Remote sensing of quality traits in cereal and arable production systems: A review
Zhenhai Li,
Chengzhi Fan,
Yu Zhao,
Xiuliang Jin,
Raffaele Casa,
Wenjiang Huang,
Xiaoyu Song,
Gerald Blasch,
Guijun Yang,
James Taylor,
Zhenhong Li
Affiliations
Zhenhai Li
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, Shandong, China; Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Corresponding authors.
Chengzhi Fan
College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, Shandong, China
Yu Zhao
Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; Corresponding authors.
Xiuliang Jin
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture and Rural Affairs, Beijing 100081, China
Raffaele Casa
DAFNE, Università della Tuscia, Via San Camillo de Lellis, 01100 Viterbo, Italy
Wenjiang Huang
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Xiaoyu Song
Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
Gerald Blasch
International Maize and Wheat Improvement Center (CIMMYT), PO Box 5689, Addis Ababa, Ethiopia
Guijun Yang
Key Laboratory of Quantitative Remote Sensing in Ministry of Agriculture and Rural Affairs, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
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.
