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Bangladeshi crops leaf disease detection using YOLOv8.

Md Shahriar Zaman Abid¹, Busrat Jahan¹, Abdullah Al Mamun²

¹Department of Computer Science and Engineering, Feni University, Feni, Bangladesh.

|September 23, 2024

Summary

This study uses YOLOv8, a computer vision model, to accurately detect and categorize leaf diseases in major Bangladeshi crops like rice and tomatoes. The advanced system significantly improves early disease identification, boosting crop yields and food security.

Keywords:

CNN Crop leaf disease detection Image classification Machine learning Object detection Technology in agriculture YOLOv8

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Area of Science:

Agricultural Science
Computer Vision
Plant Pathology

Background:

Leaf diseases pose a significant threat to crop yields and food security in Bangladesh.
Major crops like rice, corn, wheat, potato, and tomato are highly vulnerable.
There is a critical need for automated systems for early disease detection and management.

Purpose of the Study:

To evaluate the efficacy of the YOLOv8 object detection model for automated leaf disease identification.
To compare YOLOv8's performance against previous models in detecting diseases in key Bangladeshi crops.
To enhance the efficiency of crop disease detection and management through advanced computer vision techniques.

Main Methods:

A dataset of 2850 images across 19 classes (leaf disease types) was curated and annotated.
The YOLOv8 (You Only Look Once) deep neural network framework was employed for training.
Performance was evaluated using standard metrics including mean Average Precision (mAP) and F1 score.

Main Results:

The YOLOv8 framework achieved a high mean Average Precision (mAP) of 98%.
The system demonstrated a strong F1 score of 97% in disease identification.
YOLOv8 successfully detected and categorized leaf diseases in the studied crops.

Conclusions:

YOLOv8 shows significant potential for accurate and efficient automated crop disease management.
This technology can contribute to improved food security and agricultural sustainability in Bangladesh.
The study highlights the effectiveness of state-of-the-art computer vision for agricultural challenges.