Remote sensing of quality traits in cereal and arable production systems: A review

文献类型： 外文期刊

第一作者： Li, Zhenhai

作者： Li, Zhenhai;Fan, Chengzhi;Li, Zhenhai;Zhao, Yu;Song, Xiaoyu;Yang, Guijun;Jin, Xiuliang;Casa, Raffaele;Huang, Wenjiang;Blasch, Gerald;Taylor, James;Li, Zhenhong

作者机构：

关键词： Remote sensing; Quality traits; Grain protein; Cereal

期刊名称：CROP JOURNAL （ 影响因子：6.6； 五年影响因子：6.5 ）

ISSN： 2095-5421

年卷期： 2024 年 12 卷 1 期

页码：

收录情况： SCI

摘要： Cereal is an essential source of calories and protein for the global population. Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers, grading harvest and categorised storage for enterprises, future trading prices, and policy planning. The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits. Many studies have also proposed models and methods for predicting such traits based on multi platform remote sensing data. In this paper, the key quality traits that are of interest to producers and consumers are introduced. The literature related to grain quality prediction was analyzed in detail, and a review was conducted on remote sensing platforms, commonly used methods, potential gaps, and future trends in crop quality prediction. This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data. (c) 2023 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY -NC ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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