About

Scott Shearer received his Ph.D. in agricultural engineering from The Ohio State University in 1986. He currently serves as Professor and Chair of Food, Agricultural and Biological Engineering at OSU. Prior to 2011, he was Chair of Biosystems and Agricultural Engineering at the University of Kentucky. His research career highlights include the development of methodologies and controls for metering and spatial application of crop production inputs such as seed, fertilizer, and pesticides; modeling of agricultural field machinery systems; autonomous multi-vehicle field production systems; strategies for deployment of unmanned aerial systems (UAS) in agriculture; and analyses of production agriculture data sets. Dr. Shearer has led research supported by over $12 million in grants, authored more than 200 technical publications, and has made numerous invited presentations at international conferences, professional meetings, and farmer forums. His professional credentials include being a Fellow of the American Society of Agricultural and Biological Engineers.

Research topics

• Environmental science
• Computer Science
• Geography
• Engineering
• Agricultural engineering
• Business
• Agronomy
• Artificial Intelligence
• Environmental resource management
• Archaeology
• Economics
• Geotechnical engineering
• Environmental planning
• Telecommunications
• Soil science

Selected publications

• Economics of Small- vs. Large-Scale Production Platforms in Row Crop Agriculture

  Applied Engineering in Agriculture · 2026-01-01

  articleSenior author

  Highlights Small-scale autonomous systems significantly reduce labor requirements, needing only 20% of the operator time compared to conventional systems. The small-scale autonomy complement shows the lowest sensitivity to equipment investment costs from interest rates and maintains a low sensitivity to labor costs due to its lower capital investment and efficient acreage coverage. The small-scale autonomy complement consistently delivers the lowest equivalent annual cost per hectare across all scenarios. Savings from small-scale autonomous systems could fund additional autonomous equipment, potentially doubling acreage capacity while maintaining low labor needs. Abstract. The economic viability of small- and large-scale production platforms in row crop agriculture is increasingly vital as the industry seeks to enhance efficiency and profitability through automation. This study investigates the financial trade-offs of machinery complements tailored to different operational scales, with a focus on integrating supervised autonomy. A comprehensive economic model was developed to estimate the equivalent annual costs of small conventional systems, large conventional systems, small autonomous systems, and large autonomous row crop production systems in North American markets, accounting for key variables such as interest rates, labor rates, and energy costs, and the overall equipment cost sensitivity to these economic fluctuations. The analysis also evaluates potential cost savings from autonomy that could be reinvested to further enhance productivity and reduce reliance on manual labor. Findings reveal that the autonomous complement consistently achieves the lowest per-hectare costs and exhibits the least sensitivity to rising interest, labor, and fuel expenses, driven by its reduced labor requirements and efficient acreage coverage. The small autonomous equipment saves $30/ha over the small conventional equipment, the large conventional equipment by $49/ha, and the large autonomous equipment by $37/ha. In contrast, large-scale systems face greater cost impacts from interest rate increases, while small-scale systems are more vulnerable to labor cost fluctuations. These results underscore the economic advantages of autonomous systems, offering farmers actionable insights to achieve sustainable, cost-effective, and scalable agricultural practices. Keywords: Agricultural equipment, Autonomy, Economics, Machinery Selection.

  PublisherDOI

• Fuel Consumption Minimization Strategy For Electrified Agricultural Tractor

  IFAC-PapersOnLine · 2025-01-01 · 1 citations

  articleOpen accessSenior authorCorresponding

  Agricultural tractors contribute significantly to emissions, yet their electrification faces challenges due to their versatile operations and varying terrain conditions. This study explores a series hybrid range extender powertrain architecture to benefit from controlling the ICE operating points to optimize and minimize fuel consumption. A high-fidelity framework is developed that allows for exploring the benefits of the proposed powertrain architecture. A charge-sustaining energy management strategy (EMS) is implemented to optimize the power split between two power sources: (1) a Power Generation Unit (PGU) and (2) a battery pack. This strategy also allows the ICE to operate at the optimal operating points and prevent battery depletion throughout the tractor operation while minimizing fuel consumption. The initial results decrease the average fuel consumption rate by 50% compared to the baseline conventional powertrain for agricultural tractors, in addition to the ability to downsize the engine from 500 kW to 275 kW.

