VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program

Yijun Yan, Sophia Zhao, Yuxi Fang, Yuren Liu, Zhongxin Chen, Jinchang Ren

Research output: Chapter in Book/Report/Conference proceedingConference contribution book


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.
Original languageEnglish
Title of host publicationAdvances in Brain Inspired Cognitive Systems
Subtitle of host publication10th International Conference, BICS 2019, Guangzhou, China, July 13–14, 2019, Proceedings
EditorsJinchang Ren, Amir Hussain, Huimin Zhao, Kaizhu Huang, Jiangbin Zheng, Jun Cai, Rongjun Chen, Yinyin Xiao
Place of PublicationCham, Switzerland
Number of pages10
ISBN (Electronic)9783030394318
ISBN (Print)9783030394301
Publication statusPublished - 1 Feb 2020


  • precision agriculture
  • machine learning
  • information fusion
  • regression


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