Design and Experiment of Recognition System for Coated Red Clover Seeds Based on Machine Vision

ZHANG, Xiwen; Hou, Zhanjun; Xuan, Chuanzhong

doi:10.35633/inmateh-66-06

Cited by 11 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has a complete image processing function library, rich and powerful functions, and can process images efficiently and quickly (Hou Z.F. et al, 2022;Zhang, X.W. et al, 2022).…”

Section: Image Acquisition Systemmentioning

confidence: 99%

Design and Experiment of Film Laying Quality Monitoring System for Cotton Precision Planter

BAI,

YUAN,

XING

et al. 2023

INMATEH

View full text Add to dashboard Cite

To realize real-time monitoring of film laying process of cotton precision planter and improve intelligent level of cotton precision planter, based on advanced morphological filtering method and graphical programming of Labview software, a film laying quality monitoring system of cotton precision planter is designed. Using the Vision Assistant visual assistant, the system uses a color extraction function to convert colors to grayscale images. It uses LOOKup Table function and FFT filter function to perform grayscale transformation, binarization and advanced morphological filtering on it respectively. It then uses basic morphology to acquire various components in the plastic film image. It realizes the monitoring of parameters such as the width of the daylighting surface, the side length or seam length of the mechanical damaged part, and the width of the film edge covering soil. The performance test results of the film laying quality monitoring system showed that the system worked stably and reliably, the average monitoring accuracy of the width of the lighting surface and the width of the film edge covering soil reached more than 95%, and the average monitoring accuracy of the side length or the length of the seam at the mechanical damage part reached more than 88%. It solved the problems of difficulty in recognizing the similarity between the plastic film and the background interferer (soil, etc.) and could accurately detect the quality of the cotton film in real time. It effectively improved the operation quality and working efficiency of the cotton precision planter and met the practical requirements of film laying monitoring.

show abstract

“…It has a complete image processing function library, rich and powerful functions, and can process images efficiently and quickly (Hou Z.F. et al, 2022;Zhang, X.W. et al, 2022).…”

Section: Image Acquisition Systemmentioning

confidence: 99%

Design and Experiment of Film Laying Quality Monitoring System for Cotton Precision Planter

BAI,

YUAN,

XING

et al. 2023

INMATEH

View full text Add to dashboard Cite

show abstract

“…You Only Look Once (YOLO), with its unique architecture and modules, has demonstrated superior performance across numerous recognition tasks [27][28][29] . With the evolution of the YOLO series algorithms, YOLOv5 has gained increasing attention.…”

Section: Introductionmentioning

confidence: 99%

Recognition model for coated red clover seeds using YOLOv5s optimized with an attention module

Zhang,

Xuan,

Hou

2023

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

The non-destructive recognition of coated seeds is crucial for advancing studies in coating theory. Currently, the recognition of coated seeds heavily relies on manual visual inspection and machine vision detection. However, these methods pose challenges such as high misclassification rates, low recognition efficiency, and elevated labor intensity. In response to the aforementioned challenges, this study leveraged deep learning techniques to develop a coated seed recognition model named YOLO-Coated Seeds Recognition (YOLO-CSR), aiming to address the challenges posed by coated seed recognition tasks. The experiment of this study mainly includes the following steps: First, a seed coating machine was set up to coat red clover seeds, resulting in three types of coated red clover seeds. Subsequently, by collecting images of the three types of coated seeds, a coated seed image dataset was further constructed. Then, the YOLOv5s was built, incorporating the Convolutional Block Attention Module (CBAM) into the model's backbone to enhance its ability to learn features of coated seeds. Finally, the training results of YOLO-CSR were compared with those of other classical recognition models. The experimental results showed that YOLO-CSR achieved the best recognition performance on the self-built coated seed image dataset. The average precision (AP) for recognizing the three types of coated seeds reached 98.43%, 97.91%, and 97.26%, with a mean average

show abstract

Design and experiment of online cottonseed quality sorting device

Wang

Zhang

et al. 2023

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Design and Experiment of Recognition System for Coated Red Clover Seeds Based on Machine Vision

Cited by 11 publications

References 4 publications

Design and Experiment of Film Laying Quality Monitoring System for Cotton Precision Planter

Design and Experiment of Film Laying Quality Monitoring System for Cotton Precision Planter

Recognition model for coated red clover seeds using YOLOv5s optimized with an attention module

Design and experiment of online cottonseed quality sorting device

Contact Info

Product

Resources

About