数字农科院2.0

Bionic technology and deep learning in agricultural engineering: Current status and future prospects

文献类型： 外文期刊

作者： Chunlei Tu;Jinxia Li;Xingsong Wang;Shen Cheng;Jie Li

作者机构：

关键词： Agricultural Engineering;Cardiovascular and Renal Function Bionic Technology;Counter-Regulatory Arms;Deep Learning;Machine Vision;RAS

期刊名称： American Journal of Biochemistry and Biotechnology

ISSN： 1553-3468

年卷期： 2021 年 17.0 卷 2.0 期

页码：

收录情况： EI（2021版）

摘要： As one of the most important production activity of mankind, agriculture plays an important role in social development. With the development of science and technology, agricultural technology has constantly been explored and researched. By learning and imitating the characteristics of creatures in nature, bionic technology has been applied to the improvement of agricultural machinery and farm implements. In recent years, as an extension of bionic technology, machine vision and deep learning have been widely used in agricultural production. The application of bionic technology and deep learning in agricultural engineering are reviewed in this study. In traditional agricultural engineering, many bionic farming tools were developed to reduce soil resistance and multiple bionic cutting cutters were designed to improve work efficiency and save energy. Machine vision and neural networks were widely used in crop classification, sorting, phenological period recognition and navigation. Deep learning methods can promote the intelligentization of agricultural engineering and has obvious advantages in crop classification, disease and pest identification, growth status evaluation and autonomous robots. Agricultural engineering that integrates bionic technology, machine vision and deep learning will develop toward more automation and intelligence. © 2021 Chunlei Tu, Jinxia Li, Xingsong Wang, Shen Cheng and Jie Li.

分类号：

  • 相关文献

[1]Intelligent Monitoring System of Migratory Pests Based on Searchlight Trap and Machine Vision. Sun, Guojia;Liu, Shuhua;Luo, Haolun;Feng, Zelin;Yang, Baojun;Luo, Ju;Tang, Jian,Yao, Qing,Xu, Jiajun. 2022

[2]A Review of Posture Detection Methods for Pigs Using Deep Learning. Chen, Zhe,Lu, Jisheng,Wang, Haiyan. 2023

[3]YOLOv5DA: An Improved YOLOv5 Model for Posture Detection of Grouped Pigs. Wenhui Shi,Xiaopin Wang,Xuan Li,Yuhua Fu,Xiaolei Liu,Haiyan Wang. 2025

[4]Research progresses in technological innovation and integration of agricultural engineering. Zhu Ming,Zhou Xinqun,Zhai Zhifen. 2016

[5]Development of a doorframe-typed swinging seedling pick-up device for automatic field transplantation. Han, Luhua,Mao, Hanping,Hu, Jianping,Tian, Kunpeng,Tian, Kunpeng.

[6]Coordinative Management of Soil Resources and Agricultural Farmland Environment for Food Security and Sustainable Development in China. Bo Sun,Yongming Luo,Dianlin Yang,Jingsong Yang,Yuguo Zhao,Jiabao Zhang. 2023

[7]Identification of fruit and branch in natural scenes for citrus harvesting robot using machine vision and support vector machine. Qiang, Lu,Lie, Deng,Cai Jianrong,Bin, Liu,Zhang Yajing. 2014

[8]Plant Identification Based on Artificial Intelligence. Hou Chun Sheng,Zhou Ting. 2011

[9]Research on moisture content detection method during green tea processing based on machine vision and near-infrared spectroscopy technology. Zhongyuan Liu,Rentian Zhang,Chongshan Yang,Bin Hu,Xin Luo,Yang Li,Chunwang Dong. 2022

[10]Data fusion strategy for rapid prediction of moisture content during drying of black tea based on micro-NIR spectroscopy and machine vision. Sheng, Xufeng,Zan, Jiezhong,Jiang, Yongwen,Shen, Shuai,Li, Li,Yuan, Haibo. 2023

[11]Application status and challenges of machine vision in plant factory—A review. Zhiwei Tian,Wei Ma,Qichang Yang,Famin Duan. 2022

[12]The Application Status and Trends of Machine Vision in Tea Production. Zhiming Yang,Wei Ma,Jinzhu Lu,Zhiwei Tian,Kaiqian Peng. 2023

[13]Application of Machine Vision Technology in Citrus Production. Kaiqian Peng,Wei Ma,Jinzhu Lu,Zhiwei Tian,Zhiming Yang. 2023

[14]DESIGN AND IMPLEMENTATION OF THE POSITIONING AND DIRECTING PRECISION SEEDER FOR CUCURBITA FICIFOLIA SEEDS. Ding, Xinting,Chen, Tong,Wei, Yongzhe,He, Zhi,Cui, Yongjie,Yang, Qichang. 2024

[15]From Outside to Inside: The Subtle Probing of Globular Fruits and Solanaceous Vegetables Using Machine Vision and Near-Infrared Methods. Junhua Lu,Mei Zhang,Yongsong Hu,Wei Ma,Zhiwei Tian,Hongsen Liao,Jiawei Chen,Yuxin Yang. 2024

[16]Rep-ViG-Apple: A CNN-GCN Hybrid Model for Apple Detection in Complex Orchard Environments. Bo Han,Ziao Lu,Jingjing Zhang,Rolla Almodfer,Zhengting Wang,Wei Sun,Luan Dong. 2024

[17]A Machine Vision-Based Method of Impurity Detection for Rapeseed Harvesters. Xu Chen,Zhuohuai Guan,Haitong Li,Min Zhang. 2024

[18]The Application of an Intelligent Agaricus bisporus-Harvesting Device Based on FES-YOLOv5s. Hao Ma,Yulong Ding,Hongwei Cui,Jiangtao Ji,Xin Jin,Tianhang Ding,Jiaoling Wang. 2025

[19]Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology. Hanting Zou,Tianmeng Lan,Yongwen Jiang,Xiao Lan Yu,Haibo Yuan. 2024

[20]Research on SPAD Inversion of Rice Leaves at a Field Scale Based on Machine Vision and Leaf Segmentation Techniques. Bailin Yue,Yong Jin,Shangrong Wu,Jieyang Tan,Youxing Chen,Hu Zhong,Guipeng Chen,Yingbin Deng. 2025

作者其他论文 更多>>