A new dataset for coffee rust detection in Colombian crops base on classifiers

2017

rev.ing.univ.Medellin

Self Cite

Nowadays exists a trend that combines the features of Web 2.0 services and telecommunications services known as Telco 2.0. These converged services have been successfully implemented in early warning systems providing improved agility and flexibility in service delivery. However the deployment of converged services in rural zones of developing countries presents several constraints which do not allow to provide this kind of services, as the unavailability of a Next Generation Network (ngn), absence of advanced technology and lack of investment resources. This paper proposes a jain slee and OpenBTS integration architecture for early warning systems in rural zones. The implemented prototype is evaluated with a specific case study involving the deployment of Telco 2.0 warnings in Colombian coffee plantations which may be affected by coffee rust, one of the most threatening diseases in coffee production.Key words: jain slee, OpenBTS, integration, early warning system, converged services * The current proposal was supported by the program Fortalecimiento de la Red Interinstitucional de Cambio Climático y Seguridad Alimentaria -RICCLISA; specificaly by the project Servicios de generación de alertas Agroclimáticas como soporte a la toma de decisiones del sector Cafetero Colombiano -AgroCloud (WP2). This project is funded by Universidad del Cauca, CINARA, CENICAFE, CIAT, CREPIC and the program Redes de Conocimiento from COLCIENCIAS during 2013-2017. Mejorando los sistemas rurales de alertas tempranas a través de la integración de OpenBTS y jain slee ResumenActualmente existe una tendencia que combina las características de los servicios Web 2.0 y los servicios de telecomunicaciones, conocida como Telco 2.0. Estos servicios convergentes se han aplicado exitosamente en sistemas de alertas tempranas, proporcionando mayor agilidad y flexibilidad en la prestación de servicios. Sin embargo, existen varias limitantes que no permiten el despliegue de servicios convergentes en las zonas rurales de países en vía de desarrollo, como la falta de disponibilidad de una ngn (Next Generation Network), la ausencia de tecnología avanzada y la falta de recursos para inversión. Este artículo propone una arquitectura de integración entre jain slee y OpenBTS para sistemas rurales de alertas tempranas. Se evalúa el prototipo implementado con un caso de estudio específico al enviar advertencias Telco 2.0 a los cafeteros colombianos cuyas plantaciones puedan verse afectadas por la roya, una de las enfermedades más peligrosas para la producción de café.Palabras clave: jain slee, OpenBTS, integración, sistema de alertas tempranas, servicios convergentes.

Section: Case Study and Experimental Prototypementioning

confidence: 99%

“…Studies on coffee rust have concluded that the spores carrying the infection are spread by climatic elements such as wind and rainfall. Wind is the vector for long distance spore transport, whilst precipitation droplets are responsible for vertical propagation from infected leaves or soil [11].…”

Section: Case Study and Experimental Prototypementioning

confidence: 99%

Improving Rural Early Warning Systems through the Integration of OpenBTS and jain slee

Plazas

Rojas

2017

rev.ing.univ.Medellin

Self Cite

“…A real case is presented in [80,82]. Their dataset includes 147 instances to try to detect the incidence rate of rust.…”

Section: Amount Of Datamentioning

confidence: 99%

“…On the basis of the foregoing, we have identified four data quality issues (noise, incompleteness, outliers and amount of data) in a real dataset for coffee rust detection exposed in [80,82]. The data used in this work was collected at the Technical Farm (Naranjos) of the Supracafe, in Cajibio, Cauca, Colombia (21°35'08"N, 76°32'53"W), during 2011-2013.…”

Section: Timelinessmentioning

confidence: 99%

A systematic review of data quality issues in knowledge discovery tasks

Ledezma

Rev. ing. univ. Medellín

2016

Self Cite

Large volume of data is growing because organizations are continuously capturing the collective amount of data for a better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust. ResumenHay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.

“…For example, data collected about weather conditions, soil fertility properties, and physical properties and management of a coffee crop, can be used to predict the rust infection rate still in the early days of the harvest [9]. In the same way, weather conditions such as the minimum and maximum levels of temperature, humidity and rainy days can help estimating the behavior of rice blast [10].…”

Section: Introductionmentioning

confidence: 99%

Toward detecting crop diseases and pest by supervised learning

Casas

2015

IyU

Self Cite

The climate change has caused threats to agricultural production; the extremes of temperature and humidity, and other abiotic stresses are contributing factors to the etiology of disease and pest on crops. About the matter, recent research efforts have focused on predicting disease and pest crops using techniques such as supervised learning algorithms. Therefore in this paper, we present an overview of supervised learning algorithms commonly used in agriculture for the detection of pests and diseases in crops such as corn, rice, coffee, mango, peanut, and tomato, among others, with the aim of selecting the algorithms that give the best performance for the agricultural sector.