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Tyler Nigon
Tyler Nigon
Graduate Research Assistant, University of Minnesota
Verifierad e-postadress på umn.edu - Startsida
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Crop water stress index derived from multi-year ground and aerial thermal images as an indicator of potato water status
R Rud, Y Cohen, V Alchanatis, A Levi, R Brikman, C Shenderey, B Heuer, ...
Precision Agriculture 15, 273-289, 2014
1222014
Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars
TJ Nigon, DJ Mulla, CJ Rosen, Y Cohen, V Alchanatis, J Knight, R Rud
Computers and Electronics in Agriculture 112, 36-46, 2015
942015
Crop stand analysis
JD Johnson, TJ Nigon, M Abouali
US Patent 9,489,576, 2016
882016
Nitrogen status determination in growing crops
JD Johnson, TJ Nigon
US Patent App. 14/637,588, 2015
872015
Estimation of corn yield based on hyperspectral imagery and convolutional neural network
W Yang, T Nigon, Z Hao, GD Paiao, FG Fernández, D Mulla, C Yang
Computers and Electronics in Agriculture 184, 106092, 2021
762021
Adaptation of water and nitrogen management to future climates for sustaining potato yield in Minnesota: Field and simulation study
BB Vashisht, T Nigon, DJ Mulla, C Rosen, H Xu, T Twine, SK Jalota
Agricultural Water Management 152, 198-206, 2015
552015
Evaluation of the nitrogen sufficiency index for use with high resolution, broadband aerial imagery in a commercial potato field
TJ Nigon, DJ Mulla, CJ Rosen, Y Cohen, V Alchanatis, R Rud
Precision Agriculture 15, 202-226, 2014
372014
Prediction of early season nitrogen uptake in maize using high-resolution aerial hyperspectral imagery
TJ Nigon, C Yang, G Dias Paiao, DJ Mulla, JF Knight, FG Fernández
Remote sensing 12 (8), 1234, 2020
342020
Computing uncertainty in the optimum nitrogen rate using a generalized cost function
TJ Nigon, C Yang, DJ Mulla, DE Kaiser
Computers and electronics in agriculture 167, 105030, 2019
172019
Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop
TJ Nigon
University of Minnesota, 2012
92012
Case study comparing machine learning and vegetation indices for assessing corn nitrogen status in an agricultural field in Minnesota
A Laacouri, T Nigon, D Mulla, C Yang
Proceedings 15th International Conference on Precision Agriculture, 2018
52018
The influence of aerial hyperspectral image processing workflow on nitrogen uptake prediction accuracy in maize
T Nigon, GD Paiao, DJ Mulla, FG Fernández, C Yang
Remote Sensing 14 (1), 132, 2021
42021
Fusion of hyperspectral and thermal images for evaluating nitrogen and water status in potato fields for variable rate application
Y Cohen
42013
Hyperspectral imagery for the detection of nitrogen stress in potato for in-season management
T Nigon, C Rosen, D Mulla, Y Cohen, V Alchanatis, R Rud
Proceedings of the 11th International Conference on Precision Agriculture …, 2012
32012
Evaluating water status in potato fields using combined information from RGB and thermal aerial images
R Rud, Y Cohen, V Alchanatis, A Cohen, M Sprintsin, A Levi, R Brikman, ...
Proceedings of 10th ICPA, ISPA, Monticello, Il, USA. CD-ROM, 2012
22012
Estimating greensnap yield damage with crop canopy reflectance: A case study
GD Paiao, FG Fernandez, TJ Nigon, C Cummings, SL Naeve
ASA, CSSA and SSSA International Annual Meetings (2020)| VIRTUAL, 2020
12020
Methods for Effective Spectral Feature Engineering in Yield Prediction with Limited Data Capture
M Kivi, T Nigon
ASA, CSSA, SSSA International Annual Meeting, 2023
2023
Selecting Representative Image Data for Plant Counting Using Self-Supervised Networks in Drone Imagery
E Imhoff, D Zermas, T Nigon
ASA, CSSA, SSSA International Annual Meeting, 2023
2023
The Art of Agronomic Feature Engineering: From Data Capture to ML-Ready Data.
T Nigon, B Bohman, L Nieto
ASA, CSSA, SSSA International Annual Meeting, 2023
2023
Uncertainty in Economic Optimum Nitrogen Rate and Accuracy of Drone Hyperspectral Imaging for Precision Nitrogen Management in Maize
TJ Nigon
University of Minnesota, 2021
2021
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Artiklar 1–20