AI-driven environmental monitoring for hydroponic agriculture: ExCNN-LFCP approach

The continuous advancement of Artificial Intelligence (AI) and Internet of Things (IoT) technologies presents unprecedented opportunities to revolutionize agriculture. Hydroponics, a soilless method of plant cultivation, demands precise management of nutrients, water, and environmental conditions. This study introduces an innovative AI-driven hydroponics system for vegetable farming, employing a Levy Flight Carnivorous Plant optimized Extra Convolutional Neural Network (ExCNN-LFCP) integrated with an IoT cloud server. The primary objective is to enhance efficiency, productivity, and sustainability by precisely detecting and controlling the hydroponic environment, addressing the labor-intensive and error-prone nature of traditional methods. This system leverages AI and IoT to automate and optimize hydroponics operations, promoting optimal growth conditions and efficient resource utilization. Central to this network is the ExCNN-LFCP model, which combines the feature extraction capabilities of Extra Convolutional Neural Networks (ExCNN) with the optimization prowess of the Levy Flight Carnivorous Plant (LFCP) algorithm. This hybrid model enhances the prediction and adjustment of environmental parameters critical for boosting plant growth and yield. An IoT cloud server collects real-time data from sensors monitoring pH levels, nutrient concentrations, temperature, and humidity. This data is processed by the ExCNN-LFCP model to predict optimal conditions and make necessary adjustments. The server facilitates remote monitoring and centralized management of multiple hydroponic units, ensuring efficient operations across various locations. Experimental results demonstrate that the ExCNN-LFCP-based IoT hydroponics system outperforms existing terms across different performance measures, achieving an accuracy of 99.1% and a recall of 97.3% thus validating the system's efficacy and reliability in agricultural settings. It enhances learning and adaptability while achieving high performance with minimal resource wastage.