Methodological evolution of potato yield prediction: a comprehensive review

Frontiers in Plant Science

Plant Science

Dianqiu Lyv¹  Bo Li²  Yanran Ye³  Guangcun Li³  Shuang Li³  Jiangang Liu³  Liping Jin³  Chunsong Bian³  Shaoguang Duan³  Yongxin Lin⁴ 

[1] College of Agronomy and Biotechnology, Southwest University, Chongqing, China;Seeds Development, Syngenta Jealott’s Hill International Research Centre, Bracknell, United Kingdom;State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China;State Key Laboratory of Vegetable Biobreeding, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing, China;College of Agronomy and Biotechnology, Southwest University, Chongqing, China;

关键词: yield prediction;    potato;    precision agriculture;    remote sensing;    crop growth model;   

DOI  :  10.3389/fpls.2023.1214006

 received in 2023-04-28, accepted in 2023-07-06,  发布年份 2023

来源: Frontiers

【 摘 要 】

Timely and accurate prediction of crop yield is essential for increasing crop production, estimating planting insurance, and improving trade benefits. Potato (Solanum tuberosum L.) is a staple food in many parts of the world and improving its yield is necessary to ensure food security and promote related industries. We conducted a comprehensive literature survey to demonstrate methodological evolution of predicting potato yield. Publications on predicting potato yield based on methods of remote sensing (RS), crop growth model (CGM), and yield limiting factor (LF) were reviewed. RS, especially satellite-based RS, is crucial in potato yield prediction and decision support over large farm areas. In contrast, CGM are often utilized to optimize management measures and address climate change. Currently, combined with the advantages of low cost and easy operation, unmanned aerial vehicle (UAV) RS combined with artificial intelligence (AI) show superior potential for predicting potato yield in precision management of large-scale farms. However, studies on potato yield prediction are still limited in the number of varieties and field sample size. In the future, it is critical to employ time-series data from multiple sources for a wider range of varieties and large field sample sizes. This study aims to provide a comprehensive review of the progress in potato yield prediction studies and to provide a theoretical reference for related research on potato.

【 授权许可】

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Copyright © 2023 Lin, Li, Duan, Ye, Li, Li, Lyv, Jin, Bian and Liu