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Pregled bibliografske jedinice broj: 1249191

A micro-scale approach for cropland suitability assessment of permanent crops using machine learning and a low-cost UAV


Radočaj, Dorijan; Šiljeg, Ante; Plaščak, Ivan; Marić, Ivan; Jurišić, Mladen
A micro-scale approach for cropland suitability assessment of permanent crops using machine learning and a low-cost UAV // Agronomy, 13 (2023), 2; 362, 17 doi:10.3390/agronomy13020362 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1249191 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
A micro-scale approach for cropland suitability assessment of permanent crops using machine learning and a low-cost UAV

Autori
Radočaj, Dorijan ; Šiljeg, Ante ; Plaščak, Ivan ; Marić, Ivan ; Jurišić, Mladen

Izvornik
Agronomy (2073-4395) 13 (2023), 2; 362, 17

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
unmanned aerial vehicle ; tangerine plantation ; vegetation index ; FAO land suitability ; artificial neural network ; open-source GIS software

Sažetak
This study presents a micro-scale approach for the cropland suitability assessment of permanent crops based on a low-cost unmanned aerial vehicle (UAV) equipped with a commercially available RGB sensor. The study area was divided into two subsets, with subsets A and B containing tangerine plantations planted during years 2000 and 2008, respectively. The fieldwork was performed on 27 September 2021 by using a Mavic 2 Pro UAV equipped with a commercial RGB sensor. The cropland suitability was performed in a two-step classification process, utilizing: (1) supervised classification with machine learning algorithms for creating a vegetation mask ; and (2) unsupervised classification for the suitability assessment according to the Food and Agriculture Organization of the United Nations (FAO) land suitability standard. The overall accuracy and kappa coefficients were used for the accuracy assessment. The most accurate combination of the input data and parameters was the classification using ANN with all nine input rasters, managing to utilize complimentary information regarding the study area spectral and topographic properties. The resulting suitability levels indicated positive suitability in both study subsets, with 63.1% suitable area in subset A and 59.0% in subset B. Despite that, the efficiency of agricultural production can be improved by managing crop and soil properties in the currently non-suitable class (N1), providing recommendations for farmers for further agronomic inspection. Alongside low-cost UAV, the open-source GIS software and globally accepted FAO standard are expected to further improve the availability of its application for permanent crop plantation management.

Izvorni jezik
Engleski

Znanstvena područja
Poljoprivreda (agronomija), Interdisciplinarne biotehničke znanosti, Geografija



POVEZANOST RADA


Ustanove:
Fakultet agrobiotehničkih znanosti Osijek,
Sveučilište u Zadru

Profili:

Avatar Url Mladen Jurišić (autor)

Avatar Url Ante Šiljeg (autor)

Avatar Url Ivan Marić (autor)

Avatar Url Ivan Plaščak (autor)

Avatar Url Dorijan Radočaj (autor)

Poveznice na cjeloviti tekst rada:

doi www.mdpi.com

Citiraj ovu publikaciju:

Radočaj, Dorijan; Šiljeg, Ante; Plaščak, Ivan; Marić, Ivan; Jurišić, Mladen
A micro-scale approach for cropland suitability assessment of permanent crops using machine learning and a low-cost UAV // Agronomy, 13 (2023), 2; 362, 17 doi:10.3390/agronomy13020362 (međunarodna recenzija, članak, znanstveni)
Radočaj, D., Šiljeg, A., Plaščak, I., Marić, I. & Jurišić, M. (2023) A micro-scale approach for cropland suitability assessment of permanent crops using machine learning and a low-cost UAV. Agronomy, 13 (2), 362, 17 doi:10.3390/agronomy13020362.
@article{article, author = {Rado\v{c}aj, Dorijan and \v{S}iljeg, Ante and Pla\v{s}\v{c}ak, Ivan and Mari\'{c}, Ivan and Juri\v{s}i\'{c}, Mladen}, year = {2023}, pages = {17}, DOI = {10.3390/agronomy13020362}, chapter = {362}, keywords = {unmanned aerial vehicle, tangerine plantation, vegetation index, FAO land suitability, artificial neural network, open-source GIS software}, journal = {Agronomy}, doi = {10.3390/agronomy13020362}, volume = {13}, number = {2}, issn = {2073-4395}, title = {A micro-scale approach for cropland suitability assessment of permanent crops using machine learning and a low-cost UAV}, keyword = {unmanned aerial vehicle, tangerine plantation, vegetation index, FAO land suitability, artificial neural network, open-source GIS software}, chapternumber = {362} }
@article{article, author = {Rado\v{c}aj, Dorijan and \v{S}iljeg, Ante and Pla\v{s}\v{c}ak, Ivan and Mari\'{c}, Ivan and Juri\v{s}i\'{c}, Mladen}, year = {2023}, pages = {17}, DOI = {10.3390/agronomy13020362}, chapter = {362}, keywords = {unmanned aerial vehicle, tangerine plantation, vegetation index, FAO land suitability, artificial neural network, open-source GIS software}, journal = {Agronomy}, doi = {10.3390/agronomy13020362}, volume = {13}, number = {2}, issn = {2073-4395}, title = {A micro-scale approach for cropland suitability assessment of permanent crops using machine learning and a low-cost UAV}, keyword = {unmanned aerial vehicle, tangerine plantation, vegetation index, FAO land suitability, artificial neural network, open-source GIS software}, chapternumber = {362} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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