Artificial Intelligence Model for Cooperative Organic Farming - Maneetoem, Attapap; Dangcham, Siriwan (Journal of Applied Informatics and Technology, 2024)
The objectives of this research were to: 1) Design and develop artificial intelligence system model for cooperative organic farming (AI-COF). 2) To develop a model for measuring and analyzing factors in organic agriculture cultivation using NB-IoT. 3) To develop an application for producers and consumers of organic products. Tools and methods for developing a geographic information system are applied in the spiral model of system development life cycle. The system user group consists of the Phetchaburi Cooperative Organic Farming Network Agricultural Group. The research results were as follows: 1) the AI-COF is a combination of electronic sensors. There are 3 parts. (1) Measurement and analysis tools for organic agriculture cultivation factors with NB-IoT. (2) Temperature and humidity sensor kit and camera for plant growth monitoring. (3) The application for producers and consumers of organic agricultural products serves as a storefront for purchasing products and allows consumers to request specific vegetables to be grown. 2) Evaluation the model quality of the AI-COF. (1) The efficiency of the participatory organic agriculture cultivation AI system model is high, with an average level of 4.13 and a standard deviation of 0.54. (2) The efficiency of the measurement and analysis tools of planting factors with NB-IoT was at a high level with an average level of 4.18 and a standard deviation of 0.57 (3). Performance of the application for producers and consumers is at a high level with an average level of 3.55 and a standard deviation of 0.64. (4) Benefits to use in cultivation is at a high level with an average level of 4.18 and a standard deviation of 0.54. (5) Benefits to organic farming groups is at a high level with an average level of 3.76 and a standard deviation of 0.55 and the overall quality in every aspect is at a high level with an average level of 3.96 and a standard deviation of 0.57. The research results of the artificial intelligence system for participatory organic farming can be applied to groups of farmers who produce other types of crops in order to meet the needs of consumers.