Recent technological innovations such as variable rate seeding and fertilizer application have given farmers the ability to manage large fields as smaller sections with specific application needs. Crop yield data and maps from previous years are the primary source of information from which crop management recommendations and decisions are based upon. Yield monitoring has been widely adopted into current crop production practices since the first commercially successful yield monitor become available more than 20 years ago. Yield monitoring allows for producers to compress the comprehensive list of previous crop input decisions and into a single yield measurement value for that area. When combined with soil properties measurements and production inputs, yield monitoring becomes a useful tool to rate performance and increase profits per acre.
Yield data is a useful tool for making crop management decisions, but becomes irreverent when it is not accurate or reliable. The first goal of this research was to benchmark current yield monitoring solutions to better understand current performance and build performance goals for the next generation of yield monitoring. Two common yield monitors utilizing different methods of yield estimation were selected for benchmarking. Both systems required intensive calibration to achieve accuracy. The volumetric flow yield monitor maintained accuracy across the entire flow range better than the impact-based mass flow yield monitor because of a fundamental measurement system that does not rely entirely upon calibration regression.
A particle flow yield monitor utilizing the advantages of both yield systems was designed and developed for initial performance assessment. Linearity and consistency across a wide range of flow rates for three different crops demonstrated promise for future development of the system. The design performed in conjunction with an impact-based mass flow yield monitor and maintained flow rate linearity for all three crops. Limitations of the current design were revealed in field harvest conditions and validated using simulation tools. Successful initial performance and yield estimation linearity supports continued development of this technology
