Estimation of morphological traits of foliage and effective plant spacing in NFT-based aquaponics system

Abstract

Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless features. These techniques are also being integrated into modern farming systems, such as aquaponics, to address the challenges hindering its commercialization and large-scale implementation. Aquaponics is a farming technology that combines a recirculating aquaculture system and soilless hydroponics agriculture, that promises to address food security issues. To complement the current research efforts, a methodology is proposed to automatically measure the morphological traits of crops such as width, length and area and estimate the effective plant spacing between grow channels. Plant spacing is one of the key design parameters that are dependent on crop type and its morphological traits and hence needs to be monitored to ensure high crop yield and quality which can be impacted due to foliage occlusion or overlapping as the crop grows. The proposed approach uses Mask-RCNN to estimate the size of the crops and a mathematical model to determine plant spacing for a self-adaptive aquaponics farm. For common little gem romaine lettuce, the growth is estimated within 2 cm of error for both length and width. The final model is deployed on a cloud-based application and integrated with an ontology model containing domain knowledge of the aquaponics system. The relevant knowledge about crop characteristics and optimal plant spacing is extracted from ontology and compared with results obtained from the final model to suggest further actions. The proposed application finds its significance as a decision support system that can pave the way for intelligent system monitoring and control.

Keywords

• Deep learning
• Ontology modeling
• Crop phenotyping
• Leafy crops
• Aquaponics
• Digital farming