{"title":"Key technology of crop precision sowing based on vision principle","authors":"Bing-chuan Li, Jiyun Li","doi":"10.4081/jae.2022.1453","DOIUrl":null,"url":null,"abstract":"In the process of precision planting of crops, due to many external environmental interference factors, low precision of sowing technology and large relative errors, the growth of crops is seriously affected. To solve this problem, machine vision technology is introduced to study the key technology of crop precision sowing based on vision principle. After preprocessing the crop image, the corresponding histogram is established. The segmentation threshold method is used to gray the image and determine the best threshold, so that the image has a good recognition effect. According to the growth height and color analysis of crops in the image, predict the growth of crops and realize the precision sowing of crops. The comparative experimental results show that under the application of the new sowing technology, the estimation accuracy of crop planting area is high, the recognition accuracy of planting position is also high, and the fertilization uniformity is close to the actual data, which can provide an important basis for improving the quality of crop sowing.","PeriodicalId":48507,"journal":{"name":"Journal of Agricultural Engineering","volume":"46 1","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Agricultural Engineering","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.4081/jae.2022.1453","RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
In the process of precision planting of crops, due to many external environmental interference factors, low precision of sowing technology and large relative errors, the growth of crops is seriously affected. To solve this problem, machine vision technology is introduced to study the key technology of crop precision sowing based on vision principle. After preprocessing the crop image, the corresponding histogram is established. The segmentation threshold method is used to gray the image and determine the best threshold, so that the image has a good recognition effect. According to the growth height and color analysis of crops in the image, predict the growth of crops and realize the precision sowing of crops. The comparative experimental results show that under the application of the new sowing technology, the estimation accuracy of crop planting area is high, the recognition accuracy of planting position is also high, and the fertilization uniformity is close to the actual data, which can provide an important basis for improving the quality of crop sowing.
期刊介绍:
The Journal of Agricultural Engineering (JAE) is the official journal of the Italian Society of Agricultural Engineering supported by University of Bologna, Italy. The subject matter covers a complete and interdisciplinary range of research in engineering for agriculture and biosystems.