Abubakr Muhammad:
Robotic Crop Phenotyping Testbed for Sustainable Agriculture

Dr. Abubakr Muhammad is an associate professor & chair of electrical engineering and the founding director of the Centre for Water Informatics & Technology (WIT) at Lahore University of Management Sciences (LUMS), Pakistan. He received his Ph.D. in Electrical Engineering from Georgia Institute of Technology, USA. Since joining LUMS in 2008, his interests are at the intersection of environment, technology, and society, covering research topics related to hydro-informatics, agricultural robotics, and human-water interactions. He serves on various advisory panels to government agencies and industry in Pakistan on water, climate, and agricultural policy, especially on the use of emerging digital technologies for these sectors.

Muhammad Owais Tahir is an Electronics and Communication Engineer with specialization in robotics, currently working as Research Associate at NCRA – Agriculture Robotics Lab, Centre for Water Informatics & Technology (WIT), LUMS. He received his Masters degree from Shanghai Jiao Tong University. His interests includes Autonomous Robotic Systems and Navigation/Mapping in unknown environments.

The practices promoted by industrial agriculture emphasize heavy use of chemicals, mechanization, the use of GM crops, and most recently the use of digital tools. These capital-intensive practices have put small farm holders around the world in jeopardy about their livelihoods and businesses. Moreover, input-heavy agricultural practices are unsustainable for soil, atmosphere, and water. Therefore, there is a dire need to promote sustainable agricultural practices for smallholder farm prosperity. Thousands of farmers across the globe have begun adopting sustainable intensification (SI) practices such as mulching, raised beds, intercropping, no-tilling, and irrigation optimization have led to the use of minimal inputs, maximum outputs, and protection of the environmental resources. To study such innovations to scale up SI, we have set up a testbed to study sustainable agricultural practices using digital phenotyping tools driven by laser scanners and cameras. We have integrated an indigenously developed robotic platform with digital sensors to provide information about phenotypes, crop yield, irrigation usage, disease, nutrient deficiency, and other aspects useful for researchers and farmers to evaluate one agricultural practice against the other. The main purpose of the platform is to gather data and aid in the development of low-cost field-deployable sensors and systems at the farmer level with an eye towards advancing sustainable practices.