Analysis and Detection of Weeds in Agricultural Area using various Image Segmentation Algorithms

K. Deepa, Dr. N. Sujatha

Abstract: Agriculture is the backbone of human provisions in this world. In the agricultural industry, the weed and crop identification and classification are major technical and economical importance. The segmentation algorithms are focused in this paper. The enhanced segmentation algorithm has been selected to classify weed and crop from the images. There are three main parts the proposed system are segmentation, categorization and error calculation. Several sample images have been tested and the result of some weed coverage rate is illustrated. The misclassification rate is also computed. An algorithm has been done to computerize the tasks of segmentation and classification. Weeds are extracting from images using image processing and describe by shape, color and size features. These features are used to classify different weeds and crop group. We describe different segmentation techniques like Clustering, Thresholding, Watershed, Morphological, color based, Edge-based methods. These are used to differentiate weeds and crops. We analyze all the features of these methods and techniques. The various methods studied and concepts used for crop and weed detection.

Keywords: Image Enhancement, Pre-processing, Segmentation, K-Means Clustering, Watershed, Thresholding, Weeds Detection.