TY - JOUR
T1 - An approach of beans plant development classification using fuzzy logic
AU - Correa, Pedro
AU - Bautista, Pedro
AU - Lopez, Marlene
AU - Villaseñor, Marcos
AU - García, Carlos
AU - Rostro-González, Horacio
AU - Perez-Pinal, Francisco J.
N1 - Publisher Copyright:
© 2019 Pedro Correa et al.
PY - 2019
Y1 - 2019
N2 - In this paper, a method to monitor the growth of bean plants from images taken in their vegetative stage is presented. Through a fuzzy system and the RGB image components, the growth stages of the plant are classified to achieve a powerful tool in the precision agricultural field, which can reduce cost and increase system portability. This also serves as a reference for the growth of the plant according to its age, vigor, and healthy that could help to create the necessary environmental conditions. To carry out this research, the development of twenty bean plants was periodically monitored from their germinal to the first trifoliate leaf stage. Images were obtained with controlled background and lighting; later they were segmented by color to count the pixel's average for each case. These data were used to propose six different fuzzy systems to choose the best one within them. Finally, it was found that the artificial vision system can identify the vegetative stages of germination, emergence, primary leaves, and first trifoliate leaf.
AB - In this paper, a method to monitor the growth of bean plants from images taken in their vegetative stage is presented. Through a fuzzy system and the RGB image components, the growth stages of the plant are classified to achieve a powerful tool in the precision agricultural field, which can reduce cost and increase system portability. This also serves as a reference for the growth of the plant according to its age, vigor, and healthy that could help to create the necessary environmental conditions. To carry out this research, the development of twenty bean plants was periodically monitored from their germinal to the first trifoliate leaf stage. Images were obtained with controlled background and lighting; later they were segmented by color to count the pixel's average for each case. These data were used to propose six different fuzzy systems to choose the best one within them. Finally, it was found that the artificial vision system can identify the vegetative stages of germination, emergence, primary leaves, and first trifoliate leaf.
UR - http://www.scopus.com/inward/record.url?scp=85068024774&partnerID=8YFLogxK
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=pure_univeritat_ramon_llull&SrcAuth=WosAPI&KeyUT=WOS:000472872900001&DestLinkType=FullRecord&DestApp=WOS_CPL
U2 - 10.1155/2019/4232536
DO - 10.1155/2019/4232536
M3 - Article
AN - SCOPUS:85068024774
SN - 1687-725X
VL - 2019
JO - Journal of Sensors
JF - Journal of Sensors
M1 - 4232536
ER -