ImmunoYield: A Smart Greenhouse for Disease Detection and Prevention

Traditional farming methods rely heavily on manual labor and are subject to unpredictable yields due to various environmental and biological factors. To address these challenges, proposing “ImmunoYield,” an automated greenhouse system that integrates sensor technology and deep learning for optimizing farm conditions and early disease detection in crops. The system employs humidity, light, and soil sensors for real-time monitoring and control, while a Convolutional Neural Network model is utilized for diagnosing plant diseases through image analysis. Results from experiments indicate that this system reduces the water usage by approximately 30% and increases crop yield by 15% compared to traditional farming methods. Additionally, our deep learning model demonstrates an 86% accuracy in plant disease detection, improving to 80% in real-time testing with new inputs. The suggested approach not only lowers manual labor but also enhances productivity and cost-efficiency, making it a promising solution for modern agriculture. Future work will focus on enhancing the system's generalization capabilities and exploring integration with additional sustainable technologies.