Work item:
Y.smad
Subject/title:
Service model for managing product forecasting and automating smartfarm data based on AI in future networks
Status:
Under study
Approval process:
AAP
Type of work item:
Recommendation
Version:
New
Equivalent number:
-
Timing:
2027-Q3 (Medium priority)
Liaison:
ITU-T SG11, SG17, SG20, SG21, ISO TC347, FAO
Supporting members:
Sunchon university, China Telecom, China Unicom and Korea (Rep.of)
Summary:
In order to quickly improve the crop growth environment, real-time analysis of crop and environmental data is essential. This includes analysing crop growth patterns, identifying problems, and proposing methods for the process of improvement. To achieve this, network-based parallel processing and standardized procedures for disease prevention and environmental control are necessary.
An optimized service model (platform) for crop production is developed by analysing crop disease status using environmental sensor data collected from smart farms.
The smart farm environment sensor data helps maximize crop production by enabling early diagnosis of crop diseases based on weather conditions, soil microorganism information, and crop leaf analysis, followed by timely quarantine measures.
Based on the collected environmental data, machine learning and deep learning analysis are performed to diagnose crop diseases and recommend appropriate countermeasures.
Smart farm growth status is divided into observation, prescription, work, and outcome stages. In the observation step, initial data are generated by investigating the environment and condition of the agricultural land using environmental sensor data.
The proposed new work item aims to study the concept, general characteristics, scenario and use cases of the service model for managing product forecasting, as well as high-level technical aspect for support the automation of smart farm data based on AI in future networks.
Comment:
-
Historic references: