Jiong Lin , Xue Bai , Mengen Yuan , Dong Wang , Shuqin Yang , Jifeng Ning
{"title":"基于苗床平面对准和高效点线匹配的温室草莓苗床图像拼接方法","authors":"Jiong Lin , Xue Bai , Mengen Yuan , Dong Wang , Shuqin Yang , Jifeng Ning","doi":"10.1016/j.compag.2025.110416","DOIUrl":null,"url":null,"abstract":"<div><div>In the context of factory-based cultivation of strawberry seedlings in greenhouses, acquiring panoramic images of the seedbed is essential for monitoring the overall growth of the strawberry seedlings, including assessing the uniformity of growth and detecting the presence of pests and diseases. This paper presents a novel greenhouse seedbed image stitching method based on seedbed plane alignment and efficient point-line matching, using a rail-based inspection device mounted above the seedbed to capture sequential images covering the seedbed area, in order to obtain high-quality panoramic images of the strawberry seedlings. First, since the strawberry seedlings are located within the seedbed area, the Depth-Anything model is utilized to extract the seedbed plane, allowing the registration algorithm to focus on the precise alignment of the seedbed region. Secondly, to fully leverage the geometric structure in the strawberry seedbed images, a local registration method GlueStick based on point-line matching is applied to match feature points between overlapping seedbed images, significantly reducing the number of feature points while effectively enhancing matching accuracy. Finally, exploiting the equidistant imaging characteristics of the rail-based imaging device, a homography matrix optimization method is proposed, effectively mitigating the impact of a small number of inaccurate local matches on the global stitching performance. Comprehensive experiments, encompassing both subjective (qualitative scoring) and objective (RMSE, Image Distortion Degree) evaluations, conducted on the constructed strawberry seedbed image dataset, demonstrate that the proposed method achieves precise alignment of the seedbed and effectively preserves the overall naturalness, outperforming representative image stitching methods. The proposed method delivers high-quality panoramic images for seedbed monitoring, offering substantial support for precise monitoring of greenhouse crops, and provides valuable references for panoramic stitching methods of other greenhouse crops.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110416"},"PeriodicalIF":7.7000,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A greenhouse strawberry seedbed image stitching method based on seedbed plane alignment and efficient point-line matching\",\"authors\":\"Jiong Lin , Xue Bai , Mengen Yuan , Dong Wang , Shuqin Yang , Jifeng Ning\",\"doi\":\"10.1016/j.compag.2025.110416\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In the context of factory-based cultivation of strawberry seedlings in greenhouses, acquiring panoramic images of the seedbed is essential for monitoring the overall growth of the strawberry seedlings, including assessing the uniformity of growth and detecting the presence of pests and diseases. This paper presents a novel greenhouse seedbed image stitching method based on seedbed plane alignment and efficient point-line matching, using a rail-based inspection device mounted above the seedbed to capture sequential images covering the seedbed area, in order to obtain high-quality panoramic images of the strawberry seedlings. First, since the strawberry seedlings are located within the seedbed area, the Depth-Anything model is utilized to extract the seedbed plane, allowing the registration algorithm to focus on the precise alignment of the seedbed region. Secondly, to fully leverage the geometric structure in the strawberry seedbed images, a local registration method GlueStick based on point-line matching is applied to match feature points between overlapping seedbed images, significantly reducing the number of feature points while effectively enhancing matching accuracy. Finally, exploiting the equidistant imaging characteristics of the rail-based imaging device, a homography matrix optimization method is proposed, effectively mitigating the impact of a small number of inaccurate local matches on the global stitching performance. Comprehensive experiments, encompassing both subjective (qualitative scoring) and objective (RMSE, Image Distortion Degree) evaluations, conducted on the constructed strawberry seedbed image dataset, demonstrate that the proposed method achieves precise alignment of the seedbed and effectively preserves the overall naturalness, outperforming representative image stitching methods. The proposed method delivers high-quality panoramic images for seedbed monitoring, offering substantial support for precise monitoring of greenhouse crops, and provides valuable references for panoramic stitching methods of other greenhouse crops.</div></div>\",\"PeriodicalId\":50627,\"journal\":{\"name\":\"Computers and Electronics in Agriculture\",\"volume\":\"236 \",\"pages\":\"Article 110416\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2025-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers and Electronics in Agriculture\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0168169925005228\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169925005228","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
A greenhouse strawberry seedbed image stitching method based on seedbed plane alignment and efficient point-line matching
In the context of factory-based cultivation of strawberry seedlings in greenhouses, acquiring panoramic images of the seedbed is essential for monitoring the overall growth of the strawberry seedlings, including assessing the uniformity of growth and detecting the presence of pests and diseases. This paper presents a novel greenhouse seedbed image stitching method based on seedbed plane alignment and efficient point-line matching, using a rail-based inspection device mounted above the seedbed to capture sequential images covering the seedbed area, in order to obtain high-quality panoramic images of the strawberry seedlings. First, since the strawberry seedlings are located within the seedbed area, the Depth-Anything model is utilized to extract the seedbed plane, allowing the registration algorithm to focus on the precise alignment of the seedbed region. Secondly, to fully leverage the geometric structure in the strawberry seedbed images, a local registration method GlueStick based on point-line matching is applied to match feature points between overlapping seedbed images, significantly reducing the number of feature points while effectively enhancing matching accuracy. Finally, exploiting the equidistant imaging characteristics of the rail-based imaging device, a homography matrix optimization method is proposed, effectively mitigating the impact of a small number of inaccurate local matches on the global stitching performance. Comprehensive experiments, encompassing both subjective (qualitative scoring) and objective (RMSE, Image Distortion Degree) evaluations, conducted on the constructed strawberry seedbed image dataset, demonstrate that the proposed method achieves precise alignment of the seedbed and effectively preserves the overall naturalness, outperforming representative image stitching methods. The proposed method delivers high-quality panoramic images for seedbed monitoring, offering substantial support for precise monitoring of greenhouse crops, and provides valuable references for panoramic stitching methods of other greenhouse crops.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.