In the face of escalating global population, diminishing arable land, and an increasing demand for organic food, innovative farming systems are imperative. Traditional farming, characterized by excessive chemical usage, poses health and environmental concerns. Addressing these challenges, this research introduces a sophisticated system that amalgamates image processing, deep learning, and precision nutrient dosing for efficient soil-less farming. Utilizing a dataset of 24 GB, encompassing 4187 images of 11 plant varieties across three growth stages, a deep learning model was developed for automatic plant species identification, health assessment, and growth stage detection. The system’s prowess is exemplified with Butterhead lettuce, where the model’s insights guide nutrient dosing in an aeroponic tower. Integrating Arduino Uno for data acquisition and actuator control, with Central Computing Unit (Orange Pi 5) for backend management and deep learning application, the system’s architecture is robust and comprehensive. An Android application complements the system, offering real-time sensor data visualization, aeroponic tower initialization, and plant location tracking. The system also provides location-based growth recommendations, greenhouse-specific advice, and companion planting suggestions. In a month, the system projected a yield of 40 plants across 11 varieties. Drawing insights from various research papers, this system epitomizes the fusion of technology and agriculture, offering a promising solution for controlled environment agriculture and precision farming. This research not only advances soil-less farming practices but also addresses the increasing demand for organic food, presenting an efficient solution for large-scale cultivation.