Design and Testing of an On-Line Omnidirectional Inspection and Sorting System for Soybean Seeds

Quan, Longzhe; Zhang, Tianyu; Sun, Lin; Chen, Xin; Xu, Zhitong

doi:10.13031/aea.12935

Cited by 6 publications

(2 citation statements)

References 30 publications

(19 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The machine vision-based detection technology of seed quality has become relatively mature. Using image processing technology, the authors of [ 16 ] created an online detection system for soybean seeds. The system was based on classifying surface information such as the color, texture, and shape of soybeans and achieved a detection accuracy of over 97% for cracked and healthy soybeans.…”

Section: Related Workmentioning

confidence: 99%

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

The accurate and rapid detection of cotton seed quality is crucial for safeguarding cotton cultivation. To increase the accuracy and efficiency of cotton seed detection, a deep learning model, which was called the improved ResNet50 (Impro-ResNet50), was used to detect cotton seed quality. First, the convolutional block attention module (CBAM) was embedded into the ResNet50 model to allow the model to learn both the vital channel information and spatial location information of the image, thereby enhancing the model’s feature extraction capability and robustness. The model’s fully connected layer was then modified to accommodate the cotton seed quality detection task. An improved LRelu-Softplus activation function was implemented to facilitate the rapid and straightforward quantification of the model training procedure. Transfer learning and the Adam optimization algorithm were used to train the model to reduce the number of parameters and accelerate the model’s convergence. Finally, 4419 images of cotton seeds were collected for training models under controlled conditions. Experimental results demonstrated that the Impro-ResNet50 model could achieve an average detection accuracy of 97.23% and process a single image in 0.11s. Compared with Squeeze-and-Excitation Networks (SE) and Coordination Attention (CA), the model’s feature extraction capability was superior. At the same time, compared with classical models such as AlexNet, VGG16, GoogLeNet, EfficientNet, and ResNet18, this model had superior detection accuracy and complexity balances. The results indicate that the Impro-ResNet50 model has a high detection accuracy and a short recognition time, which meet the requirements for accurate and rapid detection of cotton seed quality.

show abstract

Section: Related Workmentioning

confidence: 99%

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Automated analysis and sorting systems have been developed [1], [2], [3], [4], [5], [6], [7], [8] for mm-sized objects, such as seeds, beans, and kernels, in the agriculture and food sector. Instead of artificial neural networks (ANNs), some works favour non-ANN image processing [2], [3], [6] or signal processing with multi-spectral sensors [8]. A relative advantage of these approaches is the analysis efficiency.…”

Section: Introductionmentioning

confidence: 99%

Incremental two-network approach to develop a purity analyzer system for canola seeds

Singh¹,

Fernando²,

Dileepan³

2022

View full text Add to dashboard Cite

Given a suitable dataset, transfer learning using deep convolutional neural networks is an effective method to develop a system to detect and classify objects. Despite having models pretrained on large general-purpose datasets, the requirement to manually label an application-specific dataset remains a limiting factor in system development. We consider this wider problem in the context of the purity analysis of canola seeds, where end users wish to distinguish species of interest from contaminants in images taken with optical microscopes. We use a Detector network, trained only to detect seeds, to help label the dataset used to train an Analyzer network, capable of both seed detection and classification. We present results, over three experiments that involve 25 contaminant species, including Primary and Secondary Noxious Weed Seeds (as per the Canadian Weed Seeds Order), to validate our incremental approach. We also compare the proposed system to competing ones in a literature review.

show abstract

Real-time recognition system of soybean seed full-surface defects based on deep learning

Zhao

Quan

et al. 2021

Computers and Electronics in Agriculture

View full text Add to dashboard Cite

Design and Testing of an On-Line Omnidirectional Inspection and Sorting System for Soybean Seeds

Cited by 6 publications

References 30 publications

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

A method for detecting the quality of cotton seeds based on an improved ResNet50 model

Incremental two-network approach to develop a purity analyzer system for canola seeds

Real-time recognition system of soybean seed full-surface defects based on deep learning

Contact Info

Product

Resources

About