{"title":"谷物和耕地生产系统品质性状的遥感研究进展","authors":"Zhenhai Li, Chengzhi Fan, Yu Zhao, Xiuliang Jin, Raffaele Casa, Wenjiang Huang, Xiaoyu Song, Gerald Blasch, Guijun Yang, James Taylor, Zhenhong Li","doi":"10.1016/j.cj.2023.10.005","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":10790,"journal":{"name":"Crop Journal","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remote sensing of quality traits in cereal and arable production systems: A review\",\"authors\":\"Zhenhai Li, Chengzhi Fan, Yu Zhao, Xiuliang Jin, Raffaele Casa, Wenjiang Huang, Xiaoyu Song, Gerald Blasch, Guijun Yang, James Taylor, Zhenhong Li\",\"doi\":\"10.1016/j.cj.2023.10.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":10790,\"journal\":{\"name\":\"Crop Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2023-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Crop Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.cj.2023.10.005\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AGRONOMY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Crop Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.cj.2023.10.005","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
Remote sensing of quality traits in cereal and arable production systems: A review
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.
Crop JournalAgricultural and Biological Sciences-Agronomy and Crop Science
CiteScore
9.90
自引率
3.00%
发文量
638
审稿时长
41 days
期刊介绍:
The major aims of The Crop Journal are to report recent progresses in crop sciences including crop genetics, breeding, agronomy, crop physiology, germplasm resources, grain chemistry, grain storage and processing, crop management practices, crop biotechnology, and biomathematics.
The regular columns of the journal are Original Research Articles, Reviews, and Research Notes. The strict peer-review procedure will guarantee the academic level and raise the reputation of the journal. The readership of the journal is for crop science researchers, students of agricultural colleges and universities, and persons with similar academic levels.