Development of neural networks for weed recognition in corn fields

Development of neural networks for weed recognition in corn fields
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玉米田杂草识别神经网络的开发

DOI：
10.13031/2013.8854
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发表时间：
2002
期刊：
Transactions of the ASABE
影响因子：
1.5
通讯作者：
H. Ramaswamy
H. Ramaswamy
中科院分区：
农林科学4区
文献类型：
--
作者：
Chun;S. Prasher;J. Landry;H. Ramaswamy

文献摘要

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本计画的主要目的是发展一个以人工神经网路为基础的杂草辨识系统 协助玉米地精准施用除草剂。数字图像是在1998年5月使用 市售数码相机。比较三种原色（红、绿色和蓝色）的强度， 图像的每个像素。当像素中的绿色强度较大时，像素的三个强度保持不变 否则，像素的三个强度被设置为零。背景物体，除了植物， 因此从图像中删除。修改图像的所得像素强度用作学习的输入 矢量量化（LVQ）人工神经网络（ANN）。人工神经网络被训练来区分玉米和杂草， 来区分杂草种类单个人工神经网络区分给定杂草和玉米的成功率 对4种杂草的鉴别率高达90%，对玉米的鉴别率高达80%。更好的成功率 可通过用于数据输入和/或诸如级联的结构改进的更精细的方案来获得。的 人工神经网络的图像处理时间短至0.48秒/幅图像，因此可用于实时数据处理 以及除草剂的施用。这种用于杂草识别的人工神经网络的发展可能在精准农业中有用， 指导特定地点的除草剂施用，最终减少除草剂的施用总量， 污染的风险。
The main objective of this project was to develop a weed recognition system based on artificial neural networks to assist in the precision application of herbicides in corn fields. Digital images were collected in May 1998 using a commercially available digital camera. The intensities of the three primary colors (red, green, and blue) were compared for each pixel of the images. The three intensities of a pixel remained unchanged when, in the pixel, the green intensity was greater than each of the other two; otherwise, the three intensities of the pixel were set to zero. Background objects, except plants, were thus removed from the images. The resulting pixel intensities of the modified images were used as the inputs for Learning Vector Quantization (LVQ) artificial neural networks (ANNs). ANNs were trained to distinguish corn from weeds, as well as to differentiate between weed species. The success rate for a single ANN in distinguishing a given weed species from corn was as high as 90%, and as high as 80% in distinguishing any of four weed species from corn. Better success rates might be obtainable with more elaborate schemes for data input and/or structural improvements such as cascading. The image–processing time for the ANNs was as short as 0.48 s per image, thus making it useful for real–time data processing and application of herbicides. The development of such ANNs for weed recognition could be useful in precision farming to guide site–specific herbicide application and ultimately reduce the total amount of herbicide applied as well as lowering the risk of pollution.