Crop Health Monitoring and Disease Detection

Overview:

The purpose of this project is to create a deep learning model for crop detection using YOLOv5. Crop detection technology can be valuable for various applications in agriculture, such as monitoring crop health, yield prediction, and pest detection.

📁 Dataset: For this project, you'll need a dataset of images containing various crops. You can collect or curate this dataset, ensuring it has annotated bounding boxes around the crops.

Workflow: Here's a general workflow for your crop detection project using YOLOv5:

Data Preparation: Dataset Collection and Annotation: Collect a dataset of images containing different crops. Annotate the images with bounding boxes around each crop using labeling tools like LabelImg or VGG Image Annotator. Folder Structure: Organize your dataset into a folder structure that YOLOv5 can accept. It should have separate folders for images and labels, and each label file should correspond to an image file.

Steps to Use YOLOv5:

Cloning YOLOv5 Repository: Clone the YOLOv5 repository from the official GitHub repository.

Setting Up Environment: Change the directory to the YOLOv5 folder and set up your Python environment with the necessary dependencies. You can use a virtual environment to manage your packages.

Downloading Pre-trained Weights: Download pre-trained YOLOv5 weights for your desired model size (e.g., YOLOv5s, YOLOv5m, YOLOv5l, YOLOv5x). These weights will be used as a starting point for training.

Steps Before Training Custom Dataset:

• Go to yolov5/data/.
• Open data.yaml
• Edit the following inside it:

1. Training and Validation file path
2. Number of classes and Class names.

3. Training YOLOV5 Model

• Set images size 640 with batch of 8.

• Train model around 1000 epochs .

• Visualise the training metrics with the help of tensorboard.

  Testing Images Using Test Data: