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 | 122 | 2014 |
Crop stand analysis JD Johnson, TJ Nigon, M Abouali US Patent 9,489,576, 2016 | 94 | 2016 |
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 | 93 | 2015 |
Nitrogen status determination in growing crops JD Johnson, TJ Nigon US Patent App. 14/637,588, 2015 | 89 | 2015 |
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 | 81 | 2021 |
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 | 55 | 2015 |
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 | 37 | 2014 |
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 | 34 | 2020 |
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 | 17 | 2019 |
Aerial imagery and other non-invasive approaches to detect nitrogen and water stress in a potato crop TJ Nigon University of Minnesota, 2012 | 9 | 2012 |
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 | 5 | 2018 |
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 | 4 | 2021 |
Fusion of hyperspectral and thermal images for evaluating nitrogen and water status in potato fields for variable rate application Y Cohen | 4 | 2013 |
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 | 3 | 2012 |
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 | 2 | 2012 |
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 | 1 | 2020 |
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 |