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| Filter results4 paper(s) found. |
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1. Development Of A System For Site-specific Nematicide Placement In CottonNematode distribution varies significantly in cotton fields. Population density throughout a field is highly correlated to soil texture. Field-wide application of a uniform nematicide rate results in the chemical being applied to areas without nematodes or where nematode densities are below an economic threshold, or the application of sub-effective levels in areas with high nematode densities. The investigators have developed a “Site- Specific Nematicide Placement”... A. Khalilian, W. Henderson, J. Mueller, T. Kirkpatrick, S. Monfort, C. Overstreet |
2. The Daily Erosion Project - High Resolution, Daily Estimates of Runoff, Detachment, Erosion, and Soil MoistureRunoff and sediment transport from agricultural uplands are substantial threats to water quality and sustained crop production. Farmers, conservationists, and policy makers must understand how landforms, soil types, farming practices, and rainfall affect soil erosion and runoff in order to improve management of soil and water resources. A system was designed and implemented a decade ago to inventory precipitation, runoff, and soil erosion across the state of Iowa, United States. That system utilized... B.K. Gelder, R. Cruse, D. James, D. Herzmann, C. Sandoval-green, T. Sklenar |
3. Thermal Characterization and Spatial Analysis of Water Stress in Cotton (Gossypium Hirsutum L.) and Phytochemical Composition Related to Water Stress in Soybean (Glycine Max)Studies were designed to explore spatial relationships of water and/or heat stress in cotton and soybeans and to assess factors that may influence yield potential. Investigations focused on detecting the onset of water/heat stress in row crops using thermal and multispectral imagery with ancillary physicochemical data such as soil moisture status and photosynthetic pigment concentrations. One cotton field with gradations in soil texture showed distinct patterns in thermal imagery, matching patterns... S.J. Thomson, S.L. Defauw, P.J. English, J.E. Hanks, D.K. Fisher, P.N. Foster, P.V. Zimba |
4. Scaling Up Window-based Regression for Crop-row DetectionCrop-row detection is a central element of weed detection and agricultural image processing tasks. With the increased availability of high-resolution imagery, a precise locating of crop rows is becoming practical in the sense that the necessary data are commonly available. However, conventional image processing techniques often fail to scale up to the data volumes and processing time expectations. We present an approach that computes regression lines over... A.M. Denton, G.E. Hokanson, P. Flores |