An Approach to Sugarcane Yield Estimation Using Sensors in the Harvester and ZigBee Technology

Data-driven decisions can be performed based on crop yield values, essential information for precision agriculture practices. Technical solutions for yield mapping have been increasing for the sugarcane crop. However, the adoption of a yield monitor is low among farmers. An alternative is associating the amount of sugarcane harvested with the yield. The objective of this study was to evaluate the accuracy of the sugarcane mass prediction by a hydraulic oil pressure sensor installed in the chopper of the harvester. A commercial sugarcane field was used for the field trial with four harvesters and an in-field wagon instrumented with the load cells. All equipment at the harvesting front were equipped with ZigBee technology for data transfer to the sugar mill's Remote control center. The redistribution of the total weight of sugarcane harvested within each field was based on the chopper hydraulic pressure variation. The yield monitor had a low prediction error (4.5%) compared to the total measured by the in-field wagon. The results suggest enhancing the frequency of data collection by the harvester improves the spatial variability detection of sugarcane yield at the field level. The distribution of the total mass of sugarcane harvested indicated that neither empirical model nor sensors calibration is required to estimate yield regardless of the harvester. In future, the application of telemetry and distribution of the total harvest within the field should be studied for other crops, e.g., grains, which already use this technology for the management of equipment in the field.

Automated summary

By installing a hydraulic oil pressure sensor in the chopper of a sugarcane harvester, this study aimed to evaluate the accuracy of sugarcane mass prediction. The results suggest that increasing the data collection frequency by the harvester can improve the spatial variability detection of sugarcane yield at the field level without the need for empirical models or sensor calibration.