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

Rabiya Abbasi, Pablo Martinez Rodriguez, Rafiq Ahmad*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
58 Downloads (Pure)

Abstract

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 implementation of this farming technique, a unified system is needed that can detect crop diseases and support researchers 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 constructed. Then, a disease detection system is developed that works in three phases. The first phase is a crop classification 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 disease detection system is then integrated with an ontology model through a cloud-based application. This ontology 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 decision support system that can automate aquaponics facility health monitoring and assist agricultural practitioners in decision-making processes regarding crop and disease management.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalArtificial Intelligence in Agriculture
Volume10
Early online date9 Sept 2023
DOIs
Publication statusPublished - 1 Dec 2023

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