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

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 and disease management.

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

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 signifi-cance as a decision support system that can pave the way for intelligent system monitoring and control.