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

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.

Plant phenotyping relevance

レタスの成長率と生体重を画像処理・深層学習・回帰分析でリアルタイム推定する方法を提案し、誤差評価も行っており、表現型取得手法が中心である。

Code and data availability

公開状態または取得可能な本文経路を確認できませんでした。

No evidence-backed public reproduction asset is currently recorded.