1. Microwave Sensing for Estimating Cranberry Crop Yield: A Pilot Study Using Simulated Canopies and Field Measurement Testbeds
- Author
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Alex F. Haufler, Susan C. Hagness, and John H. Booske
- Subjects
Yield (engineering) ,Dimensionality reduction ,Crop yield ,Principal component analysis ,Supervised learning ,Reflection (physics) ,General Earth and Planetary Sciences ,Environmental science ,Electrical and Electronic Engineering ,Linear discriminant analysis ,Microwave ,Remote sensing - Abstract
We present the results of an experimental and computational pilot study of cranberry crop yield prediction using low-power microwave sensing and machine learning. We simulated backscattered radiation from cranberry canopies using plane-wave illumination with frequency content from 300 to 2400 MHz. The computational canopy domains allowed for variable soil moisture and cranberry yield in terms of cranberry mass per 1 ft² of canopy surface area. We collected experimental field data with a prototype open-ended waveguide sensor operating between 600 and 1300 MHz. We measured experimental microwave signals by placing our sensor directly on top of cranberry-crop bed canopies in central Wisconsin and recording reflection coefficients across the operating band. We implemented a machine learning approach to map the microwave reflection coefficients to yield. The mapping procedure involves dimensionality reduction with principal component analysis, supervised learning with linear discriminant analysis, and error-correcting output codes. The idealized computational results demonstrate the feasibility of discriminating three cranberry volume fractions that are representative of central Wisconsin field conditions, at frequencies below 2 GHz. Performance evaluations of the machine learning algorithm applied to the measured field data indicated that, in 81% of test cases, the predicted crop yield had less than 8% error. Most importantly, the average yield prediction error was less than 1.3%. These pilot study results provide strong evidence that machine learning enables accurate cranberry yield estimation when trained with in situ (field) microwave backscattered signals.
- Published
- 2022
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