A key challenge in precision agriculture is the difficulty of obtaining plant-level yield data in specialized crops such as sour cherry.
Although mechanical harvesting has been an established practice for decades, the adoption of yield monitoring systems has remained limited, mainly due to the high cost of commercial solutions.
This study, conducted at Utah State University, aims to address this gap by developing a low-cost easily deployable system capable of providing useful data for site-specific orchard management.
The main scientific contribution lies in the design of an integrated system consisting of an ultrasonic proximity sensor, a GNSS receiver for geolocation, and a microcomputer for data acquisition and synchronization.
The system is designed to be mounted on existing harvesting machines without requiring significant structural modifications. This feature greatly enhances its practicality and potential adoption among growers. A key challenge in precision agriculture is the difficulty of obtaining plant-level yield data in specialized crops such as sour cherry.
Although mechanical harvesting has been an established practice for decades, the adoption of yield monitoring systems has remained limited, mainly due to the high cost of commercial solutions. This study, conducted at Utah State University, aims to address this gap by developing a low-cost easily deployable system capable of providing useful data for site-specific orchard management.
The main scientific contribution lies in the design of an integrated system consisting of an ultrasonic proximity sensor, a GNSS receiver for geolocation, and a microcomputer for data acquisition and synchronization.
The system is designed to be mounted on existing harvesting machines without requiring significant structural modifications. This feature greatly enhances its practicality and potential adoption among growers.
From a methodological perspective, the system relies on an indirect approach to yield estimation. Instead of measuring fruit weight in real time, which would require expensive and complex sensors, it detects when collection containers or bins are filled and replaced.
The ultrasonic sensor records the distance variation associated with this event, while the GNSS receiver assigns a geographic position to each occurrence. By assuming an average weight per bin, it becomes possible to reconstruct the spatial distribution of production and generate yield maps of the orchard.
Experimental validation was carried out under real operating conditions, demonstrating that the system can provide reliable results in practice. In particular, the positioning accuracy achieved with a low-cost GNSS proved to be adequate, with errors of only a few meters compared to more advanced systems.
The comparison between estimated bin weights in the field and those measured at processing facilities showed relatively small differences overall. These differences are mainly attributable to variability in filling levels and possible product losses during transport.
One of the most significant advantages of the system is its affordability, making it accessible to small and medium-sized producers who are typically excluded from advanced technological solutions. Its ease of installation and adaptability to other crops or bin-based harvesting systems further enhance its overall practical application value.
Additionally, the collected data can be integrated with GIS platforms and other decision-support tools for management. This contributes to more efficient and sustainable resource management in agriculture.
However, the approach also presents some inherent limitations. Yield estimation is based on an assumed average bin weight, which can vary depending on operating conditions cultivar and filling level. The spatial resolution of the resulting maps is constrained by the frequency of bin replacement during harvesting.
Although positioning errors are relatively small, they may still affect fine-scale agronomic analyses.
Overall, the study demonstrates that simple and cost-effective technological solutions can significantly improve agronomic data collection processes in contexts where economic constraints are critical. The proposed approach represents an important step toward the broader adoption of precision agriculture in specialized crops.
It also opens the way for future developments, including the integration of more advanced sensors and data analysis techniques.
Source: dos Santos Safre, A.L., Black, B., Wedegaertner, K. et al. Development of a low-cost yield monitor system for tart cherries. Precision Agric 26, 103 (2025). https://doi.org/10.1007/s11119-025-10299-1
Image source: Stefano Lugli
Melissa Venturi
University of Bologna (IT)
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