A Web-based Information System for Plant Disease Forecast Based on Weather Data at High Spatial Resolution

Abstract

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of 240 m×240 m based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.

Customized Spatial Climate Models for North America

“…Errors in estimating daily rainfall occurrence of around 17% compared well with the one other study for North America where this statistic was reported. Daily rainfall occurrence is a critical issue in, for example, modeling crop disease (e.g., Kang et al 2010). A recent revision of this analysis using data corrected for time of observation further reduced these predictive errors by about 15% and 22% for the summary temperature and precipitation residuals, respectively (Hopkinson et al 2011), further underlining the importance of data quality issues.…”

A Forecast Model for the First Occurrence of Phytophthora Blight on Chili Pepper after Overwintering

“…The soil water content model estimated volumetric water content in soil based on the amount of water due to precipitation, runoff, drainage, and evapotranspiration. The KMA weather network with more than 627 automated weather stations over the South Korean Peninsula (Kang et al, 2010) and the digital soil texture map of Korea at the spatial resolution of 30 m × 30 m (Hong et al, 2009) are the valuable sources of input data for estimating the daily soil water contents for local areas throughout Korea.…”

Web-based epidemiological surveillance systems and applications to coffee rust disease

“…This holistic-systemic approach results in a digital technological model of innovation (Figure 3) (Kang et al, 2010), but articulated to a rational research framework to support the development of methods and algorithms in solving the phytosanitary problem (Coria-Contreras et al, 2019;Mora-Aguilera et al, 2014d;Kuang et al, 2012). The general structure of a web-based ESS has three perfectly differentiated components: 1) Generation of relevant field data through App-Web platform synchronization; 2) Modular system for advanced data management and analysis in the web platform and restricted analysis in the App; 3) Alerts and risks communication.…”

“…Revista Mexicana de FITOPATOLOGÍA Mexican Journal of Phytopathology Este enfoque holístico-sistémico resulta en un modelo tecnológico digital de innovación (Figura 3) (Kang et al, 2010), pero articulado a un marco racional de investigación para sustentar el desarrollo de algoritmos en la solución del problema fitosanitario (Coria-Contreras et al, 2019;Mora-Aguilera et al, 2014d;Kuang et al, 2012). La estructura general de un SVE web tiene tres componentes perfectamente diferenciados: 1) Generación de datos pertinentes de campo a través de sincronización App-Plataforma Web; 2) Sistema modular de gestión y análisis avanzados en Plataforma Web y análisis restringido en App; 3) Comunicación de alertas y riesgos.…”