Deep Fruit Vision

This program detects, classifies, identifies ripeness, and identifies maturity of fruit in an image, video, or camera stream.

Installation

We incorporated Yolo-v5 (specifically release 6.2) for this project. You can download and install this project and its requirements with:

git clone https:
cd fruit-detection https://github.com/JVP15/fruit-detection.git
pip install -r requirements.txt
git clone https://github.com/ultralytics/yolov5.git
cd yolov5
pip install -r requirements.txt

Datasets

You can automatically download most of the datasets by doing

cd modules
python datasets.py

However, there are some datasets that need to be manually downloaded from Google Drive and renamed to the correct name. These are:

Papaya Dataset

Ripeness Dataset (rename to ripeness_dataset.zip)

Defect Dataset (rename to defect_dataset.zip)

Ensemble Dataset

Running the Program

You can run Deep Fruit Vision by using: python run.py

You can use the GUI version by adding the --use-gui argument like: python run.py --use-gui