TY - JOUR
T1 - Unmanned aerial vehicle images in the machine learning for agave detection
AU - Escobar-Flores, Jonathan Gabriel
AU - Sandoval, Sarahi
AU - Gámiz-Romero, Eduardo
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2022/9
Y1 - 2022/9
N2 - In this study, six supervised classification algorithms were compared. The algorithms were based on cluster analysis, distance, deep learning, and object-based image analysis. Our objective was to determine which of these algorithms has the highest overall accuracy in both detection and automated estimation of agave cover in a given area to help growers manage their plantations. An orthomosaic with a spatial resolution of 2.5 cm was derived from 300 images obtained with a DJI Inspire 1 unmanned aerial system. Two training classes were defined: (1) sites where the presence of agaves was identified and (2) “absence” where there were no agaves but other plants were present. The object-oriented algorithm was found to have the highest overall accuracy (0.963), followed by the support-vector machine with 0.928 accuracy and the neural network with 0.914. The algorithms with statistical criteria for classification were the least accurate: Mahalanobis distance = 0.752 accuracy and minimum distance = 0.421. We further recommend that the object-oriented algorithm be used, because in addition to having the highest overall accuracy for the image segmentation process, it yields parameters that are useful for estimating the coverage area, size, and shapes, which can aid in better selection of agave individuals for harvest.
AB - In this study, six supervised classification algorithms were compared. The algorithms were based on cluster analysis, distance, deep learning, and object-based image analysis. Our objective was to determine which of these algorithms has the highest overall accuracy in both detection and automated estimation of agave cover in a given area to help growers manage their plantations. An orthomosaic with a spatial resolution of 2.5 cm was derived from 300 images obtained with a DJI Inspire 1 unmanned aerial system. Two training classes were defined: (1) sites where the presence of agaves was identified and (2) “absence” where there were no agaves but other plants were present. The object-oriented algorithm was found to have the highest overall accuracy (0.963), followed by the support-vector machine with 0.928 accuracy and the neural network with 0.914. The algorithms with statistical criteria for classification were the least accurate: Mahalanobis distance = 0.752 accuracy and minimum distance = 0.421. We further recommend that the object-oriented algorithm be used, because in addition to having the highest overall accuracy for the image segmentation process, it yields parameters that are useful for estimating the coverage area, size, and shapes, which can aid in better selection of agave individuals for harvest.
KW - Agave crops
KW - Algorithms
KW - Drone
KW - Image segmentation
KW - OBIA
KW - Supervised classification
UR - http://www.scopus.com/inward/record.url?scp=85124077509&partnerID=8YFLogxK
U2 - 10.1007/s11356-022-18985-7
DO - 10.1007/s11356-022-18985-7
M3 - Artículo
C2 - 35112260
AN - SCOPUS:85124077509
SN - 0944-1344
JO - Environmental Science and Pollution Research
JF - Environmental Science and Pollution Research
ER -