TY - GEN
T1 - VIP-STB farm
T2 - 10th International Conference on Brain Inspired Cognitive Systems, BICS 2019
AU - Yan, Yijun
AU - Zhao, Sophia
AU - Fang, Yuxi
AU - Liu, Yuren
AU - Chen, Zhongxin
AU - Ren, Jinchang
N1 - Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - In this paper, we introduce a new concept in VIP-STB, a funded project through Agri-Tech in China: Newton Network+ (ATCNN), in developing feasible solutions towards scaling-up STB from village level to upper level via some generic models and systems. There are three tasks in this project, i.e. normalized difference vegetation index (NDVI) estimation, wheat density estimation and household-based small farms (HBSF) engagement. In the first task, several machine learning models have been used to evaluate the performance of NDVI estimation. In the second task, integrated software via Python and Twilio is developed to improve communication services and engagement for HBSFs, and provides technical capabilities. In the third task, crop density/population is predicted by conventional image processing techniques. The objectives and strategy for VIP-STB are described, experimental results on each task are presented, and more details on each model that has been implemented are also provided with future development guidance.
AB - In this paper, we introduce a new concept in VIP-STB, a funded project through Agri-Tech in China: Newton Network+ (ATCNN), in developing feasible solutions towards scaling-up STB from village level to upper level via some generic models and systems. There are three tasks in this project, i.e. normalized difference vegetation index (NDVI) estimation, wheat density estimation and household-based small farms (HBSF) engagement. In the first task, several machine learning models have been used to evaluate the performance of NDVI estimation. In the second task, integrated software via Python and Twilio is developed to improve communication services and engagement for HBSFs, and provides technical capabilities. In the third task, crop density/population is predicted by conventional image processing techniques. The objectives and strategy for VIP-STB are described, experimental results on each task are presented, and more details on each model that has been implemented are also provided with future development guidance.
KW - Information fusion
KW - Machine learning
KW - Precision agriculture
UR - http://www.scopus.com/inward/record.url?scp=85080904464&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39431-8_27
DO - 10.1007/978-3-030-39431-8_27
M3 - Conference contribution
AN - SCOPUS:85080904464
SN - 9783030394301
T3 - Lecture Notes in Computer Science
SP - 283
EP - 292
BT - Advances in Brain Inspired Cognitive Systems
A2 - Ren, Jinchang
A2 - Hussain, Amir
A2 - Zhao, Huimin
A2 - Cai, Jun
A2 - Chen, Rongjun
A2 - Xiao, Yinyin
A2 - Huang, Kaizhu
A2 - Zheng, Jiangbin
PB - Springer
CY - Switzerland
Y2 - 13 July 2019 through 14 July 2019
ER -