A vision-based method for weeds identification through the Bayesian decision theory
A vision-based method for weeds identification through the Bayesian decision theory
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DOI：
10.1016/j.patcog.2007.07.007
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发表时间：
2008-02
期刊：
影响因子：
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通讯作者：
A. Tellaeche;X. Burgos-Artizzu;G. Pajares;A. Ribeiro
中科院分区：
文献类型：
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作者：
A. Tellaeche;X. Burgos-Artizzu;G. Pajares;A. Ribeiro
精准农业的目标之一是通过使用特定地点的杂草管理系统，最大限度地减少除草剂的使用量。本文概述了一种自动的计算机视觉为基础的方法，检测和差分喷洒的杂草在玉米作物。该方法被设计用于芽后除草剂应用，其中杂草和玉米植物显示出相似的光谱特征，并且杂草在作物的田地内出现不规则分布。该策略涉及两个过程：图像分割和决策。图像分割结合了基本的合适的图像处理技术，以便从图像中提取细胞作为低级别单元。每个单元由两个基于面积的作物和杂草之间的测量关系来描述。决策基于贝叶斯框架下的后验概率的计算来确定待喷洒的细胞。考虑到该方法嵌入的物理系统（拖拉机）的动态，在这个框架中的先验概率计算。本文的主要贡献是：（1）图像分割和决策过程的组合和（2）决策本身，利用先验知识映射为先验概率。该方法的性能示出了对一些现有的策略进行比较分析。
One of the objectives of precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops. The method is designed for post-emergence herbicide applications where weeds and corn plants display similar spectral signatures and the weeds appear irregularly distributed within the crop's field. The proposed strategy involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based measuring relationships between crop and weeds. The decision making determines the cells to be sprayed based on the computation of a posterior probability under a Bayesian framework. The a priori probability in this framework is computed taking into account the dynamic of the physical system (tractor) where the method is embedded. The main contributions of this paper are: (1) the combination of the image segmentation and decision making processes and (2) the decision making itself which exploits a previous knowledge which is mapped as the a priori probability. The performance of the method is illustrated by comparative analysis against some existing strategies.