TY - GEN
T1 - Classification of organic quinoa crops using multispectral aerial imagery and machine learning techniques
AU - Flores, Andres
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Crop mapping is a vital tool for agricultural management and food security that can benefit from remote sensing data. The purpose of this research is to use machine learning (ML) techniques to classify quinoa crops from multispectral images. The spectral reflectance of five optical bands is used to develop classification models that are tested for diverse quinoa phenological phases. Deep learning methods Segnet and Unet were investigated, as well as Decision Trees, Discriminant Analysis, K nearest Neighbor, Support Vector Machines, Adaboost and Random Forest. Data was collected from quinoa crop fields in Cabana, Puno region in Peru. The multispectral images were captured using an Unmanned Aircraft System (UAS) from a height of 50 meters. Deep learning methods leave behind other approaches in the classification job, according to the results.
AB - Crop mapping is a vital tool for agricultural management and food security that can benefit from remote sensing data. The purpose of this research is to use machine learning (ML) techniques to classify quinoa crops from multispectral images. The spectral reflectance of five optical bands is used to develop classification models that are tested for diverse quinoa phenological phases. Deep learning methods Segnet and Unet were investigated, as well as Decision Trees, Discriminant Analysis, K nearest Neighbor, Support Vector Machines, Adaboost and Random Forest. Data was collected from quinoa crop fields in Cabana, Puno region in Peru. The multispectral images were captured using an Unmanned Aircraft System (UAS) from a height of 50 meters. Deep learning methods leave behind other approaches in the classification job, according to the results.
KW - Aerial images
KW - agriculture
KW - crop classification
KW - image processing
KW - machine learning
KW - quinoa crops
KW - semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85147090878&partnerID=8YFLogxK
U2 - 10.1109/ICA-ACCA56767.2022.10006196
DO - 10.1109/ICA-ACCA56767.2022.10006196
M3 - Conference contribution
AN - SCOPUS:85147090878
T3 - 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control: For the Development of Sustainable Agricultural Systems, ICA-ACCA 2022
BT - 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2022
Y2 - 24 October 2022 through 28 October 2022
ER -
