Real-time growth rate and fresh weight estimation for little gem romaine lettuce in aquaponic grow beds

Abraham Reyes-Yanes, Pablo Martinez Rodriguez, Rafiq Ahmad*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

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.
Original languageEnglish
Article number105827
Number of pages10
JournalComputers and Electronics in Agriculture
Volume179
Early online date14 Oct 2020
DOIs
Publication statusPublished - 1 Dec 2020
Externally publishedYes

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