YOLOv8n model fine-tuned for real-time tomato ripeness detection in greenhouse environments. Detects and classifies tomatoes into three ripeness stages with 3D localisation via Intel RealSense D435.

Model Description

Architecture: YOLOv8n (nano)
Task: Object detection
Input: 640×640 RGB image
Classes: 3 (unripe, semi-ripe, fully ripe)
Training epochs: 80
Framework: Ultralytics

Classes

ID	Class	Description
0	Unripe	Green tomato, not ready for harvest
1	Semi-ripe	Partially ripe tomato
2	Fully ripe	Red tomato, ready for harvest

Performance

Model	Epochs	Precision	Recall	mAP50	mAP50-95
YOLOv8n	80	0.817	0.771	0.843	0.693
YOLOv8s	80	0.825	0.796	0.850	0.710

The nano model was selected for deployment due to its speed advantage with negligible accuracy trade-off.

Files

File	Description
`model_hub_n.pt`	PyTorch model
`model_hub_n_int8_openvino_model/`	OpenVINO INT8 quantised model

Usage

python

from ultralytics import YOLO

model = YOLO("model_hub_n.pt")
results = model.track("image.jpg", conf=0.85)

With Intel RealSense D435 and full 3D localisation, see the GitHub repository.

Training Data

Custom dataset built from three public greenhouse tomato image sources, unified and preprocessed via Roboflow:

Multi-source images covering different ripeness stages and lighting conditions
Manual bounding box annotation per ripeness class
Data augmentation: flipping, rotation, brightness adjustment, mosaic
Training / validation / test split

OpenVINO Optimisation

An INT8 quantised version is available for optimised CPU inference using OpenVINO. Export from the PyTorch model:

python

model = YOLO("model_hub_n.pt")
model.export(format="openvino", int8=True)

License

MIT License

pablofntdz/yolov8-tomato-detection

YOLOv8 Tomato Ripeness Detector