Segmentation algorithm for overlap recognition of seedling lettuce and weeds based on SVM and image blocking

阻塞(统计) 苗木 人工智能 支持向量机 模式识别(心理学) 分割 计算机科学 图像(数学) 图像分割 计算机视觉 生物 植物 计算机网络
作者
Lei Zhang,Zhien Zhang,Chuanyu Wu,Liang Sun
出处
期刊:Computers and Electronics in Agriculture [Elsevier BV]
卷期号:201: 107284-107284 被引量:26
标识
DOI:10.1016/j.compag.2022.107284
摘要

• Introduce image block technology to equally divide the overlapping leaf images and label the image sub-blocks. • Use genetic algorithm to optimize support vector machine, and compare and analyze the recognition performance of single texture feature or texture combination of different fusion strategies, and get the optimal feature fusion strategy. • An image block reconstruction method based on the comparison of the center point and eight-neighbor label value is proposed, and this is combined with the proportion of image blocks of two labels for comprehensive judgment. For the problem of a low recognition rate and shape feature failure caused by overlapping seedlings and weeds during the development of an intelligent lettuce weeding robot, a method to identify seedling lettuce and weeds based on an image block and support vector machine (SVM) is proposed, which realizes their precise identification and boundary segmentation. The a* channel is used to grayscale the collected image. The Otsu and morphological methods are selected to extract all the green targets in the image. The connected component analysis method is applied to label the green targets with regions of interest (ROIs), and those with pixel areas larger than the area threshold are normalized to 256 × 256 pixels. The image blocking technique is introduced to separately aliquot the normalized ROI, with block sizes of 16 × 16, 32 × 32, and 64 × 64 pixels. On this basis, the image sub-blocks are manually labeled, block by block, to extract three texture features: histogram of oriented gradient (HOG), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM). With the accuracy of fivefold cross-validation as the optimization objective, a genetic algorithm (GA) is used to optimize the SVM penalty and kernel parameters of 21 groups of research objects (one block size has three texture features, which are arbitrarily combined to form seven research objects, with a total of three block sizes). We compare the recognition performance of the SVM, RF, KNN, and GA-SVM classifiers in a single feature and a combination of fusion strategies through comparative analysis. When the block size is 32 × 32 pixels, the fusion of LBP and GLCM features under the GA-SVM classifier has the highest accuracy, and the optimal SVM model for the identification of lettuce and weeds in the seedling stage is obtained. For the misidentified image sub-blocks in optimization model recognition, an image block reconstruction method based on the comparison of the center point and eight-neighbor label value is proposed, and this is combined with the proportion of image blocks of two labels for comprehensive judgment. The center point label value is reconstructed to the improve recognition accuracy. Experimental results show that the average precision, recall, and F1 score of the proposed method are 0.9473, 0.9529, and 0.9498, respectively, and those of images without overlapping leaves can all reach 1, thus providing a theoretical basis for crop recognition and segmentation.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
朴素羊完成签到 ,获得积分10
2秒前
2秒前
小马甲应助小王采纳,获得10
2秒前
俭朴羊青完成签到,获得积分10
3秒前
张张完成签到,获得积分10
5秒前
tomorrow完成签到 ,获得积分10
6秒前
糖炒栗子完成签到,获得积分10
7秒前
现代期待完成签到,获得积分10
7秒前
小黎完成签到,获得积分10
8秒前
呼呼呼完成签到,获得积分10
8秒前
无花果应助晴云采纳,获得10
8秒前
寸草的晖完成签到,获得积分10
9秒前
10秒前
10秒前
10秒前
小燕子发布了新的文献求助10
11秒前
12秒前
顺顺完成签到,获得积分10
13秒前
jiachun完成签到,获得积分10
13秒前
jiaolulu发布了新的文献求助10
13秒前
小王发布了新的文献求助10
14秒前
queen814完成签到,获得积分10
14秒前
简单发布了新的文献求助10
15秒前
一只呆果蝇完成签到,获得积分10
15秒前
Eternity完成签到,获得积分10
16秒前
研友_VZG7GZ应助落后从阳采纳,获得10
16秒前
乐观寻绿完成签到,获得积分10
17秒前
Hover完成签到,获得积分0
17秒前
莫晓岚完成签到,获得积分10
17秒前
123完成签到 ,获得积分10
18秒前
所所应助JSY采纳,获得30
18秒前
默默的立辉完成签到,获得积分10
18秒前
Yh完成签到,获得积分10
18秒前
引子完成签到,获得积分10
20秒前
机智的阿振完成签到,获得积分10
21秒前
KatzeBaliey完成签到,获得积分10
22秒前
量子星尘发布了新的文献求助10
23秒前
yar应助大饼采纳,获得10
24秒前
mammer应助一朵云采纳,获得20
24秒前
24秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038368
求助须知:如何正确求助?哪些是违规求助? 3576068
关于积分的说明 11374313
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029