TY - JOUR
T1 - Real-time growth rate and fresh weight estimation for little gem romaine lettuce in aquaponic grow beds
AU - Reyes-Yanes, Abraham
AU - Martinez Rodriguez, Pablo
AU - Ahmad, Rafiq
N1 - Funding information:
The authors acknowledge the financial support of this work by the Council on Science and Technology (CONACYT) (File No. 2018-000039-01EXTF-00050) and Transportes Pitic Scholarship. Also, the authors acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada (NSERC) (Grant File No. ALLRP 545537-19 and RGPIN-2017-04516).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Computer vision systems’ interest in food grading has been increasing and adopted due to the non-destructive and contactless features of the process. Aquaponics technique, on the other hand, is a farming method that combines a recirculating aquaculture system and soilless hydroponics agriculture promising to be one of the answers to sustainability in the food industry. Lack of intelligent real-time approaches to monitor and track plant growth is hindering the transition of aquaponic systems towards automation and commercialization. Computer vision can promote further contributions in smart applications in aquaponics; therefore, a methodology is proposed to measure in real-time the growth rate and fresh weight of crops in multi-instance setups. The proposed system uses image-processing techniques, deep learning, and regression analysis to estimate the size of the crops as they grow using image segmentation. Then, a correlation between the size of the crops and their fresh weight is modelled. For common little gem romaine lettuce, the size of crops and fresh weight is estimated with an overall error of 30 mm (18.7%) and 0.5 g (8.3%), respectively.
AB - Computer vision systems’ interest in food grading has been increasing and adopted due to the non-destructive and contactless features of the process. Aquaponics technique, on the other hand, is a farming method that combines a recirculating aquaculture system and soilless hydroponics agriculture promising to be one of the answers to sustainability in the food industry. Lack of intelligent real-time approaches to monitor and track plant growth is hindering the transition of aquaponic systems towards automation and commercialization. Computer vision can promote further contributions in smart applications in aquaponics; therefore, a methodology is proposed to measure in real-time the growth rate and fresh weight of crops in multi-instance setups. The proposed system uses image-processing techniques, deep learning, and regression analysis to estimate the size of the crops as they grow using image segmentation. Then, a correlation between the size of the crops and their fresh weight is modelled. For common little gem romaine lettuce, the size of crops and fresh weight is estimated with an overall error of 30 mm (18.7%) and 0.5 g (8.3%), respectively.
KW - Computer vision
KW - Deep learning
KW - Growth rate
KW - Leafy crops
KW - Aquaponics
KW - Precision farming
U2 - 10.1016/j.compag.2020.105827
DO - 10.1016/j.compag.2020.105827
M3 - Article
SN - 0168-1699
VL - 179
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
M1 - 105827
ER -
