摘要
秸秆覆盖率是保护性耕作重要的评价指标之一。针对田间秸秆形态各异、细碎秸秆难以识别的难题,基于机器视觉技术,提出了一种基于K-means和彩色空间距离灰度化方法相结合的田间秸秆覆盖率检测算法。采用彩色空间距离方法对秸秆图像进行预处理,基于K-means算法实现秸秆和土壤背景的分类识别,使用数学形态学腐蚀和膨胀方法对识别后的图像进行处理,降低细碎秸秆对覆盖率的影响,最后计算秸秆图像的覆盖率。2022年10月,通过田间试验对北京小汤山国家精准农业研究示范基地采集的220幅玉米秸秆图像进行了算法验证。试验结果表明,对低秸秆覆盖率(0~30%)图像,识别准确率达到90%;对中等秸秆覆盖率(30%~60%)图像,识别准确率达到88%;对高秸秆覆盖率(60%以上)图像,识别准确率达到86%;整体秸秆覆盖率分等定级准确度达到98.18%。本研究设计的基于K-means和彩色空间距离灰度化方法相结合的田间秸秆覆盖率检测算法为保护性耕作评价提供了快速检测方法和手段。
Straw coverage rate is one of the most important evaluation indexes for conservation tillage.In view of the difficult problem of different shapes of straw and difficult recognition of the fine straw,a straw coverage rate detection algorithm based on K-means and color space distance gray-scale method was proposed to solve the problem of different straw shapes and difficult identification of broken straw.The color spatial distance method was used to preprocess the straw image.The classification recognition of the straw and soil background was realized based on the K-means algorithm.After the identified images were processed by using the mathematical morphology method,the coverage rate of the straw image was calculated.In 2022,the field experiment was conducted to verify the algorithm on 220 corn straw images collected from Beijing Xiaotangshan National Precision Agriculture Research Demonstration Base.The experimental results showed that the recognition accuracy of low straw coverage rate(0~30%)was 90%.And the recognition accuracy of medium straw coverage rate(30%~60%)was 88%.For the high straw coverage rate(more than 60%),the recognition accuracy reached 86%.The accuracy rate of overall identification grading reached 98.18%.The straw coverage rate detection algorithm based on K-means and color spatial distance gray provided a rapid detection method and means for conservation tillage evaluation.
作者
安晓飞
代均益
路振田
徐新刚
尹彦鑫
孟志军
AN Xiaofei;DAI Junyi;LU Zhentian;XU Xin’gang;YIN Yanxin;MENG Zhijun(Intelligent Equipment Technology Research Center,Beijing Academy of Agriculture and Forestry Sciences,Beijing 100097,China;College of Mechanical and Electrical Engineering,Xinjiang Agricultural University,Urumqi 830052,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第S02期229-234,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
科技创新2030-新一代人工智能重大项目(2021ZD0110900)
财政部和农业农村部:国家现代农业产业技术体系项目(CARS-03)
北京市农林科学院创新能力项目(KJCX20210433)
