on the multilayer perceptron with a single layer of training connections, allowing a high speed training. The input of this classifier can be considered in various modes, for example, histograms of brightness, contrast, and orientation of micro contours. The RSC neural classifier has been developed for recognition and is applied to different tasks such as micromechanics, tissue recognition, and recognition of metallic textures. The RSC application can help automatize the industrial processes in agriculture. For this purpose, the computer vision based on neural networks can be used.
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