3 research outputs found
Automatic Segmentation of Trees in Dynamic Outdoor Environments
Segmentation in dynamic outdoor environments can be difficult when the illumination levels and other aspects of the scene cannot be controlled. Specifically in orchard and vineyard automation contexts, a background material is often used to shield a camera\u27s field of view from other rows of crops. In this paper, we describe a method that uses superpixels to determine low texture regions of the image that correspond to the background material, and then show how this information can be integrated with the color distribution of the image to compute optimal segmentation parameters to segment objects of interest. Quantitative and qualitative experiments demonstrate the suitability of this approach for dynamic outdoor environments, specifically for tree reconstruction and apple flower detection application
Machine Vision-Based Crop-Load Estimation Using YOLOv8
Labor shortages in fruit crop production have prompted the development of
mechanized and automated machines as alternatives to labor-intensive orchard
operations such as harvesting, pruning, and thinning. Agricultural robots
capable of identifying tree canopy parts and estimating geometric and
topological parameters, such as branch diameter, length, and angles, can
optimize crop yields through automated pruning and thinning platforms. In this
study, we proposed a machine vision system to estimate canopy parameters in
apple orchards and determine an optimal number of fruit for individual
branches, providing a foundation for robotic pruning, flower thinning, and
fruitlet thinning to achieve desired yield and quality.Using color and depth
information from an RGB-D sensor (Microsoft Azure Kinect DK), a YOLOv8-based
instance segmentation technique was developed to identify trunks and branches
of apple trees during the dormant season. Principal Component Analysis was
applied to estimate branch diameter (used to calculate limb cross-sectional
area, or LCSA) and orientation. The estimated branch diameter was utilized to
calculate LCSA, which served as an input for crop-load estimation, with larger
LCSA values indicating a higher potential fruit-bearing capacity.RMSE for
branch diameter estimation was 2.08 mm, and for crop-load estimation, 3.95.
Based on commercial apple orchard management practices, the target crop-load
(number of fruit) for each segmented branch was estimated with a mean absolute
error (MAE) of 2.99 (ground truth crop-load was 6 apples per LCSA). This study
demonstrated a promising workflow with high performance in identifying trunks
and branches of apple trees in dynamic commercial orchard environments and
integrating farm management practices into automated decision-making