AgDataBox API – Integration of data and software in precision agriculture

Bazzi, Claudio; Jasse, Ermínio Pita; Magalhães, Paulo Sérgio Graziano; Michelon, Gabriela Karoline; Souza, Eduardo Godoy de; Schenatto, Kelyn; Sobjak, Ricardo

doi:10.1016/j.softx.2019.100327

Cited by 16 publications

(9 citation statements)

References 7 publications

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“…The research community also introduced some attempts of creating data integration. The AGDATABOX [2] is an API that aims to facilitate the input and output of the agronomic data. The idea is to create a unique source of true for this data.…”

Section: Precision Agriculture and Data Integrationmentioning

confidence: 99%

Data Integration for Precision Agriculture - Challenges and Opportunities for the Database community

Santana¹

2022

Anais Da XVII Escola Regional De Banco De Dados (ERBD 2022)

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The last years the precision agriculture transformed one of the most ancient activities into a humongous source of data. This can happen by means of sensors that monitor continuously the physical environment (e.g., satellite imagery, high technology machinery, micro weather stations) producing large quantities of data in an unprecedented pace. Although there are many papers describing how to use this data (e.g., in modern Big Data systems, as the input of Machine Learning pipelines), today this is a virtually impossible task without a huge effort conciliation and integration. There are many research opportunities that emerge from this scenario, for instance data accessibility through integration methods, new tools (e.g., visualization, ETL tools), and novel datasets and benchmarks. This is specially interesting in the Brazilian context, our country have more than 800 thousand of hectares of arable land and the agribusiness represents almost 30% of our Gross Domestic Product (GDP). This paper presents the experience of four years of working at Leaf Agriculture, the goal is to list the challenges and opportunities for data integration in the precision agriculture.

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Section: Precision Agriculture and Data Integrationmentioning

confidence: 99%

Data Integration for Precision Agriculture - Challenges and Opportunities for the Database community

Santana¹

2022

Anais Da XVII Escola Regional De Banco De Dados (ERBD 2022)

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“…Multi-process applications: These applications are at the intersection of different domains and smart agriculture processes. For instance, AgDataBox [12] allows farmers in rural areas to collect and send environmental data for storage remotely. The application can also serve as a support tool for recording rainfall and scheduling tasks.…”

Section: -Tier Androidmentioning

confidence: 99%

“…Candidate base-maps are processed in data centres before they can be visualised in the application. On the other hand, VillageTree [77] offers intelligent pest management by gathering pest [12] Data collection Field data recording 2-tier Android IRIS [55] Data collection Field data recording 2-tier -iFarm [57] Data collection Field data recoding 2-tier -GeoFarmer [25] Data collection Field data recording 2-tier Android incidence reports from farmers. The application gathers crowd-sourced reports on pest incidences from end-users.…”

Section: -Tier Androidmentioning

confidence: 99%

“…Additionally, APIs can be used to integrate SAAs to other systems in the farm. For instance, in AgDa-Box [12], APIs are used to integrate to any other application that allows http communication with the server. Similarly, Agri-IoT [38] connects to other applications using APIs, while in SmartFarmKit [54], farm services are published as RESTful APIs with JSON data format.…”

Section: Reconfigurable Applicationsmentioning

confidence: 99%

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A Survey on Mobile Applications for Smart Agriculture

et al. 2021

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The increasing global demand for food and nutrition security has raised the need to automate processes in modern farming. As such, a promising way to automate those processes is by using smart agriculture applications (SAAs). Different studies in the literature classify these applications based on agricultural themes, agricultural domains, and farming scenarios. However, this classification is not sufficient for researchers and industry to gain deeper insights on software engineering issues pertaining to SAAs. In this survey, we explore SAAs and further classify them based on architectural models, supported software engineering issues, and target mobile platforms. The survey results show that SAAs in general (1) follow different architectural models, (2) are targeted for different mobile platforms, and (3) satisfy different software engineering issues. Most importantly, the key findings from this study reveal that SAAs can fail to meet their intended purpose if developers ignore key software engineering issues. These findings can be used as a starting point for researchers and industry to implement smart agriculture related mobile applications.

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“…The paper [1] proposed a set of ontologies to facilitate the agricultural data integration, the authors in [23] proposed to loaded the data into RDF triples before being transformed into a relational schema. In [2], the authors proposed a system based on JSON format and provided an API, which centralised and standardised to communicate with different applications to integrate the various datasets. They chose PostgreSQL being a relational database, as the database management system.…”

Section: Related Workmentioning

confidence: 99%

Crop Knowledge Discovery Based on Agricultural Big Data Integration

Ngo

Kechadi

2020

Proceedings of the 4th International Conference on Machine Learning and Soft Computing

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Nowadays, the agricultural data can be generated through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, agricultural laboratories, farmers, government agencies and agribusinesses. The analysis of this big data enables farmers, companies and agronomists to extract high business and scientific knowledge, improving their operational processes and product quality. However, before analysing this data, different data sources need to be normalised, homogenised and integrated into a unified data representation. In this paper, we propose an agricultural data integration method using a constellation schema which is designed to be flexible enough to incorporate other datasets and big data models. We also apply some methods to extract knowledge with the view to improve crop yield; these include finding suitable quantities of soil properties, herbicides and insecticides for both increasing crop yield and protecting the environment.

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AgDataBox API – Integration of data and software in precision agriculture

Cited by 16 publications

References 7 publications

Data Integration for Precision Agriculture - Challenges and Opportunities for the Database community

Data Integration for Precision Agriculture - Challenges and Opportunities for the Database community

A Survey on Mobile Applications for Smart Agriculture

Crop Knowledge Discovery Based on Agricultural Big Data Integration

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