Chao Qi

Chao Qi


Education Background

2019.09 – Present Ph.D of GradeOne, Agricultural Electrification and Automation, Nanjing Agricultural University, China

2017.09 – 2019.06 M.Sc, Agricultural Engineering, Nanjing Agricultural University, China Major courses automotive electronics, engineering testing, embedded technology, etc.

2012.09 – 2016.07 B.Sc, Electrical Engineering and Automation, Nanjing University of Information Science & Technology, China Major courses digital signal processing, analog signal processing, automatic control principle, C language, etc.



2019 Third prize of academic scholarship of Nanjing Agricultural University 2018 Second prize of academic scholarship of Nanjing Agricultural University

2016 ThirdprizeofacademicscholarshipofNanjingUniversityofInformationScience & Technology 2015 ThirdprizeofacademicscholarshipofNanjingUniversityofInformationScience & Technology 2014 ThirdprizeofacademicscholarshipofNanjingUniversityofInformationScience & Technology 2014 First prize of debate competition of Nanjing University of Information Science & Technology

2013 ThirdprizeofacademicscholarshipofNanjingUniversityofInformationScience & Technology 2013 Excellent officer of the outreach department



[1] Chao Qi, Yi Zuo, Zheqi Chen, Kunjie Chen. Research on Rice Processing Accuracy Classification Method Based on Improved VGG16 Convolution Neural Network[J]. Transactions of the Chinese Society for Agricultural Machinery. (Accepted)

[2]ChaoQi,JiaqiXu,ChaoLiu,MingqingWu,KunjieChen. AutomaticGrading Method of Chicken Carcass Based on Machine Vision and Machine Learning Technology [J]. Journal of Nanjing Agricultural University, 2019, 42 (03): 551– 558.

[3] Yuping Huang, Renfu Lu, Chao Qi, Kunjie Chen. Detection Methods of Tomato Quality by Wavelength Ratio and Near Infrared Spectroscopy [J]. Spectroscopy and Spectral Analysis, 2018,38 (08): 2362–2368.

[4]YupingHuang,RenfuLu,ChaoQi,KunjieChen. StudyonTomatoMaturity DiscriminationMethodBasedonSpatialResolutionSpectrum[J].Spectroscopy and Spectral Analysis, 2018, 38 (07): 2183–2188. 2

[5]YeChangwen,ChaoQi,LiuChao,ZhengXiaogang,WangPeng,Chenkunjie. Detection method of broiler corona state based on fast-rcnn [J/OL]. Journal of agricultural machinery: 1–10. [2019-12-27]

[6] Ye, Changwen, Khurram Yousaf, Chao Qi, Chao Liu and Kunjie Chen 2019. Broiler stunned state detection based on an improved fast region-based convolutional neural networkalgorithm. Poultry Science. 0: 1–10.

[7] Nyalalaa, Innocent 2019. Tomato volume and mass estimation using computer vision and machine learning algorithms. Food Science 263: 288–298.