Proceedings
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| Filter results10 paper(s) found. |
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1. Sensor-based Variable-rate N on Corn Reduced Nitrous Oxide EmissionsMore nitrogen fertilizer is applied to corn than to all other U.S. crops combined, contributing to atmospheric heat trapping when nitrous oxide is produced. Higher nitrogen rate is well known to increase nitrous oxide emissions, and earlier N application time may increase the window during which nitrous oxide can form. An experiment was initiated in 2012 comparing nitrogen management and drainage effects on corn yield and nitrous oxide emissions. Two nitrogen treatments... P. Scharf |
2. Aerial Photographs to Predict Yield Loss Due to N Deficiency in CornNitrogen fertilizer is a crucial input for corn production, and in the U.S. more nitrogen is applied to corn than to all other crops combined. In wet weather, nitrogen can be lost from soil by leaching and by denitrification. Which process predominates depends largely on soil drainage. Nitrogen deficiency in nearly any plant is expressed by a lighter green color of leaves than in nitrogen-sufficient plants. Nitrogen deficiency in corn can be easily seen from the air. ... P. Scharf |
3. Sensor-based Nitrogen Applications Out-performed Producer-chosen Rates for Corn in On-farm DemonstrationsOptimal nitrogen fertilizer rate for corn can vary substantially within and among fields. Current N management practices do not address this variability. Crop reflectance sensors offer the potential to diagnose crop N need and control N application rates at a fine spatial scale. Our objective was to evaluate the performance of sensor-based variable-rate N applications to corn, relative to constant N rates chosen by the producer. Fifty-five replicated on-farm demonstrations... P. Scharf, K. Shannon, K. Sudduth, N. Kitchen |
4. Modifying the University of Missouri Corn Canopy Sensor Algorithm Using Soil and Weather InformationCorn production across the U.S. Corn belt can be often limited by the loss of nitrogen (N) due to leaching, volatilization and denitrification. The use of canopy sensors for making in-season N fertilizer applications has been proven effective in matching plant N requirements with periods of rapid N uptake (V7-V11), reducing the amount of N lost to these processes. However, N recommendation algorithms used in conjunction with canopy sensor measurements have not proven accurate in making N recommendations... G. Bean, N.R. Kitchen, D.W. Franzen, R.J. Miles, C. Ransom, P. Scharf, J. Camberato, P. Carter, R.B. Ferguson, F. Fernandez, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan |
5. An Efficient Data Warehouse for Crop Yield PredictionNowadays, precision agriculture combined with modern information and communications technologies, is becoming more common in agricultural activities such as automated irrigation systems, precision planting, variable rate applications of nutrients and pesticides, and agricultural decision support systems. In the latter, crop management data analysis, based on machine learning and data mining, focuses mainly on how to efficiently forecast and improve crop yield. In recent years, raw and semi-processed... V.M. Ngo, N. Le-khac, M. Kechadi |
6. Suitability of ML Algorithms to Predict Wild Blueberry Harvesting LossesThe production of wild blueberries (Vaccinium angustifolium.) is contributing 112.2 million dollars to the Canada’s revenue which can be further increased through controlling harvest losses. A precise prediction of blueberry harvesting losses is necessary to mitigate such losses. In this study, the performance of three machine learning (ML) models was evaluated to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada were... H. Khan, T. Esau, A. Farooque, F. Abbas |
7. Comparing Profitability of Variable Rate Nitrogen Prescription MethodsVariable rate nitrogen (VRN) prescriptions have been field-tested against uniform N application for over 25 years. VRN prescription algorithms vary in the type and cost of information they require. To date, few studies have compared the benefits and costs of alternative VRN prescription methods. VRN prescriptions draw on diverse information, including soil and tissue N sampling, yield history (YH), and remotely sensed spectral reflectance (such as the Normalized Difference... S. Lee, S.M. Swinton |
8. Opportunity Cost of Precision ConservationCrop production and biodiversity conservation vie for limited agricultural land resources. While biodiversity conservation benefits society as a whole, it is farmers who bear the immediate economic consequences of shifting land from agricultural to conservation use. When parts of a field are put into conservation use, farmers give up the net revenue that they earned from crop production, accepting the “opportunity cost” of losing that revenue stream. But since crop yields are... S. Lee, S.M. Swinton |
9. Ground-based Imagery Data Collection of Cotton Using a Robotic PlatformIn modern agriculture, technological advancements are pivotal in optimizing crop production and resource management. Integrating robotics and image processing techniques allows the efficient collection, analysis, and storage of high-resolution images crucial for monitoring crop health, identifying pest infestations, assessing growth stages, making precise management decisions and predicting yield potential. The objective of this project is to utilize the Farm-NG Amiga robot to develop an image... O. Fernandez, M. Bhandari, J.L. Landivar-scoot, M. Eldefrawy, L. Zhao, J. Landivar |
10. Cotton Yield Estimation Using High-resolution Satellite Imagery Obtained from Planet SkySatSatellite images have been used to monitor and estimate crop yield. Over the years, significant improvements on spatial resolution have been made where ortho images can be generated at 30-centimeter resolution. In this study, we wanted to explore the potential use of Planet SKYSAT satellite system for cotton yield predictions. This system provided imagery data at 50 centimeters resolution, and we collected data 14 times during the season. The data were collected from two different cotton... M. Bhandari |