TY - JOUR
T1 - Remote Monitoring of Crop Nitrogen Nutrition to Adjust Crop Models
T2 - A Review
AU - Silva, Luís
AU - Conceição, Luís Alcino
AU - Lidon, Fernando Cebola
AU - Maçãs, Benvindo
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04035%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05064%2F2020/PT#
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.
PY - 2023/4/6
Y1 - 2023/4/6
N2 - 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.
AB - 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.
KW - conservative agriculture
KW - crop nutrition
KW - decision support systems
KW - machine learning
KW - nitrogen crop sensor
UR - http://www.scopus.com/inward/record.url?scp=85153705687&partnerID=8YFLogxK
U2 - 10.3390/agriculture13040835
DO - 10.3390/agriculture13040835
M3 - Review article
AN - SCOPUS:85153705687
SN - 2077-0472
VL - 13
JO - Agriculture (Switzerland)
JF - Agriculture (Switzerland)
IS - 4
M1 - 835
ER -