Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/155175
Título: Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models
Autor: Silva, Luís
Conceição, Luís Alcino
Lidon, Fernando Cebola
Maçãs, Benvindo
Palavras-chave: conservative agriculture
crop nutrition
decision support systems
machine learning
nitrogen crop sensor
Food Science
Agronomy and Crop Science
Plant Science
Data: 6-Abr-2023
Citação: Silva, L., Conceição, L. A., Lidon, F. C., & Maçãs, B. (2023). Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models: A Review. Agriculture (Switzerland), 13(4), Article 835. https://doi.org/10.3390/agriculture13040835
Resumo: Nitrogen use efficiency (NUE) is a central issue to address regarding the nitrogen (N) uptake by crops, and can be improved by applying the correct dose of fertilizers at specific points in the fields according to the plants status. The N nutrition index (NNI) was developed to diagnose plant N status. However, its determination requires destructive, time-consuming measurements of plant N content (PNC) and plant dry matter (PDM). To overcome logistical and economic problems, it is necessary to assesses crop NNI rapidly and non-destructively. According to the literature which we reviewed, it, as well as PNC and PDM, can be estimated using vegetation indices obtained from remote sensing. While sensory techniques are useful for measuring PNC, crop growth models estimate crop N requirements. Research has indicated that the accuracy of the estimate is increased through the integration of remote sensing data to periodically update the model, considering the spatial variability in the plot. However, this combination of data presents some difficulties. On one hand, at the level of remote sensing is the identification of the most appropriate sensor for each situation, and on the other hand, at the level of crop growth models is the estimation of the needs of crops in the interest stages of growth. The methods used to couple remote sensing data with the needs of crops estimated by crop growth models must be very well calibrated, especially for the crop parameters and for the environment around this crop. Therefore, this paper reviews currently available information from Google Scholar and ScienceDirect to identify studies relevant to crops N nutrition status, to assess crop NNI through non-destructive methods, and to integrate the remote sensing data on crop models from which the cited articles were selected. Finally, we discuss further research on PNC determination via remote sensing and algorithms to help farmers with field application. Although some knowledge about this determination is still necessary, we can define three guidelines to aid in choosing a correct platform.
Descrição: Funding Information: In January 2020, the EU launched the Farm Sustainability Tool for Nutrients (FaST), aiming to generate fertilization recommendations based on satellite images, crop growth models, and meteorological data. Supported by the European Space Program and the EU ISA Programme, the FaST digital platform will provide resources for agriculture, environment, and sustainability of European farmers, member state paying agencies, agricultural consultants, researchers, and developers of digital solutions. It is intended to be a world-leading platform to generate and reuse solutions for agricultural sustainability and competitiveness based on spatial data (Copernicus and Galileo) and other public data or private databases. It will also support the common agricultural policy by enabling ML-based solutions applied to image recognition, as well as the use and reuse of data from the Internet of Things (IoT), public data, and user-generated data []. FaST relies on multiple data sources, either connected (online sources) or imported (static sources) into the platform. Publisher Copyright: © 2023 by the authors.
Peer review: yes
URI: http://hdl.handle.net/10362/155175
DOI: https://doi.org/10.3390/agriculture13040835
ISSN: 2077-0472
Aparece nas colecções:FCT: DCT - Artigos em revista internacional com arbitragem científica

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