Guidelines for The Utilization of Unmanned Aerial Vehicle in Promoting and Enhancing Rice Farming Among Farmers in Song Phi Nong District, Suphanburi Province

Abstract

This study addresses the critical challenges faced by rice farmers in Thailand, including increasing production costs, labor shortages, and health risks associated with manual agrochemical application, by examining the potential of unmanned aerial vehicles (UAVs) in rice cultivation. The study aims to examine farmers’ attitudes toward the use of UAVs in rice cultivation in Song Phi Nong District, Suphanburi Province. It also investigates government and private sector policies in promoting UAV accessibility for farmers, with the goal of proposing guidelines for the integration of UAVs in rice farming. A quantitative research approach was employed, collecting data through surveys from a sample of 400 farmers in Song Phi Nong District, Suphanburi Province. Additionally, a qualitative research method was used to conduct in-depth interviews with 10 experts from government agencies and the private sector. The findings indicate that farmers’ attitudes toward UAV-assisted rice cultivation do not significantly differ at the 0.05 statistical level between farmers who use manual labor and those who use UAVs. Most farmers agree that UAVs help in reducing the use of fertilizers and agricultural chemicals, leading to lower production costs, reduced working hours, and decreased direct exposure to chemicals. However, the high cost and complex operational systems of UAV technology present significant barriers to adoption. The in-depth interviews reveal that both the government and private sectors have policies to support and promote UAV technology for farmers. These policies include training programs on UAV operation and financial assistance for acquiring agricultural UAVs. Through content analysis, the researcher developed the DRONE Model, a framework for promoting UAV integration in rice cultivation. This model, distinguished by its five core components (Data-driven agriculture, reduce fertilizers and chemicals, Operation automation, Network collaboration, and Environmental and economic sustainability), offers a comprehensive and innovative approach to sustainable smart farming. The model achieved a suitability score of over 0.5, demonstrating its potential applicability in advancing the agricultural sector in Thailand.