Real-time growth rate and fresh weight estimation for little gem romaine lettuce in aquaponic grow beds
Metadata:
type: 📥
- Cite key: reyes-yanesRealtimeGrowthRate2020
- Tags: ⭕, review, aquaponics
- Item Type: Article
- Authors: A. Reyes-Yanes, P. Martinez, R. Ahmad
- Date: 2020
- Date Added: 2021-02-05
- DOI: 10.1016/j.compag.2020.105827
zotero_url: * Local library - PDF Attachments
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 pro posed 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.