Selection of proper combine harvesters to field conditions by an effective field capacity prediction model

Farmers have to finish their harvesting with high efficiency, because of time and cost. However, farmers are lacking knowledge and information required for selecting suitable combine harvesters and giving the conditions of their rice fields, because both information factors (combine harvester and field condition) impact the field capacity. The field capacity model was generated from combine harvesters with the Thai Hom Mali rice variety (KDML-105). Therefore, this study aimed to determine the prediction model for effective field capacity to combine harvesters when harvesting the Thai Hom Mali rice variety (KDML-105). The methods began by collecting data of 15 combine harvesters, such as field, crop, and machine conditions and operating times; to generate the prediction model for the KDML-105 variety. The prediction model was then validated using 12 combine harvesters that were collected similarly to the model creation. The results showed a root mean square error (RMSE) of 0.24 m2/s for the model. The prediction model can be applied for farmers to select the proper combine harvesters and give their field conditions.