Frequent measurements of the plant phenotypes make it possible to monitor plant status during the growing season. Stem diameter is an important proxy for overall plant biomass and health. However, the manual measurement of stem diameter in plants is time consuming, error prone, and laborious. The use of agricultural robots to automatically collect plant phenotypic data for trait measurements can overcome many of the drawbacks of manual phenotyping. The objective of this research was to develop a robotic system that can automatically detect and grasp the stem, and measure its diameter of maize and sorghum plants. The robotic system comprises of a four degree of freedom robotic manipulator, a time-of-flight camera for vision system, and a linear potentiometer sensor to measure the stem diameter. Deep learning and conventional image processing were used to detect stem in images and find grasping point of stem, respectively. An experiment was conducted in a greenhouse using maize and sorghum plants to evaluate the performance of the robotic system. The system demonstrated successful grasping of stem and a high correlation between manual and robotic measurements of diameter depicting its ability to be used as a prototype to integrate other sensors to measure different physiological and chemical attributes of the stem.

Abstract.: Pesticide application is an integral part of crop production, and ground-based agricultural boom sprayers are used extensively to apply pesticides to the crop canopy or soil surface across millions of acres in the United States. Efficient application is necessary to minimize costs and limit adverse environmental impacts. The goals of this study were to provide quantified measurements on the effects of nozzle setup errors on spray pattern uniformity and evaluate how laboratory patternator-based simulations would compare to measurements on a full spray boom. More specific objectives were to determine the effects of factors such as nozzle lateral angle, nozzle spacing, nozzle replacement, and nozzle pitch angle on spray pattern distribution and evaluate a simulation approach to predict the effects of single nozzle boom setup errors on full boom system pattern uniformity. Laboratory and sprayer-based tests were devised to quantify the impact of nozzle setup and operational errors on spray pattern uniformity, boom pressure, and nozzle flow rates. Results indicated that small variations in boom setup or nozzle operation (i.e., pressure or flow) can cause significant errors in spray nozzle distribution which may not be completely detectable by measuring spray pattern alone. Simulations using laboratory data from setup or operational errors reflected similar changes (differences less than 2.6%) in spray pattern CV as full boom data with similar setup errors. These findings were significant in that it may be possible to model errors within full boom spray distributions based on smaller laboratory-collected datasets. Keywords: Equipment, Equipment for crop protection, Patternator, Spray pattern distribution, Spraying.

As various technologies are integrated and implemented into the food and agricultural industry, it is increasingly important for stakeholders throughout the sector to identify and reduce cybersecurity vulnerabilities and risks associated with these technologies. However, numerous industry and government reports suggest that many farmers and agricultural equipment manufacturers do not fully understand the cyber threats posed by modern agricultural technologies, including CAN bus-driven farming equipment. This paper addresses this knowledge gap by attempting to quantify the cybersecurity risks associated with cyberattacks on farming equipment that utilize CAN bus technology. The contribution of this paper is twofold. First, it presents a hypothetical case study, using real-world data, to illustrate the specific and wider impacts of a cyberattack on a CAN bus-driven fertilizer applicator employed in row-crop farming. Second, it establishes a foundation for future research on quantifying cybersecurity risks related to agricultural machinery.