  PublisherDOI

• Development of a Subsurface Sensing Probe for Measurement of Nitrogen Oxide Gases

  Applied Engineering in Agriculture · 2025-01-01

  articleOpen access

  Highlights A novel 3D printed subsurface soil probe device integrated with NDIR N2O and electrochemical NOx sensors was designed. The performances of these sensors were calibrated using compressed gases and by applying inorganic nitrogen fertilizer to soil cores. The readings from the developed sensors closely matched the gas concentration profile measured by the commercially available Gasmet FTIR instrument for both N2O and NO. Results confirmed that the sensing devices can detect subsurface nitrogen oxide gases, presumably microbially induced, including the changes following the application of an inorganic fertilizer and an irrigation event. Abstract. The goal of this study was to develop a subsurface soil probe device that uses sensors for continuous measurement of nitrogen oxide gases (N 2 O and NO x =NO + NO 2 ) generated microbially within the soil. The polymer-based soil probe device is manufactured by 3D printing and consists of a perforated probe that penetrates the soil and an attached above-ground chamber that stores the sensors and the electronics. For N 2 O, we have developed a non-dispersive infrared sensor (NDIR), and for NO x , an electrochemical sensor. Both sensors can measure gases in the ppb-ppm range. The soil probe device was tested with soil cores. To validate the accuracy of the sensors, we used compressed gas tanks of NO and N 2 O to permeate these gases into the soil core. The compressed gases spread through a soil core profile, and the concentrations of the nitrogen oxide gases that diffused into the in-ground probe chamber were measured by the sensors. The sensor readings closely matched the gas concentration profiles measured by the commercial Gasmet instrument for both N 2 O and NO. Both N 2 O and NO x sensors were able to measure gas diffusing through the soil for sustained periods of time. To demonstrate the agricultural relevance of the device, we designed an experiment using soil cores in which the soil was treated with inorganic fertilizer followed by irrigation and then the subsequent evolution of nitrogen oxides was monitored over a period of days using both sensors. These trials illustrated that the in-ground probe chamber and respective sensing devices can measure subsurface nitrogen oxide gases that are microbially induced, including the changes that occur after the application of an inorganic fertilizer and an irrigation event. Keywords: Electrochemical sensor, Greenhouse gases, Nitrogen oxides, Nitrous oxide, Nondispersive infrared (NDIR) sensor, Potentiometric sensing, Soil microbiology.

  PublisherOA PDFDOI

• Supplemental figures for "Development of a Subsurface Sensing Probe for Measurement of Nitrogen Oxide Gases"

  2025-06-11

  preprintOpen access

  <p dir="ltr">This is the supplemental material document corresponding to the article entitled 'Development of a Subsurface Sensing Probe for Measurement of Nitrogen Oxide Gases'. The document primarily includes supplementary figures and plots that display gas concentration measurements for N2O and NO sensor devices during additional 14-day fertilizer trials. Also included are data plots regarding soil properties during all fertilizer trials.</p>

  PublisherDOI

• Supplemental figures for "Development of a Subsurface Sensing Probe for Measurement of Nitrogen Oxide Gases"

  2025-06-11

  preprintOpen access

  <p dir="ltr">This is the supplemental material document corresponding to the article entitled 'Development of a Subsurface Sensing Probe for Measurement of Nitrogen Oxide Gases'. The document primarily includes supplementary figures and plots that display gas concentration measurements for N2O and NO sensor devices during additional 14-day fertilizer trials. Also included are data plots regarding soil properties during all fertilizer trials.</p>

  PublisherDOI

• Cyberinfrastructure for machine learning applications in agriculture: experiences, analysis, and vision

