The main aim of this project is to identify the location of weeds among crops. The approach of the project includes achieving a baseline method for developing a real time weed detection system through binary classification. Image is captured using camera, The digital images were captured in outdoor light conditions with perspective projection over crop. Captures have 8MP resolution in RGB color space with JPG extension. The captured image is converted to HSV image. Then image is segmented using excessive green colour segmentation. Further image is converted into gray scale image. Grey scale image is given as input to ‘Gray level co-occurrence matrix’. • Contrast • Correlation • Energy • Homogeneity
HSV image of weed:
Hue is therefore the actual color. Brightness refers to how much white or black is mixed in the color, while Saturation indicates the amount of grey in a color. A saturation value of 0 indicates mostly grey while 100% luminosity (or L = 255) is white. H - Hue S - Saturation V – Value
Segmented image of weed:
In computer vision, image segmentation is the process of partitioning a digital image into multiple segments . The goal of segmentation is to simplify and change the representation of an image into more meaningful and easier image to analyze.
Gray image of the weed:
A gray scale image is simply one in which the only colors are shades of gray. The reason for differentiating such images from any other sort of color image is that less information needs to be provided for each pixel.
The proposed system ensures the presence of weed by contrast, color, homogeneity. The image captured is processed using machine vision and plant is identified either to be crop or weed by image segmentation. The final gray image indicates the presence of weed, the location of weed is clearly identified and pesticide is sprayed to diminish the growth of weed. The system enhances growth of crops and increases the yield. The system uses rotating camera and avoids obstacles during weed detection.