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Vision Based Detection of Xylem Water Expression
by Joshua Chen
| Institution: | University of California |
|---|---|
| Department: | |
| Degree: | |
| Year: | 2022 |
| Keywords: | Electrical engineering; Robotics; Artificial intelligence; computer vision; object detector; precision agriculture; stem water potential; xylem |
| Posted: | 3/25/2025 |
| Record ID: | 2224135 |
| Full text PDF: | https://escholarship.org/uc/item/06r8m7t3 |
Stem water potential (SWP) measurements are used in agriculture to determine the water stress in crops and the optimal irrigation schedule. The current widely used method to take such measurements is the Scholander pressure chamber due to its simplicity, but it is labor intensive and presents potential harm to the operator if not prepared or used properly. Automating the process of taking SWP measurements with a computer vision control system can help increase efficiency, precision, and accuracy over the current methods, additionally ensuring operator safety as it is unnecessary for the operator to maintain close proximity to the chamber. This research aims to pioneer an autonomous computer vision control system as a means to aid growers and function on the existing platform, with the intent of eventually being widely adopted in precision agriculture.
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