文献类型： 外文期刊
作者： Li, Zhenhai 1 ; Fan, Chengzhi 1 ; Zhao, Yu 2 ; Jin, Xiuliang 3 ; Casa, Raffaele 4 ; Huang, Wenjiang 5 ; Song, Xiaoyu 2 ; Blasch, Gerald 6 ; Yang, Guijun 2 ; Taylor, James 7 ; Li, Zhenhong 8 ;
作者机构： 1.Shandong Univ Sci & Technol, Coll Geodesy & Geomatics, Qingdao 266590, Shandong, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Key Lab Quantitat Remote Sensing, Minist Agr & Rural Affairs, Beijing 100097, Peoples R China
3.Chinese Acad Agr Sci, Inst Crop Sci, Key Lab Crop Physiol & Ecol, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China
4.Univ Tuscia, DAFNE, Via San Camillo Lellis, I-01100 Viterbo, Italy
5.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
6.Int Maize & Wheat Improvement Ctr CIMMYT, POB 5689, Addis Ababa, Ethiopia
7.Univ Montpellier, Inst Agro, ITAP, INRAE, F-34000 Montpellier, France
8.Changan Univ, Coll Geol Engn & Geomatics, Xian 710054, Shaanxi, Peoples R China
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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