Proceedings
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| Filter results8 paper(s) found. |
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1. Rapid Data Acquisition For In-Field Plant PhenomicsHigh throughput sensing is necessary for the rapid acquisition of plant canopy physical and physiological parameters on field scales. Simultaneous measures of these descriptive parameters will provide a clearer picture of plant response to biotic and abiotic stressors. Information obtained can assist in early identification of desired genetic traits and the degree to which they are expressed. Identifying these traits and their expression can provide higher efficiency in genetic selection... M.R. Sclemmer, K.H. Holland |
2. Development of a Crop Edge Line Detection Algorithm Using a Laser Scanner for an Autonomous Combine HarvesterThe high cost of real-time kinematic (RTK) differential GPS units required for autonomous guidance of agricultural machinery has limited their use in practical auto-guided systems especially applicable to small-sized farming conditions. A laser range finder (LRF) scanner system with a pan-tilt unit (PTU) has the ability to create a 3D profile of objects with a high level of accuracy by scanning their surroundings in a fan shape based on the time-of-flight measurement principle. This paper describes... C. Jeon, H. Kim, X. Han, H. Moon |
3. Estimating Water and Nitrogen Deficiency in Corn Using a Multi-parameter Proximal SensorThe Crop Circle Phenom (CCP) is an innovative integrated proximal sensor that can be potentially used to perform in-season diagnosis of nitrogen and water status. In addition to measuring spectral reflectance in several bands including the red, red edge, and near-infrared wavelengths, the CCP can also measure canopy and air temperatures and provides several parameters that can be associated with chlorophyll content, crop vigor, and water status. These capabilities differentiate the CCP from other... L. Lacerda, Y. Miao, V. Sharma, A. E. Flores, A. Kechchour, J. Lu |
4. In-season Diagnosis of Corn Nitrogen and Water Status Using UAV Multispectral and Thermal Remote SensingFor irrigated corn fields, how to optimize nitrogen (N) and irrigation simultaneously is a great challenge. A promising strategy is to use remote sensing to diagnose corn N and water status during the growing season, which can then be used to guide in-season variable rate N application and irrigation management. The objective of this study was to evaluate the effectiveness of UAV multispectral and thermal remote sensing in simultaneous diagnosis of corn N and water status. Two field experiments... Y. Miao, A. Kechchour, V. Sharma, A. Flores, L. Lacerda, K. Mizuta, J. Lu, Y. Huang |
5. On-farm Evaluation of the Potential Benefits of Variable Rate Seeding for Corn in MinnesotaMany farmers in Minnesota are interested in adopting variable rate seeding technology for corn, however, little has been reported about their potential benefits. The objectives of this study were to 1) determine within-field variability of optimal seeding rates, and 2) evaluate the potential benefits of variable rate seeding in commercial corn fields in Minnesota. Four on-farm variable rate seeding trials were conducted in Minnesota in 2022 and 2023, with seeding rates ranging from 31,000 to 41,000... Y. Miao, A. Kechchour, S. Folle, K. Mizuta |
6. Affordable Telematics System for Recording and Monitoring Operational Data in Crop FarmingThe aim of this research was to create an affordable telematics system for agricultural tractors for enhancing existing data logging capabilities. This system enables real-time transmission of operational data from the tractor's CAN bus to a server for storage, monitoring, and further analysis. By leveraging standardized communication protocols like ISO 11783 and J1939, operational data such as fuel consumption and engine load can be easily monitored. The system was built around a Raspberry... A. Lajunen, H. Hovio |
7. Evaluating On-Farm Variable Rate Seeding Trials with Causal Inference and Machine Learning.Identifying field-specific economically optimal seeding rates (EOSR) is central to profitable crop production, yet conventional analytical approaches applied to on-farm trial data frequently conflate association with causation, limiting their utility for generating actionable site-specific management recommendations and constraining broader adoption of variable rate seeding (VRS) technology. Mixed-model ANOVA and quadratic response surface methods calculate a single average EOSR, masking spatial... B. Adeyemi, Y. Miao, A. Kechchour |
8. Improving In-season Corn Nitrogen Status Prediction using Satellite Remote Sensing and Foundation Models with Agronomic ConstraintsPrecision nitrogen (N) management in maize requires in-season estimates of crop nitrogen status that are both accurate and physiologically credible, yet agronomic training data are often limited because destructive sampling is expensive and spatially sparse. Mechanistic crop models respect physiology but require extensive calibration and are computationally costly at field scale, whereas purely data-driven machine learning using remote sensing can achieve good accuracy while producing implausible... A. Kechchour, Y. Miao, V. Sharma, D. Mulla |