Smart Aquaponics with Disease Detection

Barosa, Roysing; Hassen, Sayed Issamuddine Sayed; Nagowah, Leckraj

doi:10.1109/nextcomp.2019.8883437

Cited by 14 publications

(6 citation statements)

References 7 publications

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“…Data gathering and fusion aim to provide intelligent automation for the extract, transform, and load phases (Barosa et al, 2019). Before doing analytic calculations, the raw data collected from fish farms must be cleaned and processed.…”

Section: Role Of Intelligent Aquaculture In Disease Managementmentioning

confidence: 99%

Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges

Islam,

Ahammad,

Mohammed

2024

J World Aquaculture Soc

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Aquaculture is now the main source of seafood in human diets and is one of its fastest‐growing industries worldwide. However, the industry is facing several difficulties, including infectious diseases, the most significant limiting factor for aquaculture expansion. The impact of diseases on aquaculture growth, fecundity, mortality rates, and marketability is profound. Hence, the ability to predict disease outbreaks is crucial to overcoming these challenges. Various infectious agents such as bacteria, viruses, fungi, and parasites can cause significant losses of fish in intensive aquaculture practices. In an aquaculture environment, the high host density coupled with restricted water flow promotes pathogen spread. Early detection of disease is crucial for farmers as mortality rates can reach as high as 100% if left untreated. Therefore, new techniques and technical solutions for disease management in aquaculture are required. In this context, data analytics technologies, such as internet of things (IoT) sensors, artificial intelligence, and machine learning, allow farmers to proactively monitor their farms and detect potential disease outbreaks before they strike. Here, we highlighted the potential of machine learning algorithms in early pathogen detection and the possibilities of intelligent aquaculture in controlling disease outbreaks at the farm level. IoT is currently a popular study topic for smarter and sustainable aquaculture, as seen by the growing interest and broad overall assumptions. Therefore, this review aims to provide comprehensive information on the various aspects and challenges associated with modern technologies for controlling pathogenic microorganisms, as well as the potential benefits of using the IoT to improve fish health and welfare in aquaculture.

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Section: Role Of Intelligent Aquaculture In Disease Managementmentioning

confidence: 99%

Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges

Islam,

Ahammad,

Mohammed

2024

J World Aquaculture Soc

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“…The research in [41] uses a Local adaptive filter with 3x3 neighbourhoods to remove the digitization noise in diseased leaf images. In the research by [42], a morphological algorithm is used in conjunction with an area-filling approach to remove noises from leaf images. The morphological algorithm also identifies spot symptoms on the leaf through dilating and eroding.…”

Section: Image Pre-processingmentioning

confidence: 99%

“…Disease identification in black spot ≤ 92% Disease identification in powdery mildew ≤ 98% [42] Image enhancement is one of the pre-processing steps to improving the interpretability of information in images, in this case, providing better input for an automated chili disease identification system. The basis of doing image enhancement is to improve the visual quality of images to the next level and prepare these images to extract the maximum possible disease features [43].…”

Section: Region Filling Chili Citrus and Mandarin Leavesmentioning

confidence: 99%

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Aminuddin¹,

Joret²,

Zulkifli³

et al. 2023

EmAIT

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Chili, an important crop whosefruit is used as a spice, is significantly hampered by the existence of chili diseases.While these diseases pose a significant concern to farmers since they impair the supply of spices to the market, they can be managed and monitored to lessen their impact. Therefore, identifying chili diseases using a pertinent approach is of enormous importance. Over the years, the growth of computational approaches based on image processing has found its application in automated disease identification, leading to the availability of a reliable monitoring tool that produces promising findings for the chili. Numerous research papers on identifying chili diseases using the approaches have been published. Still, to the best knowledge of the author, there has not been a proper attempt to analyze these papers to describe the many steps of diagnosis, including pre-processing, segmentation, extraction of features, as well as identification techniques. Thus, a total of 50 researchpaperpublications on theidentificationof chilidiseases, with publication dates spanning from 2013 to 2021, arereviewed in this paper.Through the findings in this paper, it becomes feasible to comprehend the development trend for the application of computational approaches based on image processing in the identification of chili diseases, as well as the challenges and future directions that require attention from the present research community.

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“…This helps in the detailed identification and analysis of unique fish skin patterns for behavioural changes (IPI Singapore 2019). Similarly, an IoT-based smart aquaponics system that combines aquaculture with hydroponics for real-time environmental monitoring and disease detection of aquatic leaf and plants using machine technology was developed (Barosa et al 2019). By adopting the CIA, fish diseases that are detrimental and likely to cause colossal havoc will be timely identified and remedied to prevent future loss.…”

Section: Disease Detectionmentioning

confidence: 99%

Sustainable aquaculture development: a review on the roles of cloud computing, internet of things and artificial intelligence (CIA)

Mustapha

Alhassan

Jiang

et al. 2021

Reviews in Aquaculture

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Each year, there is a significant rise in demand for global food production due to population increase and a rise in demand for protein food sources. This puts pressure on capture fishery as fish is a preferred protein source worldwide. However, the more resources we put into the capture fisheries to obtain maximum catch, the faster the fisheries stock becomes depleted. The best option left to produce enough fish to meet demand is relying on advanced aquaculture. Unfortunately, the impact of technological advancement in the aquaculture sector is not profound compared to the agricultural and manufacturing sectors. The advent of Cloud computing, Internet of Things, and Artificial Intelligence (CIA) has expanded numerous possibilities for applying and integrating information technology in all works of life. This article reviews the emergence of research development on CIA and the potential to revolutionize the aquaculture industry. The use of CIA techniques and tools such as drones, nano and micro-sensors, bionic robots, remote cameras, intelligent sorting, energy-saving processing equipment, statistical modules, and algorithms will reduce human intervention and increase aquaculture productivity. Also, the application of CIA in the aquaculture value chain to ensure effectiveness in traceability, feeding, disease detection, growth prediction, environmental monitoring, market information, and others is key to increasing aquaculture productivity and sustainability. Therefore, the future of aquaculture operations with less human labour, effective maintenance, and resource utilization largely depend on innovative technologies. Here, we outlined the need for adopting innovative technologies and the limiting factors that hinge on CIA adoption in the aquaculture industry.

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Smart Aquaponics with Disease Detection

Cited by 14 publications

References 7 publications

Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges

Cutting‐edge technologies for detecting and controlling fish diseases: Current status, outlook, and challenges

Computational Approaches Based On Image Processingfor Automated Disease IdentificationOn Chili Leaf Images: A Review

Sustainable aquaculture development: a review on the roles of cloud computing, internet of things and artificial intelligence (CIA)

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