Plant Species Detection Using YOLOv8 Deep Learning
Discover how YOLOv8 enables real-time plant species identification and localization for precision agriculture and biodiversity monitoring.
Deep Learning · Computer Vision
Plant Species Object Detection
Using YOLOv8 Model
Automated identification and localization of plant species using state-of-the-art real-time object detection
Research Team · October 2023
CONTENTS
01
Project Overview & Motivation
02
Dataset & Plant Species
03
YOLOv8 Architecture
04
Training Pipeline
05
Results & Performance
06
Conclusion & Future Work
01 — Project Overview
Why Automate Plant Species Detection?
Manual plant identification is time-consuming, error-prone, and requires expert knowledge. Automating with deep learning enables scalability for agriculture, ecology, and biodiversity monitoring.
🌿 Biodiversity Monitoring
Track and catalog plant species at scale
🌾 Precision Agriculture
Identify crops and weeds automatically
🔬 Scientific Research
Accelerate botanical studies
02 — Dataset & Plant Species
Training Data & Species Coverage
5,000+
Training Images
15
Plant Species
3 Classes
Healthy / Diseased / Weed
Rose
Sunflower
Fern
Cactus
Tomato Plant
Basil
Dataset sourced from PlantVillage & custom field photography. Annotated using Roboflow.
03 — YOLOv8 Architecture
How YOLOv8 Works
Input Image
Raw pixel data
Backbone (CSPDarknet)
Feature extraction
Neck (PANet / FPN)
Feature aggregation
Detection Head
Coord & class predictions
Output: Bounding Boxes + Labels
Final predictions
Key Features of YOLOv8
Why YOLOv8 for Plants?
Anchor-free detection
Multi-scale feature fusion
Real-time inference speed
Pretrained on COCO dataset
High accuracy on small objects (leaves, flowers)
Fast inference — suitable for field deployment
Easy fine-tuning on custom datasets
04 — Training Pipeline
From Raw Images to Trained Model
Data Collection
Field photography + public datasets
Annotation
Bounding box labeling with Roboflow
Preprocessing
Resize to 640×640, augmentation (flip, rotate, HSV)
Model Training
YOLOv8n/s fine-tuned, 100 epochs, batch size 16, AdamW optimizer
Evaluation
mAP@0.5, Precision, Recall metrics
yolov8s.pt
plants.yaml
100
640
05 — Results & Performance
Model Evaluation Metrics
91.4%
89.7%
88.3%
42
Tomato Plant — 96.2% confidence
06 — Conclusion & Future Work
What We Achieved & What's Next
Key Takeaways
Successfully trained YOLOv8 to detect 15 plant species with 91.4% mAP
Real-time detection at 42 FPS — suitable for edge deployment
Robust performance across lighting conditions and leaf orientations
Open dataset pipeline enables easy extension to new species
Future Work
Expand to 50+ plant species
Mobile deployment (iOS/Android via CoreML/TFLite)
Integrate disease severity scoring
Drone-based field detection system
Bridging deep learning and botanical science for a greener future.
Thank You
Questions & Discussion
github.com/nature-research/plant-yolo
contact@botanicalvision.edu
Computer Vision & Plant Sciences Lab
- yolov8
- computer-vision
- deep-learning
- plant-identification
- precision-agriculture
- ai-research
- object-detection