The soybean cyst nematode (SCN) is responsible for billions of dollars in yield losses for the soybean industry. This plant parasite infects the roots of soybean plants and feeds off the plant’s nutrients to complete its life cycle. Effective long-term control of SCN populations in fields has been an ongoing subject of research efforts, including new methods to extract, count, and possibly deter these pests from soybean roots. While SCNs are constantly evolving new strategies to invade a multitude of soybean varieties, most of the engineering approaches applied by soybean farmers and soil diagnostic clinics to extract SCNs and count their eggs were developed several decades ago. This thesis has a central focus on automating the engineering methods of soil processing to extract and count the SCN cysts and eggs with discussions on various sensors, image processing techniques, and building blocks of robotic systems.

Chapter 1 provides an introduction to the area of SCN extraction and quantification from soil. This includes a history of the discovery of SCN parasites, and a review of the existing extraction and quantification methods applied to this nematode. This is followed by a narrative on modern actuation methods and technologies for automation and robotics that may provide a better alternative to the manual SCN extraction method. It also discusses how sensor systems have applications within the realm of robotics and soil science.

Chapter 2 details the development of a robotic instrumentation platform for the purposes of carrying out SCN extractions from soil in an automated fashion. It offers a discussion of the new system (capabilities, modular design, software interface, etc) and outlines the robotic application of the extraction protocols it automates. It also compares the performance of the developed robotic platform with the manual sieving method predominately used in the soil diagnostic industry to extract SCN cysts and eggs from soil samples.

Chapter 3 describes two methods to remove debris from extracted samples of SCN cysts and eggs and the use of high-throughput image processing techniques to count the cysts and eggs. A density gradient solution is prepared to efficiently separate the SCN cysts and eggs through centrifugation, followed by imaging the separated sample on a scanner or in microfluidic flow chips. The two quantification methods provide low-cost autonomous alternatives to the time consuming and specialized nematode slide counting method utilized in several nematology labs today.

Chapter 4 is a review of emerging sensor technologies that have integrated various materials, physical designs, and devices to create body-compliant wearable sensing platforms. Within the realm of wearables for sweat sensing, the chapter discusses different target analytes, sensing functionalities, and current applications. While such sensor technologies have been generally applied to monitor the human physio-chemical profile, their introduction to plants and agricultural applications is largely awaited. As such, this chapter provides a comprehensive background to wearable sensing platforms that could possibly be expanded to monitor the surface physiology and health of plant systems.