Agriculture is the major for the people in India, where the loss of irrigation is due to deficiency in plants. This project is to identify the nutrient deficiency in plants like calcium (Ca), magnesium (Mg), nitrogen (N), phosphorus (P), potassium (K), and sulfur (S). A demanding component of plant health is decided by plant stress responses, which are characterized by a suite of molecular and cellular processes that are triggered by the plant in some form of extraction. Extraction can be abiotic, for example, dry season or abundance light, or biotic, for example, herbivores. Studies have appeared, while plants do demonstrate their pressure by means of variety of leaf in synthetic substance and atomic changes, the shades of leaves are additionally clear markers of plant pressure. In general isolation, deficiency of nutrients such as nitrogen, phosphorous, potassium, calcium, and magnesium in soils results in changes in coloring patterns of leaves. Healthier/stress-free leaves are typically greener in color, while increased yellowing indicates progressively unhealthier leaves. Unfortunately, existing techniques to detect plant health incur for excess exorbitant infrastructure. In this paper, the features of different leaf images are captured, then train and test those images to identify mineral deficiency through machine learning. © Springer Nature Singapore Pte Ltd 2021.