Leaf maturation from initiation to senescence is a phenological event of plants that is a result of the influences of temperature and water availability on physiological activities during a life cycle. Detection of newly grown leaves (NGL) is therefore useful in diagnosis if growth of trees, tree stress and even climatic change. There are many important applications that can naturally be modeled as a low-rank plus a sparse contribution. This paper develop a new algorithm and application to detect NGL. It uses first sparse matrix as a preprocessing to enhance target and applied deep learning to segment the image. The experimental results show that our proposed method can detect targets effectively and decrease false alarm rate.