Proceedings
Topics
| Filter results17 paper(s) found. |
|---|
1. A Precise Fruit Inspection System for Huanglongbing and Other Common Citrus Defects Using GPU and Deep Learning TechnologiesWorld climate change and extreme weather conditions can generate uncertainties in crop production by increasing plant diseases and having significant impacts on crop yield loss. To enable precision agriculture technology in Florida’s citrus industry, a machine vision system was developed to identify common citrus production problems such as Huanglongbing (HLB), rust mite and wind scar. Objectives of this article were 1) to develop a simultaneous image acquisition system using multiple c... D. Choi, W. Lee, J.K. Schueller, R. Ehsani, F.M. Roka, M.A. Ritenour |
2. Development of a Multiband Sensor for Citrus Black Spot Disease DetectionCitrus black spot (CBS), or Guignardia citricarpa, is known as the most destroying citrus fungal disease worldwide. CBS causes yield loss as a result of early fruit drop, and it leaves severely blemished and unmarketable fruit. While leaves usually remain symptomless, CBS generates various forms of lesions on citrus fruits including hard spot, cracked spot, and virulent spot. CBS lesions often appear on maturing fruit, starting two months before maturity. Warm temperature and sunlight exposur... A. Pourreza, W. Lee, J. Lu, P. Roberts |
3. Sensor-based Technologies for Improving Water and Nitrogen Use EfficiencyLimited reports exist on identifying the empirical relationships between plant nitrogen and water status with hyperspectral reflectance. This project is aiming to develop effective system for nitrogen and water management in wheat. Specifically: 1) To evaluate the effects of nitrogen rates and irrigation treatments on wheat plant growth and yield; 2) To develop methods to predict yield and grain protein content in varying nitrogen and water environments, and to determine the minimum nit... O.S. Walsh, K. Belmont, J. Mcclintick-chess |
4. Development of a Multispectral Sensor for Crop Canopy Temperature MeasurementQuantifying spatial and temporal variability in plant stress has precision agriculture applications in controlling variable rate irrigation and variable rate nutrient application. One approach to plant stress detection is crop canopy temperature measurement by the use of thermographic or radiometric methods, generally in the long wave infrared (LWIR) wavelength range. A confounding factor in LWIR canopy temperature estimation is eliminating the effect of the soil background in the image. One ... P. Drew, K.A. Sudduth, E. Sadler |
5. Prediction of Sugarcane Yields in Commercial Fields by Early Measurements with an Optical Crop Canopy SensorAs a grass (Poaceae), sugarcane needs supplemental mineral nitrogen (N) to achieve high yields on commercial production areas. In Brazil, N recommendations for sugarcane ratoons are based on expected yield and the results of N response trials, as soil N analyses are not a suitable basis for decisions on optimum N fertilizer rates under tropical conditions. Since the vegetative parts in sugarcane are harvested, yield components such as the number of stalks and stalk height are directly correla... G. Portz, J. Jasper, J.P. Molin |
6. Field-scale Nitrogen Recommendation Tools for Improving a Canopy Reflectance Sensor AlgorithmNitrogen (N) rate recommendation tools are utilized to help producers maximize grain yield production. Many of these tools provide recommendations at field scales but often fail when corn N requirements are variable across the field. This may result in excess N being lost to the environment or producers receiving decreased economic returns on yield. Canopy reflectance sensors are capable of capturing within-field variability, although the sensor algorithm recommendations may not always be as ... C.J. Ransom, M. Bean, N. Kitchen, J. Camberato, P. Carter, R. Ferguson, F. Fernandez, D. Franzen, C. Laboski, E. Nafziger, J. Sawyer, J. Shanahan |
7. Active and Passive Crop Canopy Sensors As Tools for Nitrogen Management in CornThe objectives of this research were to (i) assess the correlation between active and passive crop canopy sensors’ vegetation indices at different corn growth stages and (ii) assess sidedress variable rate nitrogen (N) recommendation accuracy of active and passive sensors compared to the agronomic optimum N rate (AONR). The experiment was conducted near Central City, Nebraska on a Novina sandy loam planted to corn on 15 April 2015. The experiment was a randomized complete-block design w... L. Bastos, R. Ferguson |
8. 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 demonstrat... P. Scharf, K. Shannon, K. Sudduth, N. Kitchen |
9. Liquid Flow Control Requirements for Crop Canopy Sensor-Based N Management in Corn: A Project SENSE Case StudyWhile on-farm adoption of crop canopy sensors for directing in-season nitrogen (N) application has been slow, research focused on these systems has been significant for decades. Much emphasis has been placed on developing and testing algorithms based on sensor output to predict N needs, but little information has been published regarding liquid flow control requirements on equipment used in conjunction with these sensing systems. Addition of a sensor-based system to a standard spray rate cont... J. Luck, J. Parrish, L. Thompson, B. Krienke, K. Glewen, R.B. Ferguson |
