000			02645nab|a22004097a|4500
001			67170
003			MX-TxCIM
005			20240919021234.0
008			20241s2024||||mx |||p|op||||00||0|eng|d
022			_a2072-4292 (Online)
024	8		_ahttps://doi.org/10.3390/rs16020282
040			_aMX-TxCIM
041			_aeng
100	1		_aVictor, B. _933127
245	1	0	_aHigh-throughput plot-level quantitative phenotyping using convolutional neural networks on very high-resolution satellite images
260			_bMDPI, _c2024. _aBasel (Switzerland) :
500			_aPeer review
500			_aOpen Access
520			_aTo ensure global food security, crop breeders conduct extensive trials across various locations to discover new crop varieties that grow more robustly, have higher yields, and are resilient to local stress factors. These trials consist of thousands of plots, each containing a unique crop variety monitored at intervals during the growing season, requiring considerable manual effort. In this study, we combined satellite imagery and deep learning techniques to automatically collect plot-level phenotypes from plant breeding trials in South Australia and Sonora, Mexico. We implemented two novel methods, utilising state-of-the-art computer vision architectures, to predict plot-level phenotypes: flowering, canopy cover, greenness, height, biomass, and normalised difference vegetation index (NDVI). The first approach uses a classification model to predict for just the centred plot. The second approach predicts per-pixel and then aggregates predictions to determine a value per-plot. Using a modified ResNet18 model to predict the centred plot was found to be the most effective method. These results highlight the exciting potential for improving crop trials with remote sensing and machine learning.
546			_aText in English
650		7	_aAgriculture _2AGROVOC _91007
650		7	_aImage analysis _2AGROVOC _96509
650		7	_aPlant breeding _gAGROVOC _2 _91203
650		7	_aRemote sensing _2AGROVOC _91986
650		7	_aMachine learning _2AGROVOC _911127
700	1		_aNibali, A. _933128
700	1		_aNewman, S.J. _933129
700	1		_aCoram, T. _917729
700	1		_aPinto Espinosa, F. _8I1707012 _94431 _gFormerly Global Wheat Program
700	1		_aReynolds, M.P. _8INT1511 _9831 _gGlobal Wheat Program
700	1		_aFurbank, R.T. _98940
700	0		_aZhen He _910981
773	0		_tRemote Sensing _gv. 16, no. 2, art. 282 _dBasel (Switzerland) : MDPI, 2024. _wu57403 _x2072-4292
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/23043
942			_cJA _n0 _2ddc
999			_c67170 _d67162