A Mobile Robotic Platform for Crop Monitoring

Abstract

Development, implementation and performance verification of a field-based high-throughput plant phenotyping mobile platform for monitoring Canola plants, including both data acquisition/visualization software and measurement system, was the main contribution of this research. Plant breeders need an efficient tool to monitor a number of plant traits to achieve a higher yield. Currently, manual measurement to gather the required information is customary. Manual measurement has many limitations especially to study a large-scale field. In this research, a high-throughput plant phenotyping platform was developed. A long boom was attached to a farm vehicle to carry different sensors, cameras and other measurement equipment. A program was developed to read sensors signals and to geo-tag data using GPS for future retrieval. Three programs were developed for image acquisition via webcam and still cameras and a central program for data processing and data visualization. The efficiency of different system architecture including different data transmission networks was examined by conducting several laboratory and field tests on a Canola nursery in spring-summer of 2017. The performance of the phenotyping platform was validated by various measures such as conducting manual measurements and comparing the results with the values given by the platform. The main contributions of this research for plant phenotyping research are automation of image acquisition, enhancement of the data acquisition cycle to minimize data geo-referencing error, development of a modular program for data visualization, and faster data collection in a high-throughput fashion (almost 125 times faster). Works are underway to replace the vehicle with an autonomous ground vehicle (AGV). Keywords: Mobile platform; Sensors; Automated crop monitoring