Tea leaf maturity levels based on ycbcr color space and clustering centroid

Abstract

Abstract Technology develops very rapidly in all areas. Smart Farming 4.0 is a farming management concept that uses modern technology to increase quantity and quality. The picking of tea leaves during this time the farmer is only based on the quotes from the planting block. If the block is already arriving, then the block is taken in a thorough plucking. However, the picking time can be erratic due to weather factors. The design of the tea leaf maturity level identification system based on the digital image processing of tea leaves. The leaf image of the tea is then processed on a system that begins with segmentation preprocessing, the image that has been uniform then carried out the extraction of images transformed into the color features of YCbCr. After obtaining the luma and chroma values, the classification using Centroid is based on statistical characteristics. Then the extraction and classifying data is a system database that will then be used during the testing process. The total data of Peko tea leaves (P + 2) is used as much as 90 training images and 90 test images. The maturity classification of tea leaves uses a Centroid Clustering method with centroid 10 based on YCbCr color space and a minimum statistical feature, maximum, and variances get an accuracy value of 80% and computation time of 2.80 seconds.