000			02627nab a22003857a 4500
999			_c62023 _d62015
001			62023
003			MX-TxCIM
005			20251010172732.0
008			200602s2020 ne |||p|op||| 00| 0 eng d
022			_a0378-4290
024	8		_ahttps://doi.org/10.1016/j.fcr.2020.107793
040			_aMX-TxCIM
041			_aeng
100	1		_913778 _aVelumani, K.
245	1	3	_aAn automatic method based on daily in situ images and deep learning to date wheat heading stage
260			_aAmsterdam (Netherlands) : _bElsevier, _c2020.
500			_aPeer review
520			_aAccurate and timely observations of wheat phenology and, particularly, of heading date are instrumental for many scientific and technical domains such as wheat ecophysiology, crop breeding, crop management or precision agriculture. Visual annotation of the heading date in situ is a labour-intensive task that may become prohibitive in scientific and technical activities where high-throughput is needed. This study presents an automatic method to estimate wheat heading date from a series of daily images acquired by a fixed RGB camera in the field. A convolutional neural network (CNN) is trained to identify the presence of spikes in small patches. The heading date is then estimated from the dynamics of the spike presence in the patches over time. The method is applied and validated over a large set of 47 experimental sites located in different regions in France, covering three years with nine wheat cultivars. Results show that our method provides good estimates of the heading dates with a root mean square error close to 2 days when compared to the visual scoring from experts. It outperforms the predictions of a phenological model based on the ARCWHEAT crop model calibrated for our local conditions. The potentials and limits of the proposed methodology towards a possible operational implementation in agronomic applications and decision support systems are finally further discussed.
546			_aText in English
650		7	_2AGROVOC _94770 _aPhenology
650		7	_2AGROVOC _94872 _aInternet
650		7	_2AGROVOC _99056 _aNeural Networks
650		7	_2AGROVOC _92530 _aSensors
650		7	_2AGROVOC _911710 _aModelling
700			_910190 _aMadec, S.
700			_aDe Solan, B. _910072
700	1		_913779 _aLopez-Lozano, R.
700	1		_913780 _aGillet, J.
700	1		_913781 _aLabrosse, J.
700	1		_913782 _aJezequel, S.
700	1		_910054 _aComar, A.
700	1		_910106 _aBaret, F.
773	0		_dAmsterdam (Netherlands) : Elsevier, 2020. _gv. 252, art. 107793 _tField Crops Research _x0378-4290 _wu444314
942			_2ddc _cJA _n0