10. Crop Modeling-based Framework to Explore Region-specific Impact of Nitrogen Fertilizer Management on Productivity and Environmental FootprintTo maintain current crop production while reducing negative environmental impacts, improved understanding of the relative impact of the 4Rs for nitrogen (N) management (rate, time, place, and source) for a given geo-agroecosystem are needed and can play a critical role in driving policy, recommendations, and local practices. However, the timeframe and cost required to assess and characterize the impact of N rate and timing over years and weather conditions through field experiments is prohibi... L. Thompson, S. Archontoulis, P. Grassini, L. Puntel, T. Mieno |
11. Development of Standard Protocols for Soil Tilth Assessment As an Essential Component of Tillage Tool Automation to Improve Soil HealthThe accurate assessment of soil tilth may be pivotal when assessing soil health as part of a holistic process to ensure sustainable and profitable crop production practices. In this study, we focus on demonstrating methodologies for the spatial assessment of soil tilth as ground truth for assessing real-time soil tilth quality sensing technologies. The proposed methodologies for evaluating tillage effects involve the integration of the line transect method for residue distribution analysis. S... C. Dean, A. Klopfenstein, A. Klopfenstein, S.A. Shearer |
12. Optimizing Corn Irrigation Strategies: Insights from NDVI Trends, Soil Moisture Dynamics, and Remote SensingThis comprehensive field experiment systematically examines the impact of varied irrigation rates on corn growth and yield across three treatments: 33%, 67%, and 100% irrigation rates. Utilizing the normalized difference vegetation index (NDVI) as a parameter for vegetation health, distinct patterns emerge throughout key growth stages. The 100% irrigation treatment consistently exhibits superior vegetation health, sustaining higher NDVI values across all stages, while the 33% treatment reveal... J.O. Abon, A. Sharda |
13. Hyperspectral Sensing to Estimate Soil Nitrogen and Reduce Soil Sampling IntensityRecognizing soil's critical role in agriculture, swift and accurate quantification of soil components, specifically nitrogen, becomes paramount for effective field management. Traditional laboratory methods are time-consuming, prone to errors, and require hazardous chemicals. Consequently, this research advocates the use of non-imaging hyperspectral data and VIS-NIR spectroscopy as a safer, quicker, and more efficient alternative. These methods take into account various soil components, i... W.A. Admasu, D. Mandal, R. Khosla |
14. Changes in Soil Chemical and Physical Properties After a Flooding Event in ChileDuring the winter of 2023, ridges were made to plant French prunes (Prunus domestica). After building the ridges, the soil was surveyed using gamma radiation technology (SoilOptix technologies, Ontario, CA). Due to the intense rains that occurred at the end of august 2023, the Cachapoal River, the main water supply of the O’Higgins region, left its course and flooded several fields, including the one where the ridges had been built, destroying them. Ridges were washed out... R.A. Ortega, H.P. Poblete |
15. Extension Program Prioritization Guides Web-mapping Application Delivery to RanchersCooperative Extension has a long history of helping agricultural producers address their current needs and emerging public issues; often through training in the use of technologies that are not yet widely adopted. The quality of geospatial data and tools to visualize and analyze that data continues to improve. However, barriers exist to rancher adoption of geospatial decision support tools. These barriers can include costs, ease of use, and privacy concerns. The sustainability of beef ca... W. Boyer |
16. Fertigation Management Strategies Effect on Residual Nitrates in the Soil Profile and Ground WaterNitrogen is an input that is vital for growth and productivity within the corn belt states of the U.S. However, when nitrogen as an input into agricultural cropping systems is often over-applied and thus not optimally utilized by the cropping system. Therefore, it is at risk of loss within the environment through processes of leaching, denitrification, and volatilization. This is a major concern in Nebraska, as the reality is that much of the state’s groundwater has been contaminated wi... K.J. Bathke, T. Cross, J.D. Luck |
17. Integrating Collected Field Machine Vibration Data with Machine Learning for Enhanced Precision in Agricultural OperationsIn this research, we provide an innovative combination of the Agricultural Vibration Data Acquisition Platform (avDAQ) with cutting-edge machine learning methods for data collecting from agricultural machinery. The avDAQ system, which has a strong connection to a GPS sensor, provides precise spatial information to the vibration data that has been collected, providing an in-depth explanation of the locations of the vibrations. The objective is to fully utilize avDAQ's potential to extract ... S. Janbazialamdari, E. Brokesh |