Weeds to increase crop yields Weeds are an unwanted intruder in the agricultural business. We have to detect the Crop and weeds using YOLOV8 Model.
I include the image that the dataset folder must have. The Test and Val folders also contain an images and labels folder inside of them, as does the train folder.
The main thing for the YOLO Custom object detection is custom.yaml formation.It attached the format is given below and also I add the My custom.yaml file in this repository
path: (optinal) train: # train folder path val: # val images test: # test images (optional) nc: 2 #number of classes names: ['crop','weed'] # mention the object name
The main thing in yaml file variable after colan(:) a space must be provided before the values.If you are correctly mention the path ,nc and names but forget the space it will show the error.
The all the output will be saved same path under the runs/detect/train. prediction result also saved in the same path in runs/detect/predict The Predict folder object detected images.
