Surface defect detection in lime by using computer vision

Limes are one of the key ingredients used in Thailand, both in food and beverages. Due to such high market demand for Thai limes, quality inspection is then very important. While manual sorting by humans can lead to inaccuracy, leveraging on technology could alternatively make the process more accurate. Limes are graded by quality. The factors that affect the lime quality or market price are texture, shape, color, and maturity. This research focuses on detecting defects or diseases on lime surface by using computer vision through data collection from image capturing. The researcher used k-means clustering as a segmentation technique to separate the surface of limes into clusters. Each cluster was classified into a color indication of diseases or health. To evaluate the cluster, we used artificial neural network (ANN) and support vector machine (SVM), in which results showed that ANN has led to better performance than SVM. Defect percentage on a lime surface can be measured by computation of pixels from clustering. Consequently, this system will reduce both cost and time.