PETEFA: Geographic Information System for Precision Agriculture

Palomino, Walther; Morales, Giorgio; Huaman, Samuel; Telles, Joel

doi:10.1109/intercon.2018.8526414

Cited by 8 publications

(5 citation statements)

References 4 publications

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“…A monitoring tool called PETEFA is proposed in 31 , it exploits the data received from a meteorological station sensors, weather station sensors, and images from satellites such as LandSat 8, Spot 6 and 7, WorldView 2, PERUSAT-1 and from UAVs, to provide three types of information and spatial data for the yellow corn crops (Zea Mays) for the sake of the National Institute of Agrarian Innovation (INIA): the crop temperature and health status throughout its lifecycle, the soil state, and information on crop evapotranspiration with different Privileges for INIA and the farmers.…”

Section: Literature Reviewmentioning

confidence: 99%

A case study of a real-time internet of things system for site-specific potato crop management in El-Salhia Area-Egypt

El-Basioni

Mohamed

Belal

et al. 2022

Sci Rep

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The site-specific management is the technology that considers the natural variability within the same field of factors related to crop growth to improve its management practices such that the agricultural treatments are varied for field's small production zones saving resources and environment, and improving crop quality and size. Since site-specific decisions are not far from the Fourth Industrial Revolution and the concept of processes automation, this work addresses improving the process of spatial variability analysis and thus supporting management decisions by developing a system—entitled EGYPADS—based on the Internet of Things and its enabling technologies. EGYPADS automates data collection, zones delineation according to their land suitability evaluation, and maps generation. The paper addresses a case study of potato crop in a specific area in Egypt, El-Salhia, in which eighty-five sites were chosen as main dataset for the modeling process during different stages of crop growth. Three management zones were recognized of the selected field based on the differentiation in their land suitability characteristics, representing about 5%, 65%, and 30% of the total area, respectively. The structure, screens, and services of EGYPADS are described in this paper. EGYPADS offered services include: management zones delineation using absolute and virtual coordinates, Land Suitability Assessment (LSA), data entry from field in real-time as well as from excel sheets, saving maps in suitable format for variable rate application, real-time and historical data processing, centralized management, and flexible formulation of events and related actions. The implementation of EGYPADS was verified. The system dynamically produces non-contiguous isobands, each representing a specific range of parameter values, and can be properly exported for use by other programs or smart machinery. It was proven that EGYPADS supports more than one land with different geometry, area, location, and number of nodes. EGYPADS was compared with the traditional LSA method, and was found to produce similar management zones.

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Section: Literature Reviewmentioning

confidence: 99%

A case study of a real-time internet of things system for site-specific potato crop management in El-Salhia Area-Egypt

El-Basioni

Mohamed

Belal

et al. 2022

Sci Rep

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“…Furthermore, to get value out of the displayed information, some of the SAAs permit data input from the end-users. [9] Irrigation management Irrigation scheduling 2-tier iOS EVAPO [49] Irrigation management Evapotranspiration estimation 2-tier Android PIS [34] Irrigation management Soil moisture sensing 2-tier iOS, Android SoilWaterApp [27] Irrigation management Soil moisture monitoring 2-tier iOS RaGPS [56] Irrigation management Extraterrestrial solar radiation 1-tier Windows SmartIrrigation [79] Irrigation management Irrigation scheduling 2-tier iOS, Android Crop Water Stress [69] Irrigation management Vine water stress 2-tier Android pCAPS [32] Irrigation management Crop water needs 1-tier Android VitiCanopy [23,29,30] Irrigation management Plant water requirement 1-tier iOS, Android SmartfLAIr [11] Yield management Crop yield estimation 1-tier Android PETEFA [62] Geographic information systems Geo-referenced soil analysis 2-tier Android eFarm [86] Geographic information systems Geo-tagging land data 2-tier Android PAMS [85] Geographic information systems Managing farmland spatial data 2-tier iOS, Android, Windows AMACA [76] Farm machinery/tools Estimating machinery cost 2-tier iOS, Android Ecofert [15] Fertiliser management Crop fertiliser estimation 2-tier Android cFertigUAL [65] Fertiliser management Crop fertiliser estimation 2-tier Android BaoKhao [87] Fertiliser management Leaf colour estimation for N fertiliser…”

Section: -Tier Applicationsmentioning

confidence: 99%

“…The processed data are then saved to a cloud-hosted database which is periodically synchronised for updates. Data fetching: To fetch data from the server, some SAAs such as SmartIrrigation [79], PETEFA [62], and Ecofert [15] employ pull-based techniques. The data are pulled at regular intervals to make it real time, i.e., almost at the rate at which it is generated from weather stations and pushed to the cloud.…”

Section: -Tier Androidmentioning

confidence: 99%

“…For instance, in SmartIrrigation [79], a notification is generated and sent to the end-user whenever rainfall data that is recorded at the nearest weather station is received. On the other hand, in PETEFA [62], field images pulled from a geo-server are used to visualise information on evapotranspiration. Additionally, in some SAAs such as Ecofert [15] and cFertigUAL [65], data are pulled from cloud-hosted databases to help end-users compute the amount of fertiliser and water that can be applied to different crops.…”

Section: -Tier Androidmentioning

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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“…En la región Lambayeque, Perú el Instituto Nacional de Innovación Agraria (INIA) desarrollo un SIG denominado PETEFA, está herramienta proporciona información sobre las estaciones meteorológicas, tipo de suelo, usos de la tierra y georreferenciación, asimismo ofrece información temporal sobre la salud de los cultivos a lo largo de su vida, además la versión móvil permite que los agricultores de mantengan informados sobre el estado de cada una de sus parcelas (Palomino et al, 2018).…”

Section: Introductionunclassified

Aplicación del GIS en la caracterización edafoclimática, del distrito de Cacatachi – provincia de San Martín, con fines de producción agrícola rentable y sostenible

2021

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La caracterización edafoclimática de los suelos es un proceso que contribuye a la planificación de las siembras con mejores perspectivas rentables, siendo el punto de partida de la actividad productiva agropecuaria. El objetivo fue generar una base de datos geoespaciales integrada de condiciones edafoclimáticas del distrito de Cacatachi, de consulta dinámica a través de mapas temáticos como de fisiografía, capacidad de uso, características físicas y químicas de suelos y de uso actual de las tierras. Para ello se empleó un software GIS utilizando información de instituciones públicas que iniciaron el proceso; asimismo, la metodología de trabajo tuvo dos etapas, a nivel de gabinete y a nivel de campo. El resultado fue la base de datos Quantum GIS (QGIS), que facilita el manejo de la información edafoclimática, con 104 mapas en detalle, cuyas representaciones espaciales exponen la caracterización de las zonas estratificadas en el proyecto, donde se hicieron las calicatas, de los cuales se hicieron los análisis químicos, físicos y biológicos de los horizontes identificados en los perfiles. La información edafoclimática generada nos aproxima a una microzonificación en detalle, que se convierte en herramienta orientadora de las siembras de cultivos, en función a las potencialidades nutricionales, fisiográficas y climáticas de los suelos, segmentados en sectores productivos.

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PETEFA: Geographic Information System for Precision Agriculture

Cited by 8 publications

References 4 publications

A case study of a real-time internet of things system for site-specific potato crop management in El-Salhia Area-Egypt

A case study of a real-time internet of things system for site-specific potato crop management in El-Salhia Area-Egypt

A Survey on Mobile Applications for Smart Agriculture

Aplicación del GIS en la caracterización edafoclimática, del distrito de Cacatachi – provincia de San Martín, con fines de producción agrícola rentable y sostenible

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