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

Abstract

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.