Lin Yongxin, Li Shuang, Duan Shaoguang, Ye Yanran, Li Bo, Li Guangcun, Lyv Dianqiu, Jin Liping, Bian Chunsong, Liu Jiangang
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.
Front Plant Sci. 2023 Jul 26;14:1214006. doi: 10.3389/fpls.2023.1214006. eCollection 2023.
Timely and accurate prediction of crop yield is essential for increasing crop production, estimating planting insurance, and improving trade benefits. Potato ( 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.
及时准确地预测作物产量对于提高作物产量、估算种植保险和提高贸易效益至关重要。马铃薯（L.）是世界许多地区的主食，提高其产量对于确保粮食安全和促进相关产业发展十分必要。我们进行了全面的文献调查，以展示预测马铃薯产量方法的演变。对基于遥感（RS）、作物生长模型（CGM）和产量限制因素（LF）方法预测马铃薯产量的出版物进行了综述。RS，尤其是基于卫星的RS，在大面积农场的马铃薯产量预测和决策支持中至关重要。相比之下，CGM通常用于优化管理措施和应对气候变化。目前，结合低成本和操作简便的优势，无人机（UAV）RS与人工智能（AI）相结合在大规模农场精准管理中预测马铃薯产量显示出卓越潜力。然而，关于马铃薯产量预测的研究在品种数量和田间样本规模方面仍然有限。未来，利用来自多个来源的时间序列数据针对更广泛的品种和大田间样本规模至关重要。本研究旨在全面综述马铃薯产量预测研究的进展，并为马铃薯相关研究提供理论参考。
