Tomato Disease Segmentation using K-Means Clustering

Infected Fruit Part Detection using K-Means Clustering Segmentation Technique

— Nowadays, overseas commerce has increased drastically in many countries. Plenty fruits are imported from the other nations such as oranges, apples etc. Manual identification of defected fruit is very time consuming. This work presents a novel defect segmentation of fruits based on color features with K-means clustering unsupervised algorithm. We used color images of fruits for defect segmenta...

Estimation of Fungus/Disease in Tomato Crop using K-Means Segmentation

Tomato crop is primarily infected by various common diseases like Bacterial Canker, bird's-eye fruit spots, Bacterial Spot, Chlorosis, Curly Top, Early Blight, Fusarium Wilt, Gray Leaf, Gray Mold Rot, Leaf Mold, Leaf Roll and Leaf Curl, Powdery mildew, Septoria Leaf Spot, Tobacco Mosaic Virus, Verticillium Wilt. The presented work describes a algorithm for different disease detection based on t...

Automatic White Blood Cell Segmentation Using K Means Clustering

Evaluation of blood smear is a commonly clinical test these days. Most of the time, the hematologists are interested on white blood cells (WBCs) only. Digital image processing techniques can help them in their analysis and diagnosis. For example, disease like acute leukemia is detected based on the amount and condition of the WBC. The main objective of this paper is to segment the WBC to its tw...

Color Image Segmentation Using a Spatial K-Means Clustering Algorithm

This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In additi...

K - Means Clustering for Automatic Image Segmentation