Among the developing countries, India is one of the fastest-growing economies in the world today, and a considerable part of this economy is dependent on its agricultural sector. The country also boasts the world’s second-largest population, implying that resource scarcity is constantly a concern. Fresh water is one of the most crucial and precious resources. We also have unpredictable monsoons and isotropic climate conditions to contend with. For many years, traditional techniques have been utilized to irrigate gardens and farms. This technique still needs automation in order to reduce the time and effort required for manual examination. We present a model for an automated watering system that attempts to reduce both human interaction and water usage. Our system offers a simple interface for cultivating in various settings, from home to industrial. This system will use data from soil moisture sensors and images of crops acquired by a camera and weather forecasts via the application programming interface (API). In addition, we used a deep learning model to classify captured images into a droop and healthy class. Finally, our fuzzy logic algorithm aggregates all these parameters and regulates the irrigation system’s operation time.