Proposed decision support system (DSS) for Indian rice crop yield prediction

Gandhi, Niketa; Armstrong, Leisa; Petkar, Owaiz

doi:10.1109/tiar.2016.7801205

Cited by 19 publications

(14 citation statements)

References 12 publications

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“…Visualisations in viticulture enable a better vineyard monitoring, reducing costs and at the same time, generating a more transparent representation of the existent variability in the vineyard, which is valuable for the optimisation • AgroDSS [31] • AquaGIS [32] • • ATLAS [33] • • Blauth et al [34] • Byishimo et al [35] • CAMDT [36] • CropGIS [37] • • CropSAT [38] • • DIDAS [39] • DyNoFlo [40] • • Galindo et al [41] • GeoVisage [42] • Geovit [43] • GramyaVikas [44] • HydroQual [45] • Li et al [46] • LMTool [47] • • Luvisi et al [48] • mDSS [49] • SmartScape [50] • • VBoxReporting [51] Vite.net [52] • • • visualizeR [8] • ViPER [53] • Ochola et al [54] • Falcao et al [55] • LandCaRe DSS [56] • ValorE [57] • Agroland [58] • Gandhi et al [59] • • CaNaSTA [60] • eFarmer [61] • FARMERS [62] • PlanteInfo [63] • CropScape [64] • SIMAGRI [65] • FDSSFIS [66] • MOTIFS [67] • CarrotAge [68] • AgriSensor [69] • • • CognitiveInputs [70] • • Ruß et al [71] Tan et al…”

Section: Viticulturementioning

confidence: 99%

“…AgMine [28] • AgriAG [29] • • • AgriSuit [30] • AgroDSS [31] • AquaGIS [32] • Blauth et al [34] • Byishimo et al [35] • CAMDT [36] • • • CropGIS [37] • CropSAT [38] • DIDAS [39] • DyNoFlo Dairy [40] • Galindo et al [41] • • GeoVisage [42] • Geovit [43] • GramyaVikas [44] HydroQual [45] Li et al [46] • LMTool [47] • SmartScape [50] • VBoxReporting [51] • Vite.net [52] • ViPER [53] • Ochola et al [54] • • LandCaRe [56] • ValorE [57] • • Gandhi et al [59] • • • eFarmer [61] • FARMERS [62] •…”

Section: End-usersmentioning

confidence: 99%

“…We found a total of four tools ( [50,59,62,74]) that are designed to be used by policymakers. Policymakers, such as rural extension technicians, use DSSs to facilitate the evaluation of consequences of changes in agriculture, such as crop affectations in landscapes.…”

Section: Policymakersmentioning

confidence: 99%

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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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Section: Viticulturementioning

confidence: 99%

Section: End-usersmentioning

confidence: 99%

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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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“…For the agricultural domain, a system named AgroDSS was developed to bridges the gap between the system of agricultural and decision support methodology [6]. Another decision support systems application is proposed to help farmers predicting crop productivity under different particular climate [7]. Decision support system was also used to stimulate food security strategy in terms of food supply [8].…”

Section: Related Workmentioning

confidence: 99%

Decision Support System: A Prototype for Determining Rice Price Based on Its Quality Measurement

Hadiwiyanti¹,

Kusumantara²,

Anugrah³

et al. 2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

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Quality measurement of rice is an important and necessary activity before it is sold to market. By distinguishing its quality, pricing will also be suitable by the physical characteristics of rice delivered by farmers. This paper proposed a prototype of DSS application to assist intermediaries in determining the price of rice according to its quality. Fuzzy triangular numbers and AHP method is used to evaluate and assess the rank of rice quality. The evaluation criterion are whiteness, grain shape, foreign materials, moisture content and damaged grains. Pricing is based on the Indonesian domestic retail prices in 2017. Keywords-decision support system; rice quality, priceI.

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“…A recent review [11] presents several examples of DSS for agricultural applications, but these are usually addressing the farmers, farm advisers, or agronomists. They identify four tools that address policymakers [12][13][14][15], but still none of these address the PAs or IOs for crop type mapping or for mapping of affected agricultural areas by natural disasters.Regarding the crop type mapping, some recent projects focus on the exploitation of Copernicus Sentinel data. The Sen2-Agri project [16] proposes a processing chain at parcel level with Sentinel-2…”

mentioning

confidence: 99%

Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications

et al. 2020

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The successful implementation of the European Commission's Common Agricultural Policy (CAP) and the insurance coverage in case of a natural disaster requires precise and regular mapping of crop types and detailed delineation of the disasters' effects by frequent and accurate controls. Free and open access policy to Copernicus Sentinel data offers a big volume of data to the users on a consistent and complete basis. Today, the Sentinels are involved in an increasing number of agriculture applications, but their effective exploitation is still being investigated and the development of efficient tools, aligned to the user's needs, is yet to be realised. To this end, the DiAS (Disaster and Agriculture Sentinel Applications) project proposes methods for decision support in agriculture using Sentinel data for crop type mapping, as well as mapping of the extend of fire and flood effects in agricultural areas. The DiAS Decision Support System (DSS) is designed in consultation with potential users in participatory approach and aims to provide a prototype tool, which provides assistance to the responsible paying agencies and insurance organizations to make decisions on farmers' subsidies and compensations. The DiAS DSS prototype and its functionalities are presented in this paper and its use is demonstrated through example applications for two test sites in Greece. The DiAS DSS demonstrates the necessity for the development of similar tools, as this emerges from the user's requirements, and wishes to stimulate and inspire further research and development.Sustainability 2020, 12, 1233 2 of 20 filed inspections may determine the eligible areas for subsidy or compensation precisely, however they require a lot of effort and time and some factors like the adverse weather conditions, or remote areas, may cause large delays in the payments, which has an impact on the agricultural processes and development of the country. [1,2]. Lately, the control efforts involve the examination of satellite imagery, but this refers to very high-resolution commercial imagery and visual image interpretation or rare case image classification of single-date images [3]. These procedures improve the control efforts in some cases, but they have a high cost and their effectiveness relies mostly on the skills of the photo-interpreter and therefore cannot always offer timely, reliable, and robust results [4].There are several attempts for automating the mapping procedures in agriculture-related applications. For example, in terms of crop types mapping, the EU is coordinating the Monitoring Agricultural Resources (MARS) initiative [5] with the help of the Joint Research Center (JRC) in order to facilitate the coordination of the CAP. In the framework of MARS and following guidelines from the JRC, each EU member state is building a geographic information Land Parcel Identification System (LPIS) by interpreting very-high-resolution satellite images. Along with LPIS, the JRC is in charge of the image acquisition and it provides scientific and technical suppor...

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Proposed decision support system (DSS) for Indian rice crop yield prediction

Cited by 19 publications

References 12 publications

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

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

Decision Support System: A Prototype for Determining Rice Price Based on Its Quality Measurement

Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications

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