Energy-Efficient LED Lighting and Smart Climate Control in the Hydroponic System of Vertical Farming using AI and IoT

Many factors today pose problems in the agricultural industry, such as the increasing expansion of urban settings, less land on which food can be produced, and limited resources leading this path of sustainable food production to necessity. Vertical farming can help feed an increasingly global population in the future. Vertical farming is one of the innovative solutions to tackle the increasing demand for food production in city regions while maximizing resource utilization. By cultivating crops in vertically stacked layers, this method maximizes land utilization vertically, reduces water consumption, and minimizes the environmental footprint compared to traditional farming. To enhance the efficiency and productivity of vertical farming, this study integrates Internet of Things (IoT) technology with hybrid deep learning algorithms, specifically utilizing the ResNet50 architecture within Convolutional Neural Network (CNN). The system employs a network of IoT sensors to continuously monitor key environmental parameters such as temperature, humidity, pH levels, nutrient concentrations, and light exposure Using a CNN-ResNet50 model specifically trained on this sensor data allows not only for predictive analysis, but also to optimize the farming conditions in real time. Data is collected and stored via a cloud-based platform backed by machine learning algorithms to analyze relevant trends and recommend actionable solutions to improve yield while reducing the wastage of resources. This new methodology highlights the power of intelligence in the shape of technologies to transform a vertical farming idea into practical data-driven, so an efficient, sustainable and deployable method has been introduced for contemporary agriculture. This system utilizes the potential of the Internet of things and Artificial Intelligence to overcome the challenges in Smart agriculture.