Proceedings
Authors
| Filter results4 paper(s) found. |
|---|
1. Developing an Integrated Rice Management System for Improved Yield and Nitrogen Use Efficiency in Northeast China... G. Zhao, Y. Miao, F. Zhang, M. Fan |
2. Precision Design Of Vegetative BuffersPrecision agriculture techniques can be applied at field margins to improve performance of water quality protection practices. Effectiveness of vegetative buffers, conventionally designed to have uniform width along field margins, is limited by spatially non-uniform runoff from fields. Effectiveness can be improved by placing relatively wider buffer at locations where loads are greater. A GIS tool was developed that accounts for non-uniform flow and produces more-effective, variable-width,... T. Mueller, S. Neelakantan, M. Helmers, M. Dosskey |
3. Utilizing Hyperspectral Field Imagery for Accurate Southern Leaf Blight Severity Grading in CornCrop disease detection using traditional scouting and visual inspection approaches can be laborious and time-consuming. Timely detection of disease and its severity over large spatial regions is critical for minimizing significant yield losses. Hyperspectral imagery has been demonstrated as a useful tool for a broad assessment of crop health. The use of spectral bands from hyperspectral data to predict disease severity and progression has been shown to have the capability of enhancing early... G. Vincent, M. Kudenov, P. Balint-kurti, R. Dean, C.M. Williams |
4. Automated Southern Leaf Blight Severity Grading of Corn Leaves in RGB Field ImageryPlant stress phenotyping research has progressively addressed approaches for stress quantification. Deep learning techniques provide a means to develop objective and automated methods for identifying abiotic and biotic stress experienced in an uncontrolled environment by plants comparable to the traditional visual assessment conducted by an expert rater. This work demonstrates a computational pipeline capable of estimating the disease severity caused by southern corn leaf blight in images of field-grown... C. Ottley, M. Kudenov, P. Balint-kurti, R. Dean, C. Williams |