  Frontiers in Artificial Intelligence · 2025-01-23 · 5 citations

  articleOpen access

  Introduction: Advancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. In many fields, including agriculture, this progress has outpaced the availability of sufficiently diverse and high-quality datasets, which now serve as a limiting factor. While many agricultural use cases appear feasible with current compute resources and ML algorithms, the lack of reusable hardware and software components, referred to as cyberinfrastructure (CI), for collecting, transmitting, cleaning, labeling, and training datasets is a major hindrance toward developing solutions to address agricultural use cases. This study focuses on addressing these challenges by exploring the collection, processing, and training of ML models using a multimodal dataset and providing a vision for agriculture-focused CI to accelerate innovation in the field. Methods: Data were collected during the 2023 growing season from three agricultural research locations across Ohio. The dataset includes 1 terabyte (TB) of multimodal data, comprising Unmanned Aerial System (UAS) imagery (RGB and multispectral), as well as soil and weather sensor data. The two primary crops studied were corn and soybean, which are the state's most widely cultivated crops. The data collected and processed from this study were used to train ML models to make predictions of crop growth stage, soil moisture, and final yield. Results: The exercise of processing this dataset resulted in four CI components that can be used to provide higher accuracy predictions in the agricultural domain. These components included (1) a UAS imagery pipeline that reduced processing time and improved image quality over standard methods, (2) a tabular data pipeline that aggregated data from multiple sources and temporal resolutions and aligned it with a common temporal resolution, (3) an approach to adapting the model architecture for a vision transformer (ViT) that incorporates agricultural domain expertise, and (4) a data visualization prototype that was used to identify outliers and improve trust in the data. Discussion: Further work will be aimed at maturing the CI components and implementing them on high performance computing (HPC). There are open questions as to how CI components like these can best be leveraged to serve the needs of the agricultural community to accelerate the development of ML applications in agriculture.

  PublisherOA PDFDOI

• Economics of Small- vs. Large-Scale Production Platformsin Row Crop Agriculture

  2025-01-01

  articleSenior author

  <b><sc>Abstract.</sc></b> The economic viability of small- and large-scale production platforms in row crop agriculture is increasingly vital as the industry seeks to enhance efficiency and profitability through automation. This study investigates the financial trade-offs of machinery complements tailored to different operational scales, with a focus on integrating supervised autonomy. A comprehensive economic model was developed to estimate the equivalent annual costs of small-scale, large-scale, and small-scale autonomous grain crop production systems, accounting for key variables such as interest rates, labor rates, and energy costs, and the overall equipment cost sensitivity to these economic fluctuations. The analysis also evaluates potential cost savings from autonomy that could be reinvested to further enhance productivity and reduce manual labor dependency. Findings reveal that the autonomous complement consistently achieves the lowest per-acre costs and exhibits the least sensitivity to rising interest, labor, and fuel expenses, driven by its reduced labor requirements and efficient acreage coverage. In contrast, large-scale systems face greater cost impacts from interest rate increases, while small-scale systems are more vulnerable to labor cost fluctuations. These results underscore the economic advantages of autonomous systems, offering farmers actionable insights for adopting advanced technologies to achieve sustainable, cost-effective, and scalable agricultural practices.

  PublisherDOI

• Understanding the limitations of grain yield monitor technology to inform on‐farm research

  Agronomy Journal · 2024-09-21 · 4 citations

  articleOpen access

  Abstract Yield monitoring technology (YM) is a valuable tool to evaluate crop performance in on‐farm research (OFR). However, limited information exists on utilizing this technology to accurately inform OFR. The objectives of this study were to evaluate the ability of grain yield monitor mass flow sensors to detect changes in corn ( Zea mays L.) yield for different plot lengths, provide a recommended minimum plot length to utilize YM in OFR, and determine if differences in estimating yield existed between YMs. Six treatment lengths that varied in distance of intentional yield differences (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) were created by alternating high‐yield (202 kg N/ha application) and low‐yield (0 kg N/ha application) plots. A total of four grain YMs with impact‐style mass flow sensors were used within two commercially available combines. Yield comparisons were made between the plot combine and YMs to evaluate the accuracy of each technology for detecting the magnitude of yield change across lengths using analysis of variance and exponential regression curves. Results indicated that the mass flow sensors were not sensitive enough to detect quickly changing flow rates for alternating yield changes in small plot lengths. Minimum plot lengths ranged from 43 to 107 m depending on YM. Significant differences were observed between grain YMs from different manufacturers. Future work could evaluate the influence additional crops or smaller yield differences have on the optimum OFR plot length.

