Digital Platform for Data Driven Aquaculture Farm Management

Piplani, Divya; Singh, Dineshkumar; Srinivasan, Karthik; Ramesh, Nikam Vinayak; Kumar, Avinash; kumar, Viswa

doi:10.1145/2835966.2836277

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…As figure 5 shows one of the main problems faced by the farmers is the monitoring of water quality parameters (#T8). Water quality is an important aspect of aquaculture harvest and if any of the parameters are not at the optimum level may affect the animal health badly and can cause loss to the farmers (Piplani et al, 2015). In many of the aquaculture harvesting practiced in the developing countries, the farmers monitor these parameters manually and perform lab tests weekly.…”

Section: Resultsmentioning

confidence: 99%

33rd Bled eConference – Enabling Technology for a Sustainable Society: June 28 – 29, 2020, Online Conference Proceedings

2020

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Section: Resultsmentioning

confidence: 99%

33rd Bled eConference – Enabling Technology for a Sustainable Society: June 28 – 29, 2020, Online Conference Proceedings

2020

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“…A data‐driven android application known as ‘mKRISHI ® ‐AQUA’ for aquaculture farm management was proposed and developed by (Piplani et al . 2015) to help speed up farm operations in India. Their application supported data collection, processing and presentation through steps from registration of farms, creation, management, and report of pond data.…”

Section: Trends In Ciamentioning

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

106

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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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“…• CarrotAge [68] • AgriSensor [69] • CognitiveInputs [70] • • Ruß et al [71] • Tan et al [23] • • INT-VIS [72] • Piplani et al [73] • HyperTree [74] • • Munro et al [76] Mudisshu et al [83] • Zheng et al [85] •…”

Section: End-usersmentioning

confidence: 99%

“…Similarly for the tools that are designed for mobile devices, we found only one [48] that is currently available for end-users, which again supports the argument that more tools should consider support for multiple devices for flexible decision-making [23]. • GeoVisage [42] • Geovit [43] • • GramyaVikas [44] • HydroQual [45] • • Li et al [46] • LMTool [47] • • Luvisi et al [48] • • eFarmer [61] • FARMERS [62] • PlanteInfo [63] • • CropScape [64] • SIMAGRI [65] • • Zhong-Xiao [78] • Fegraus et al [79] • • • LandEx [80] • RF-CLASS [81] • FluxDataONE [82] • [73] • • HyperTree [74] • iGreen [75] • Munro et al [76] • • DEVA [77] • • Zhong-Xiao [78] • • Fegraus et al [79] •…”

Section: Tool Type Platform and Development Statusmentioning

confidence: 99%

A review of visualisations in agricultural decision support systems: An HCI perspective

Gutiérrez

Htun

Schlenz³

et al. 2019

Computers and Electronics in Agriculture

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Decision Support Systems (DSSs) are used in precision agriculture to provide feedback to a variety of stakeholders, including farmers, advisers, researchers and policymakers. However, increments in the amount of data might lead to data quality issues, and as these applications scale into big, real-time monitoring systems the problem gets even more challenging. Visualisation is a powerful technique used in these systems that provides an indispensable step in assisting end-users to understand and interpret the data. In this paper, we present a systematic review to synthesise literature related to the use of visualisation techniques in the domain of agriculture. The search identified 61 eligible articles, from which we established end-users, visualisation techniques and data collection methods across different application domains. We found visualisation techniques used in various areas of agriculture, including viticulture, dairy farming, wheat production and irrigation management. Our results show that the majority of DSSs utilise maps, together with satellite imagery, as the central visualisation. Also, we observed that there is an excellent opportunity for dashboards to enable end-users with better interaction support to understand the uncertainty of data. Based on this analysis, we provide design guidelines towards the implementation of more interactive and visual DSSs.

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Digital Platform for Data Driven Aquaculture Farm Management

Cited by 7 publications

References 2 publications

33rd Bled eConference – Enabling Technology for a Sustainable Society: June 28 – 29, 2020, Online Conference Proceedings

33rd Bled eConference – Enabling Technology for a Sustainable Society: June 28 – 29, 2020, Online Conference Proceedings

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

A review of visualisations in agricultural decision support systems: An HCI perspective

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