Date of Award:

8-2025

Document Type:

Dissertation

Degree Name:

Doctor of Philosophy (PhD)

Department:

Civil and Environmental Engineering

Committee Chair(s)

Alfonso Torres-Rua

Committee

Alfonso Torres-Rua

Committee

Burdette Baker

Committee

Brennan Bean

Committee

Sierra Young

Committee

Brent Black

Abstract

Tart cherry is an important specialty crop in the United States, particularly in Utah where the area dedicated to tart cherry cultivation is larger than any other fruit grown in the state. However, growers face increasing challenges due to rising land prices, limited water availability, and extreme weather events. These factors make it essential to adopt new technologies that improve efficiency and sustainability. Precision agriculture, which uses data-driven tools to manage crops more effectively, can help address these challenges. However, most tart cherry orchards still rely on traditional practices due to a lack of specialized technologies for this crop. This dissertation introduces two innovative methods for monitoring tart cherry yield. The first is a low-cost yield monitor that tracks fruit bin changes during harvest. A proximity sensor detects when a bin has been unloaded, and a Global Positioning System (GPS) records its location. Yield is estimated by dividing the average weight of a full bin by the distance between bins. Field tests showed that bin filling was consistent among different operators and that the system was robust enough to withstand harvest conditions, while successfully demonstrating spatial variation of yield. The second approach applies artificial intelligence to count cherries as they move through the conveyor belt of the harvester. An object detection model detects individual fruits, providing an estimate of total yield. While the system showed strong agreement with actual yield, although some variation remained unexplained due to occlusion from fruit overlap and differences in cherry size. Beyond yield monitoring, this dissertation also developed models to estimate stem water potential, a key indicator of water stress. Using drone-based multispectral and thermal imagery combined with weather and soil moisture data, six equations were created. The models performed well, highlighting the importance of chlorophyll related wavelengths, particularly the red band and vegetation indexes in water stress detection. These findings provide valuable tools for tart cherry growers, enabling yield mapping and allowing more precise irrigation practices. By adopting these technologies, growers can enhance productivity while using water and other resources more efficiently.

Checksum

1189a17d70c8af6382acc7f148fe3c0a

Creative Commons License

Creative Commons Attribution-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-No Derivative Works 4.0 License.

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