Tomato Planting Environment Monitoring and Pest Warning System Using IOT and Huawei Modelarts

This paper presents an intelligent agricultural monitoring system that integrates Internet of Things (IoT) technology with deep learning using Huawei Model Arts for tomato crop management. The system continuously monitors environmental parameters such as temperature, humidity, soil moisture, and pH using IoT sensors, while a Convolutional Neural Network (CNN) analyses leaf images to detect pest infestations and diseases. A Raspberry Pi serves as the central processing unit, enabling real-time data acquisition, processing, and control of irrigation and pest management systems. Alerts are generated via GSM, LCD display, and buzzer for timely farmer intervention. The proposed system achieves a disease detection accuracy of 91.4% and operates with a low-cost hardware setup. The integration of AI and IoT enhances precision agriculture by reducing manual monitoring, minimizing crop loss, and improving productivity.