Assessment of Coffee Yields Using Unmanned Aerial Vehicle Techniques - Chansing, Sawitree (Journal of Letters, 2024)
The objective of this research is to study factors related to coffee yields and to develop a model for assessing the productivity of Arabica coffee based on data obtained from unmanned aerial vehicle (UAV) in Ban San Charoen, Tha Wang Pha District, Nan Province, during the 2021-2022 harvest seasons. The researchers used variables which are plant height, canopy size, trunk circumference, and visible atmospherically resistant index (VARI). These variables are related to coffee yields using linear regression analysis and evaluated statistical reliability using the coefficient of determination (R2), and the root mean square error (RMSE) were employed. The research findings revealed that the equations of coffee-yield estimation for all four study areas, using plant height, canopy size, trunk circumference and visible atmospherically resistant index (VARI), were able to explain 74-88% of yield variations, with all variables significantly affecting coffee-yield estimation at a 95% confidence level. The most influential factor in yield predictions was trunk circumference (0.582), followed by the VARI vegetation index (0.411), canopy size (0.406), and plant height (-0.401). When analyzing the average yield per plant for all study areas, the RMSE value was found to be 2.12 kilogram per plant. Additionally, the researchers tested the coffee yield estimation model for average yield per plant on a validation area and found the root mean square error (RMSE) value of 2.37 kilogram per plant on average. This research can serve as an example of using UAV data for future Arabica coffee production.