@article {10.3844/ajbbsp.2022.141.154,
article_type = {journal},
title = {Multi-Feature Recognition of Healthy Vegetable Seedlings Based on Machine Vision Technology},
author = {Chen, Kaikang and Fu, Yongkun and Zheng, Yongjun and Zhao, Bo and Yuan, Yanwei and Zhou, Liming and Jin, Xin},
volume = {18},
number = {2},
year = {2022},
month = {Apr},
pages = {141-154},
doi = {10.3844/ajbbsp.2022.141.154},
url = {https://thescipub.com/abstract/ajbbsp.2022.141.154},
abstract = {The quality of potted seedlings has an important influence on the yield of vegetables during seedling raising and transplanting. The inconsistency of potted seedlings after transplanting is the main factor causing the decline in vegetable quality and yield. To eliminate or reduce this influence, the health test of potted vegetable seedlings before transplanting is particularly important to ensure crop yield. In this study, an image recognition technology based on machine vision is proposed. It is a multi-feature recognition method for the non-destructive detection of healthy vegetable seedlings. The color of the pot seedling image is enhanced by the industrial control computer system and the self-written image recognition algorithm (hereinafter referred to as SIXA algorithm). The image segmentation and denoising are realized by the ultra-green threshold segmentation method and 3D Block Matched filtering (BM3D) algorithm.  Information about the color and leaf area features of vegetable pot seedlings was collected. The criteria for healthy vegetable pot seedlings are confirmed and analyzed. Among them, the color feature thresholds of healthy vegetable pot seedlings in this study were set as R≥60.7; G≥119.4; B≥1.9, and the leaf area feature thresholds were set as F≥0.15. This is to reduce the limitation of identifying healthy vegetable potted seedlings based on single information and establish a multi-feature identification method for healthy vegetable potted seedlings, aiming to improve the accuracy of identifying healthy vegetable potted seedlings. The experimental verification shows that the overall recognition rate of the experimental platform is as high as 96.67%, which meets the experimental expectations.},
journal = {American Journal of Biochemistry and Biotechnology},
publisher = {Science Publications}
}