The symptoms associated with deficiencies in plants tends to appear often on the leaves. The color and shape of a leaf often used for diagnosing the nutritional deficiencies in plants and classification of these properties often pose serious problem. Since same color and shape may have many root cause problems. It is hence necessary to carefully analyze the texture of leaf with proper training of a classifier. In this paper, we design an acquisition-based classification model that utilizes Internet of Things (IoTs) for data acquisition and recurrent neural networks (RNN) for the task of classification. Prior classification, the model is trained over several iteration based on careful observation of features and its related symptoms. The simulation is conducted with fine-tuning of classification after several iterations. The results of simulation show that the proposed method obtains improved classification accuracy in terms of accuracy and F-measure than other deep learning models.