  PublisherOA PDFDOI

• On-farm experimentation: assessing the effect of combine ground speed on grain yield monitor data estimates

  Precision Agriculture · 2024-12-14 · 1 citations

  articleOpen access

  On-farm experiments (OFE) typically do not account for limitations of grain yield monitors such as the dynamics of grain flow through a large combine. A common question asked within OFE is how ground speed impacts yield estimates from grain yield monitors. Therefore, the objective of this study was to determine if combine ground speed influences the ability of grain yield monitors to report yield differences for OFE. Six sub-plot treatment resolutions that differed in length (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) of imposed yield variation were harvested at combine ground speeds of 3.2 and 6.4 km h−1. Treatments were replicated 3 times. The intentional yield variability in maize (Zea mays L.) was created by alternating nitrogen application (0–202 kg N ha−1) across the treatment lengths. A factory installed yield monitor (YM3) and a third-party platform (P1) using the controller area network (CAN) bus data were used to collect yield data and compared to plot combine data collected from adjacent rows for each treatment length along a pass. Comparisons were made between each YM and plot combine yield estimates for each low and high yield treatment lengths. Combine ground speed did not significantly impact yield estimates (p ≥ 0.31 for all speed interactions) except speed * method due to lack of calibration. There were no significant differences the computed yield differences (all speed interactions p ≥ 0.40). Combine ground speed did not significantly influence the ability of yield monitoring technologies (i.e. mass flow sensor) to estimate the average low and high yields (p ≥ 0.31 for all speed interactions for individual plot lengths except when operating outside the calibrated flow range of the mass flow sensor. Operating outside the calibrated flow range of the mass flow sensor resulted in mass flow rate being overestimated by an average of 23% for both yield monitors (YM3 and P1).

  PublisherOA PDFDOI

• Precision of grain yield monitors for use in on-farm research strip trials

  Precision Agriculture · 2023-12-11 · 8 citations

  articleOpen access

  Abstract On-farm research (OFR) has become popular as a result of precision agriculture technology simplifying the process and farm software capabilities to summarize results collected through the technology. Different OFR designs exists with strip-trials being a simple approach to evaluate different treatments. Common in OFR is the use of yield monitors to collect crop performance data since yield represents a primary response variable in these type studies. The objective was to investigate the ability of grain yield monitoring technologies to accurately inform strip trials when frequent yield variability exists within an experimental unit. A combination of six sub-plot treatment resolutions (TR) that differed in length of imposed yield variation (7.6, 15.2, 30.5, 61.0, 121.9, and 243.8 m) were harvested at combine ground speeds of 3.2, 6.4, 7.2, and 8.1 kph, depending on study site (three study sites total). Intentional yield differences in maize ( Zea mays L. ) were created for each sub-plot by alternating the amount nitrogen (N) applied: 0 or 202 kg N/ha. Yield was measured by four commercially available yield monitoring (YM) technologies and a weigh wagon. Comparisons were made between the accumulated mass of the YM technology and weigh wagon through percent differences along with testing the significance of the plotted relationship between YM and weigh wagon. Results indicated that yield monitoring technology can be used to evaluate strip trial performance regardless of yield frequency and variability (error &lt; 3%) within an experimental unit when operating within the calibrated range of the mass flow sensor. Operating outside of the calibrated range of the mass flow sensor resulted in &gt; 15% error in estimating accumulated weight and overestimation of yield by 23%. Finally, no significant differences existed in estimating accumulated weight values between grain yield monitor technologies (all p-values ≥ 0.54).

  PublisherOA PDFDOI

Frequent coauthors

• T. S. Stombaugh

  40 shared

• Joe D. Luck

  31 shared

• John P. Fulton

  The Ohio State University

  25 shared

• Santosh K. Pitla

  University of Nebraska–Lincoln

  24 shared

• John P Fulton

  Brown University

  23 shared

• Carl R. Dillon

  18 shared

• Rodrigo Sinaidi Zandonadi

  Universidade Federal de Mato Grosso

  14 shared

• Tom Mueller

  14 shared

Education

• Ph.D., Food, Agricultural and Biological Engineering

  The Ohio State University

  1990

• M.S., Food, Agricultural and Biological Engineering

  The Ohio State University

  1986

• B.S., Food, Agricultural and Biological Engineering

  The Ohio State University

  1984

Awards & honors

• Fellow of the American Society of Agricultural and Biologica…