Sunna Forecast Warning System (STUS)

Pest forecast and warning system

Description

Sunna Forecast Warning System (STUS) is a web-based decision-support system that uses sunn pest biology, wheat phenology and meteorological data to forecast pest development and guide control decisions in wheat production. The solution predicts pest outbreaks, supports timing of field sampling and insecticide applications.

Technology

  • Artificial intelligence (AI)
  • Big data
  • Cloud
  • Desktop/web-based application
  • Remote sensing
  • Sensor

Sunna Forecast Warning System is built around a central database that automatically imports weather observations from national meteorological stations and ministry forecast–warning stations (METOS) and stores pest and crop observations. The system applies biological and phenology-based models to estimate pest and disease risk for specific crops, regions, and time periods. STUS provides web interfaces where experts can view risk maps and time-series charts, update field scouting data, manage and issue warnings. AI-supported functions are being piloted to complement the core models, such as using remote imaging to track adult migrations and detecting nymph hotspots.

Target

  • Academia and research institutions
  • Farm advisors
  • Public sector

The Sunna Forecast Warning System primarily serves authorized plant-protection researchers and public plant-health officers, together with farm advisors and relevant public authorities.

Business model

  • Fully free

Sunna Forecast Warning System is provided as a public digital service by the Republic of Türkiye Ministry of Agriculture and Forestry. Development and operation are financed through national agricultural research, meteorological and information-technology budgets and competitive national research grants, with no fees for users. Training, extension materials and online access are offered free of charge as part of ministry plant-protection and rural advisory programmes.

Impact

Sunna Forecast Warning System aims to reduce wheat yield and quality losses and limit unnecessary pesticide use by focusing monitoring and insecticide applications. Within Türkiye’s plant-health forecast and warning services, 274 devices in 47 provinces provide data for around 2 million decares of cropland, helping direct timely advisory messages and treatments.

Partners

Bilgi Teknolojileri Genel Müdürlüğü; Bozok University; Central Plant Protection Research Institute; Education and Publication Department of the Ministry of Agriculture and Forestry; Gıda ve Kontrol Genel Müdürlüğü; Konya Provincial Directorate of Agriculture and Forestry; Meteoroloji Genel Müdürlüğü; Middle East Technical University Department of Computer Engineering; Tarla Bitkileri Merkez Araştırma Enstitüsü Müdürlüğü; Tarımsal Araştırmalar ve Politikalar Genel Müdürlüğü; Zirai Mücadele Merkez Araştırma Enstitüsü Müdürlüğü

Four Betters

  • Better production
  • Better environment
  • Better life

Sustainable Development Goals

  • SDG 2: Zero hunger
  • SDG 9: Industry, innovation, and infrastructure
  • SDG 12: Responsible consumption and production
  • SDG 17: Partnerships for the goals
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