End-to-end deep learning for directly estimating grape yield from ground-based imagery

TL;DR

This study demonstrates the use of deep learning and proximal ground-based imagery to accurately estimate grape yield in vineyards, reducing manual effort and improving scalability in challenging environments.

Contribution

It introduces an end-to-end deep learning approach that eliminates manual labeling, achieving comparable accuracy to object detection models for vineyard yield estimation.

Findings

Transformer and object detection models achieved ~18% error.

End-to-end models performed comparably to object detection.

Saliency maps showed model focus near grape bunches.

Abstract

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.