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.
| Principais autores: | , , , , , , , , , , |
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| Formato: | Atigo biblioteca |
| Idioma: | English |
| Publicado em: |
ICS
2024
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| Assuntos: | AGRICULTURAL SCIENCES AND BIOTECHNOLOGY, Quality Traits, Grain Protein, REMOTE SENSING, QUALITY, GRAIN, PROTEINS, CEREALS, PRODUCTION SYSTEMS, Sustainable Agrifood Systems, |
| Acesso em linha: | https://hdl.handle.net/10883/22755 |
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