Background/Objectives: Object identification in color images is a complex task, especially when the background and lighting conditions of the environment are uncontrolled. Methods/Statistical Analysis: This paper proposes an object identification algorithm to identify the light green tea leaves from the tea plants. Findings: In this paper we examine the wavelet transform, one of the most recent mathematical tools related to image processing, for identifying our desired object. Here, we concentrated on identifying the light green tea leaves. The paper is divided into two main parts. In the first part, we illustrated the compression for a color image, by using two different wavelets transforms i.e. the Haar wavelet and Integer wavelet transform (IWT). In this we have analyzed the behavior of these two wavelet transform with different tea leaves images and identified the most appropriate wavelet transform that can perform better process to compress the given color image. To analyses the performance of the wavelet transform with the color images, we calculated Peak Signal to Noise Ratio (PSNR) and compression ratio. The second part deals with Segmentation, which includes edge segmentation and clustering i.e. K-means. Applications/Improvements: The enhancement has been performed for edges. K-means clustering is used to separate the colors from the image, whereas edge based segmentation gives the shapes of the desired regions. Finally the light green tea leaves are identified by using above mentioned methods. These theoretical aspects are illustrated through MATLAB.