The main component of a plant is water. Consequently, a plant’s ability to grow is greatly impacted by changes in its water content. Visual identification of items is a relatively easy talent for a human. Still, computer-based picture recognition is a difficult task. More recently, developments in machine learning have led to strong detection and classification methods that perform well in challenging contexts. Based on plant recognition and classification, this work proposes a machine learning-based intelligent agricultural architecture. The goal of this project is to organize and expand an intelligent framework that takes machine vision and support vector machines (SVM) into account in order to improve plant development in a regional water scenario. For item recognition in the context of smart agriculture, the approach leverages a strong deep learning-based architecture. This design describes a technique for image identification that focuses on automatically recognizing plants and flowers. It looks for, locates, and classifies disease in leaves and fruit automatically. It then contours the exact site of harm for viewing. The aforementioned mobile iOS application can achieve improved accuracy by employing the SVM as a classification tool with high accuracy for plant conditions, according to the results.