4.1 Article Data Paper

A dataset of the quality of soybean harvested by mechanization for deep-learning-based monitoring and analysis

Related references

Note: Only part of the references are listed.
Article Agriculture, Multidisciplinary

Rice grains and grain impurity segmentation method based on a deep learning algorithm-NAM-EfficientNetv2

Qinghua Liu et al.

Summary: In this study, a lightweight fully convolutional segmentation algorithm based on NAM EfficientNetV2 was proposed to improve the detection accuracy and processing speed in mobile terminal equipment. By introducing a standardized NAM attention mechanism and utilizing multi-scale feature fusion and fully convolutional pixel segmentation technology, rice grain and impurities can be effectively segmented.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2023)

Letter Agricultural Engineering

ONLINE DETECTION SYSTEM FOR CRUSHED RATE AND IMPURITY RATE OF MECHANIZED SOYBEAN BASED ON DEEPLABV3+

Man Chen et al.

INMATEH-AGRICULTURAL ENGINEERING (2023)

Article Chemistry, Analytical

Online Detection System for Wheat Machine Harvesting Impurity Rate Based on DeepLabV3+

Man Chen et al.

Summary: In this study, a vision system based on deep learning was designed to realize online detection of wheat impurity rate in mechanized harvesting. The optimal DeepLabV3+ model was determined and an online detection method based on image information was constructed. Test results showed high recognition and segmentation performance of the model. This study provides a real-time method for measuring impurity rate in wheat mechanized harvesting.

SENSORS (2022)

Article Education, Scientific Disciplines

Semantic segmentation-based mechanized harvesting soybean quality detection

Chengqian Jin et al.

Summary: This study proposes an improved U-Net method for intelligent quality detection of mechanically harvested soybeans based on machine vision. It accurately identifies intact soybean grains, crushing soybean grains, and impurities, allowing real-time quality detection. The test results show that the comprehensive evaluation index values of the improved U-Net segmentation algorithm are significantly improved compared to the traditional model.

SCIENCE PROGRESS (2022)

Article Agriculture, Multidisciplinary

Machine vision based soybean quality evaluation

Md Abdul Momin et al.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2017)