Determination of field capacity for the sugarcane harvester using GNSS data

Abstract

Harvesting is an important activity in the sugar production industry. Due to the labor shortage and time limitation during harvesting season, farmers have adopted cane harvesters to substitute the farm workers in this restless period. Cane harvesters are huge and expensive machines with high field capacity. Because of inappropriate working conditions in Thailand, the actual field capacity is much lower than that in its specification. The objective of this research is to study the factors affecting the field capacity of the sugarcane harvester. A GNSS logging system was used to record the machine’s position and traveling speed during operation. Crop yield for each field was also collected. Field dimension and other working parameters such as working time and the number of turns were derived from the GNSS data. A field capacity prediction model was developed. The study shows that the optimal working speed, crop yield, and the number of turns per field area were significant factors to predict the harvester’s field capacity. The coefficient of determination (R2 value) of the model was 0.625. It was suggested to include more machine and field variation for further robust model development and uses in the optimization of field operation performance.