Articles
SMARTOM operational group: integrated crops monitoring systems based on remote sense and hyper spectral images
Article number
1351_21
Pages
131 – 134
Language
English
Abstract
In this work, different monitoring systems are studied through the use of high-resolution remote sensing technologies for the processing tomato crop.
The study is part of the work carried out in the project executed by the SmarTom Operational Group.
The objective of this work is the use and integration of monitoring systems to improve the management and inputs optimization.
Monitoring is based on soil and crop management, using remote sensors to measure soil parameters and hyperspectral imaging technology to detect crop parameters.
This solution combines remote sensing for measuring climatic and soil parameters and obtaining specific algorithms for the automatic interpretation of hyperspectral images for the detection of nutritional problems, water stress, detection of pests and diseases signs and determination of the state of maturity.
This allows a better management of irrigation and crop optimizing the use of inputs.
From hyperspectral images and thanks to chemometrics techniques, it has been possible to identify the different components present in images, to quantify the number of fruits regarding their stage of ripeness, as well as to analyze parameters of interest in soil and leaf samples.
The results obtained suggest that the methodology developed in this work could become a reference in the fruit and vegetable sector due to its ease of measurement, immediacy and multiparametric character.
The study is part of the work carried out in the project executed by the SmarTom Operational Group.
The objective of this work is the use and integration of monitoring systems to improve the management and inputs optimization.
Monitoring is based on soil and crop management, using remote sensors to measure soil parameters and hyperspectral imaging technology to detect crop parameters.
This solution combines remote sensing for measuring climatic and soil parameters and obtaining specific algorithms for the automatic interpretation of hyperspectral images for the detection of nutritional problems, water stress, detection of pests and diseases signs and determination of the state of maturity.
This allows a better management of irrigation and crop optimizing the use of inputs.
From hyperspectral images and thanks to chemometrics techniques, it has been possible to identify the different components present in images, to quantify the number of fruits regarding their stage of ripeness, as well as to analyze parameters of interest in soil and leaf samples.
The results obtained suggest that the methodology developed in this work could become a reference in the fruit and vegetable sector due to its ease of measurement, immediacy and multiparametric character.
Publication
Authors
J. Gil, F.J. Rodríguez-Pulido, J. Seoane, J. Riola, C. Fernández, E. Ordiales, F. Núñez, L.M. Muñoz-Reja, J.I. Gutiérrez-Cabanillas, J.L. Llerena-Ruiz
Keywords
water, computer vision, hyperspectral imaging, remote sensing, crops management
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