Developing robust sensors for IoT applications in precision agriculture

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The long-term objective of this research program is to increase diversity of sensors, and reliability to operate under different spatial and temporal agricultural scenarios. In the future, sensors in remote farms shall emit data for management; and machines will become more autonomous. On the short term, the specific objectives are: - To discover the relationship between electromagnetic waves and foliar nutrients to develop novel non-invasive sensors for crop health parameters, - To design machine vision systems, to distinguish between farm materials and objects including plants, stones, soil, crop, etc. to be implemented within farm machinery for autonomous operations or decision-support systems, - To design power and connectivity schemes for sensors to enable them working in farm conditions by; inventing hybrid energy harvester that can operate newly developed sensors based on abundant energy in farm such as machine vibration, solar light, and wind; and incorporating low-power data transmission components in the sensors. The first objective depends on studying the fundamentals of electromagnetic wavelengths and their interactions with biological materials. The methodology of developing spectral sensors requires creating datasets through lab testing and implementing statistical modelling to develop estimation models. The second requires applications of image processing and machine learning to manipulate the spectral data captured in image datasets. The accuracy of the machine vision system will be influenced by the design of components such as light and camera. The third relies on finding renewable energy within the agricultural context and inventing hybrid energy harvesting method to guarantee continuous power supply. This will be achieved by reducing the power requirement of the machine vision system connectivity before integrating it to the energy harvester. The novel sensors will address challenges of remote and reliable data collection. This is a necessary step towards enhancing digital agriculture in terms of detailed management of the food supply chain and machine automation. In Canada, where most of farms are remote, the application of these sensors will reduce travelling need for data collection and maintenance. The ability to conduct this research program while partnering with McCain Foods and Potatoes New Brunswick provides field and facility accessibility necessary to test sensors. Also, it increases the chance of fast adaptation for commercialization on local and global scales because of McCain Foods' leading position in potato processing industry globally. The HQP training will focus on educational and research methodologies, and project management skills through prototyping and testing novel sensors and methodologies of the research output. This will prepare engineering students for both academic and industrial work environments as they move forward in their careers.