Crop diagnostic system:A robust disease detection and management system for leafy green crops grown in an aquaponics facility  

作　　者：R.Abbasi P.Martinez R.Ahmad 

机构地区：[1]Aquaponics 4.0 Learning Factory(AllFactory),Department of Mechanical Engineering,University of Alberta,9211116 St.,Edmonton,AB T6G 2G8,Canada [2]Mechanical and Construction Engineering Department,Northumbria University,Newcastle Upon Tyne NE77YT,UK

出　　处：《Artificial Intelligence in Agriculture》2023年第4期1-12,共12页农业人工智能（英文）

基　　金：the financial support of this work from the Natural Sciences and Engineering Research Council of Canada(NSERC)(Grant File No.ALLRP 545537-19 and RGPIN-2017-04516).

摘　　要：Crops grown on aquaponics farms are susceptible to various diseases or biotic stresses during their growth cycle,just like traditional agriculture.The early detection of diseases is crucial to witnessing the efficiency and progress of the aquaponics system.Aquaponics combines recirculating aquaculture and soilless hydroponics methods and promises to ensure food security,reduce water scarcity,and eliminate carbon footprint.For the large-scale imple-mentation of this farming technique,a unified system is needed that can detect crop diseases and support re-searchers and farmers in identifying potential causes and treatments at early stages.This study proposes an automatic crop diagnostic system for detecting biotic stresses and managing diseases in four leafy green crops,lettuce,basil,spinach,and parsley,grown in an aquaponics facility.First,a dataset comprising 2640 images is con-structed.Then,a disease detection system is developed that works in three phases.The first phase is a crop clas-sification system that identifies the type of crop.The second phase is a disease identification system that determines the crop's health status.The final phase is a disease detection system that localizes and detects the diseased and healthy spots in leaves and categorizes the disease.The proposed approach has shown promising results with accuracy in each of the three phases,reaching 95.83%,94.13%,and 82.13%,respectively.The final dis-ease detection system is then integrated with an ontology model through a cloud-based application.This ontol-ogy model contains domain knowledge related to crop pathology,particularly causes and treatments of different diseases of the studied leafy green crops,which can be automatically extracted upon disease detection allowing agricultural practitioners to take precautionary measures.The proposed application finds its significance as a de-cision support system that can automate aquaponics facility health monitoring and assist agricultural practi-tioners in decision-making processes regarding crop a

关 键 词：Computer vision Deep learning Disease detection Leafy crops Aquaponics Digital farming 

分 类 号：S2[农业科学—农业工程] S43