Work

Development of an Application for Rice Cultivation: Weather Forecasting System and Crop Variety Selection

Year: 2025

Type: article

Abstract: The primary objective of this research was to develop a mobile application aimed at supporting rice production planning, specifically in the areas of weather forecasting and selecting suitable rice varieties for cultivation. Additionally, the study assessed technology acceptance using the Technology Acceptance Model (TAM), which is a key framework for evaluating consumers' attitudes towards new technologies. The research employed the Extreme Programming (XP) approach in software development to connect data through an Application Programming Interface (API), allowing for the collection of weather forecasts and agricultural information from government organizations. The sample group comprises 50 rice farmers from Mueang District, Surin Province, in the Lower Northeastern Region of Thailand, selected using a purposive sampling technique. Participants were required to have a basic understanding of technology and the ability to access information online. The research team systematically gathered data using questionnaires to explore and process descriptive statistics. The findings indicated that the developed application successfully provided relevant information for rice cultivation decision-making, including weather forecasts, expected rainfall, appropriate rice varieties for the production area, agricultural market information, and contact information of agricultural agencies. The evaluation of technology acceptance among the sample group showed high levels of satisfaction regarding usability, with a mean score of 4.07 (S.D. = 0.80), and perceived usefulness, with a mean score of 4.16 (S.D. = 0.63). The result indicated that the farmers’ technology acceptance was at a high level, which reflected the essential beginning of adaptation to a new form of data-driven agriculture for decision-making.

Source: Asian Health, Science and Technology Reports

DOI: 10.69650/ahstr.2025.3364

Authors: Attapol Kunlerd, Boonlueo Nabumroong, Kraisak Jantarakomet, Ruttanachira Ruttanaprasert, Kunlachat Burana

Institutions: Rajamangala University of Technology Isan, Surin Campus, Khon Kaen University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Industry, innovation and infrastructure

Open Access status: diamond

Funders: Naresuan University, Thailand Science Research and Innovation