Smart Monitoring System for Enhancing Plant Health and Sustainable Growth

As agriculture plays a vital role responsible for sustaining the growing population, it is important to enhance crop health monitoring to ensure food security and maximize farming methods. This paper presents an extensive review of crop health monitoring systems, with the integration of artificial intelligence (AI), computer vision, and the Internet of Things (IoT). The efficacy of several approaches, such as sensor-based networks, data-driven prediction models, and image processing techniques, in predicting plant diseases and improving agricultural yield is explored. Key deep learning architecture models for image classification, such as -- Convolutional Neural Networks (CNNs), Data Augmentation, Transfer Learning, and You Only Look Once (YOLO), for plant disease identification are discussed. Using an image classification process via CNN, the images of a few leaves and other plant traits are analyzed to distinguish between healthy and unhealthy crops. Further, key parameters such as temperature, humidity, soil moisture, and pH levels are continuously monitored through sensor networks integrated into agricultural systems, in order to get the details of crop health. For optimal crop growth, the soil needs to be nutrient-rich with a pH level between 6.5 and 7.5 to increase the soil’s fertility. In conclusion, crop health monitoring using AI and computer vision has proven highly effective, with a 92% accuracy in plant disease detection. Through this integration, farmers are empowered to take preventive measures, reduce losses, and maximize their crop yields. This study paves the way for future advancements in sustainable farming by utilizing innovative technologies.