000			02123nam a22003135a 4500
001			125-2020
003			CL-ChUAC
005			20210609150947.0
006			m o d |
007			cr cn|||||||||
008			210609s2020 ne |||||s|||| 000 ||eng d
022			_a0168-1699
040			_aCL-ChUAC _bspa _cCL-ChUAC
041			_aeng _beng _feng
100	1		_aQuiroza, Ignacio A. _eautor
245	1	0	_aImage recognition of Legacy blueberries in a Chilean smart farm through deep learning _cA. Quiroza, Ignacio ; H. Alférez, Germán
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
520	3		_aAgriculture is one of the most important pillars of development in Chile. However, it is expected that around the year 2030 there is going to be a gradual decrease in the number of farmers. Therefore, it is necessary to replace this workforce with technology and mechanization. One way to do this is through smart farms to leverage agricultural production. The contribution of this research work is a novel approach for deep-learning image recognition of Legacy blueberries in the rooting stage that can be used in smart farms in Chile. Legacy blueberry is a variety of Southern Highbush blueberry. This species constitutes 80% of the blueberry crops in Chile. Specifically, we propose an image recognition approach based on a convolutional neural network (CNN) to detect the presence of trays with living blueberry plants, the presence of trays without living plants, and the absence of trays. The average results of the evaluation of the predictive model are as follows: accuracy: 86%, precision: 86%, recall: 88%, and F1 score: 86%.
650		4	_aConvolutional neural networks
650		4	_aSmart farms
650		4	_aImage recognition
650		4	_aLegacy blueberry
700	1		_aAlférez, Germán H. _ecoautor
773	0		_dÁmsterdam, Países Bajos _gVolume 168, January 2020, 105044 _tComputers and Electronics in Agriculture [artículo de revista]
856	4	1	_uhttp://bibliorepositorio.unach.cl/handle/BibUnACh/1794
942			_2ddc _cAREV
999			_c2366425 _d2366425