Estimation of morphological traits of foliage and effective plant spacing in NFT-based aquaponics system
作者机构：Aquaponics 4.0 Learning Factory(AllFactory)Department of Mechanical EngineeringUniversity of Alberta9211116 St.EdmontonAB T6G 2G8Canada Mechanical and Construction Engineering DepartmentNorthumbria UniversityNewcastle upon Tyne NE77YTUK
出 版 物：《Artificial Intelligence in Agriculture》 (农业人工智能（英文）)
年 卷 期：2023年第9卷第3期
页 面：76-88页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：Deep learning Ontology modeling Crop phenotyping Leafy crops Aquaponics Digital farming Plant spacing
摘 要：Deep learning and computer vision techniques have gained significant attention in the agriculture sector due to their non-destructive and contactless *** techniques are also being integrated into modern farming systems,such as aquaponics,to address the challenges hindering its commercialization and large-scale *** is a farming technology that combines a recirculating aquaculture system and soilless hydroponics agriculture,that promises to address food security *** 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 *** 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 *** 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 *** common little gem romaine lettuce,the growth is estimated within 2 cm of error for both length and *** final model is deployed on a cloud-based application and integrated with an ontology model containing domain knowledge of the aquaponics *** 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 *** proposed application finds its signifi-cance as a decision support system that can pave the way for intelligent system monitoring and control.
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