Application-Based Integration of Motile Near-Infrared and Electrochemical Sensors with Realtime, In-Situ Indication of Nitrate-Induced Crop Stress

Overview

Researchers in precision agriculture are exploring efficient methods of monitoring nitrogen content in crop systems to manage resources, increase crop yields, and prevent environmental degradation from fertilizers. Conventional methods of monitoring nitrogen are inaccessible, expensive, and unrepresentative of spatial and temporal changes. Given that nitrogen levels are extremely variable, it is key to have an efficient system that monitors dynamic nitrogen levels in both the soil and crop canopy throughout the growing season. This study aimed to create a system that connects optical near-infrared (NIR) sensors and nitrate sensors, due to their strong biological association, via python script, an autonomous soil collector, and an interactive app with geospatial mapping, allowing for realtime analysis of crop health. To create a functional module, several sensors were interfaced using a Raspberry Pi (RPi), Python, and Google Firebase (GF). An integrated program was successfully designed and tested to receive, process, and upload data from a NIR sensor, GPS receiver, and nitrate ion selective electrode (ISE). A soil collection mechanism was designed, built, and controlled remotely with the Python script for in-situ soil analysis. Data from each sensor was uploaded realtime to a database, overlaid on a map, and presented in an IOS app. This study takes a novel approach to in-situ nitrogen monitoring for the prospect of more sustainable farming and resource management: a system that collects data pertaining to crop health and the soil, equates field conditions, and displays geospatial data.

Competition history

• JSHS 2020 Category not listed

Resources

• National JSHS abstract book ↗

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