ParallelFarm: An AI-Enabled Sustainable Farming Management System for Carbon Neutrality

Gaoshan Bi, Yu Zhang, Qing Xue, Yang Li, Zhipeng Yuan, Kang Liu, Tong Liu, Po Yang

Abstract: Promoting sustainable agriculture plays a crucial role in reducing greenhouse gas (GHG) emissions, lowering the carbon footprint, and improving farm resilience. Three challenges must be overcome to achieve sustainable agriculture management. Firstly, there is a lack of reliable and sustainable fertiliser solutions to improve fertiliser use efficiency, reduce GHG emissions while maintaining crop production. In addition, how to cost-effectively quantify the response of soil carbon and GHG fluxes to different fertilisation practices. Thirdly, there is a requirement to integrate multi-source farming data and AI models into a farm management information system (FMIS) to support intelligent decisions for farmers. To address these challenges, we developed the ParellelFarm, an AI-enabled sustainable farming management system that integrates multi-source farming data and AI-driven fertiliser and soil carbon models into a multi-tenant cloud platform, to support sustainable farming. It also provides remote field visualisation and management as well as instant messaging via web and mobile clients, supporting fast and accurate labour allocation with fewer resources. It is a potential solution for a cost-effective, highly productive and sustainable modern net-zero farm.