You will get YOLO model for detecting strawberry phenological stages and ROS integration
Project details
1 Collect and label strawberry images.
2 Configure YOLOv8.
3 Train with split datasets.
4 Adjust detection layers.
5 Enhance accuracy with post-processing.
6 Optimize for real-time use.
7 Deliverables are optimal model file (.pt), data configuration (YAML), performance graphs, and optionally, the annotated dataset.
2 Configure YOLOv8.
3 Train with split datasets.
4 Adjust detection layers.
5 Enhance accuracy with post-processing.
6 Optimize for real-time use.
7 Deliverables are optimal model file (.pt), data configuration (YAML), performance graphs, and optionally, the annotated dataset.
Machine Learning Tools
NumPy, Python, PyTorchWhat's included $2,200
These options are included with the project scope.
$2,200
- Delivery Time 10 days
- Number of Revisions 1
- Number of Model Variations 1
- Number of Scenarios 2
- Number of Graphs/Charts 3
- Model Validation/Testing
- Model Documentation
- Source Code
Optional add-ons
You can add these on the next page.
Fast 6 Days Delivery
+$300
Additional Revision
+$300
Additional Model Variation
(+ 2 Days)
+$300
Additional Scenario
+$300
Additional Graph/Chart
+$300
Data Source Connectivity
+$500About Pablo
Agricultural Robotics Researcher | ROS, AI, Mechanical
Celaya, Mexico - 11:44 pm local time
Steps for completing your project
After purchasing the project, send requirements so Pablo can start the project.
Delivery time starts when Pablo receives requirements from you.
Pablo works on your project following the steps below.
Revisions may occur after the delivery date.
Project
1 Collect and label strawberry images. 2 Configure YOLOv8. 3 Train with split datasets. 4 Deliverables are optimal model file (.pt), data configuration (YAML), performance graphs, and optionally, the annotated dataset.