278 research outputs found
End-to-end deep learning for directly estimating grape yield from ground-based imagery
Yield estimation is a powerful tool in vineyard management, as it allows
growers to fine-tune practices to optimize yield and quality. However, yield
estimation is currently performed using manual sampling, which is
time-consuming and imprecise. This study demonstrates the application of
proximal imaging combined with deep learning for yield estimation in vineyards.
Continuous data collection using a vehicle-mounted sensing kit combined with
collection of ground truth yield data at harvest using a commercial yield
monitor allowed for the generation of a large dataset of 23,581 yield points
and 107,933 images. Moreover, this study was conducted in a mechanically
managed commercial vineyard, representing a challenging environment for image
analysis but a common set of conditions in the California Central Valley. Three
model architectures were tested: object detection, CNN regression, and
transformer models. The object detection model was trained on hand-labeled
images to localize grape bunches, and either bunch count or pixel area was
summed to correlate with grape yield. Conversely, regression models were
trained end-to-end to predict grape yield from image data without the need for
hand labeling. Results demonstrated that both a transformer as well as the
object detection model with pixel area processing performed comparably, with a
mean absolute percent error of 18% and 18.5%, respectively on a representative
holdout dataset. Saliency mapping was used to demonstrate the attention of the
CNN model was localized near the predicted location of grape bunches, as well
as on the top of the grapevine canopy. Overall, the study showed the
applicability of proximal imaging and deep learning for prediction of grapevine
yield on a large scale. Additionally, the end-to-end modeling approach was able
to perform comparably to the object detection approach while eliminating the
need for hand-labeling
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