A Smart, Wireless, and Energy-Efficient Color-Based Plant Health Monitoring System Using M5Stack's M5Stamp-Pico
Abstract
Plant health monitoring is one of the key components of precision agriculture for timely interventions to maximize crop output and utilization of resources. This chapter describes a novel, smart, wireless, and energy-efficient plant health monitoring system based on the M5Stamp-Pico module of M5Stack. The system uses RGB and multispectral sensors to detect critical signs, such as leaf discoloration and water levels, that classify plant health as either healthy, moderately strained, or severely stressed. Real-time data communicated over Wi-Fi or LoRa protocols is then processed and visualized for actionable insights within less than two seconds. The system recorded an accuracy of 95.2% against a standard NDVI-based approach (88.5%), as well as manual inspection (80.0%). Energy efficiency has become much improved and has reduced power consumption by up to 25%. Its use is therefore feasible at distant locations or off-grid areas for agricultural purposes. Its robust validation by 490 statistical metrics ensured that the results it produced were reliable. This study addresses several essential challenges in plant health monitoring, such as energy consumption, scalability, and real-time utilization. It is an innovative solution for current agriculture, combining IoT and machine intelligence. Future enhancements, such as edge computing and integration of renewable energy, can help further improve system autonomy and scalability and open new ways for more sustainable farming approaches.
