1 |
Moon, T.W., D.H. Jung, S.H. Chang, and J.E. Son. 2018. Estimation of greenhouse concentration via an artificial neural network that uses environmental factors. Hortic. Environ. Biotechnol. 59:45-50.
DOI
|
2 |
Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. 2011. Scikit-learn: Machine learning in python. JMLR. 12:2825-2830
|
3 |
Pieters, J. G., J. M. Deltour, and M. J. Debruyckere. 1997. Light transmission through condensation on glass and polyethylene. Agric. Forest Meteorol. 85:51-62.
DOI
|
4 |
Seginer, I. 1997. Some artificial neural network applications to greenhouse environmental control. Comput. Electron. Agric. 18:167-186.
DOI
|
5 |
Sheela, K.G. and S.N. Deepa. 2013. Review on methods to fix number of hidden neurons in neural networks. Math. Probl. Eng. 2013:11.
|
6 |
Silver, D., A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al. 2016. Mastering the game of Go with deep neural networks and tree search. Nature 529:484-489.
DOI
|
7 |
Tweedie, R. L., K.L. Mengersen, and J.A. Eccleston. 1994. Garbage in, garbage out: can statisticians quantify the effects of poor data?. Chance 7:20-27.
DOI
|
8 |
Walker, J.N. 1965. Predicting temperatures in ventilated greenhouses. Trans. ASAE 8:445-448.
DOI
|
9 |
Went, F.W. 1953. The effect of temperature on plant growth. Annu. Rev. Plant Physiol. 4:347-362.
DOI
|
10 |
Abadi, M., P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, et al. 2016. TensorFlow: A system for large-scale machine learning. In: Proceedings of 12th USENIX OSDI, November, Savanah, GA, USA, 265-283.
|
11 |
Al Shalabi, L. and Z. Shaaban. 2006. Normalization as a preprocessing engine for data mining and the approach of preference matrix. In: Proceedings of 2006 International Conference on Dependability of Computer Systems, May, Szklarska Poreba, Poland, IEEE, 207-214.
|
12 |
Arauz, L. F. and T. B. Sutton. 1990. Effect of interrupted wetness periods on spore germination and apple infection by Botryosphaeria obtusa. Phytopathol. 80:1218-1220.
DOI
|
13 |
Froehlich, D.P., L.D. Albright, N.R. Scott, and P. Chandra. 1979. Steady-periodic analysis of glasshouse thermal environment. Trans. ASAE 22:387-399.
DOI
|
14 |
Benediktsson, J.A., P.H. Swain, and O.K. Ersoy. 1990. Neural network approaches versus statistical methods in classification of multisource remote sensing data. In: Proceedings of Transactions on Geoscience and Remote Sensing, July, Vancouver, Canada, IEEE, 540-552.
|
15 |
Chandra, P., L.D. Albright, and N.R. Scott. 1981. A time dependent analysis of greenhouse thermal environment. Trans. ASAE 24:442-449.
DOI
|
16 |
Ferreira, P.M., E.A. Faria, and A.E. Ruano. 2002. Neural network models in greenhouse air temperature prediction. Neurocomputing 43:51-75.
DOI
|
17 |
Glorot, X., A. Bordes, and Y. Bengio. 2011. Deep sparse rectifier neural networks. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, April, Ft. Lauderdale, FL, USA, 315-323.
|
18 |
Grange, R.I. and D.W. Hand. 1987. A review of the effects of atmospheric humidity on the growth of horticultural crops. J. Hortic. Sci. 62:125-134.
DOI
|
19 |
Haykin, S. 2009. Neural networks: a comprehensive foundation. 3st ed. Prentice Hall PTR, Upper Saddle River, NJ, USA. p. 122-129.
|
20 |
He, F. and C. Ma. 2010. Modeling greenhouse air humidity by means of artificial neural network and principal component analysis. Comput. Electron. Agric. 71:S19-S23.
DOI
|
21 |
He, K., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June, Las Vegas, NV, USA, IEEE, 770-778.
|
22 |
Ioffe, S. and C. Szegedy. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167.
|
23 |
Hinton, G., L. Deng, D. Yu, G.E. Dahl, A.R. Mohamed, N. Jaitly, A. Senior, V. Vanhouckeet, P. Nguyen, T.N. Sainath, et al. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Proc. Mag. 29:82-97.
|
24 |
Hochreiter, S. and J. Schmidhuber. 1997. Long short-term memory. Neural Comput. 9:1735-1780.
DOI
|
25 |
Hong, S.W. and I.B. Lee. 2014. Predictive model of microenvironment in a naturally ventilated greenhouse for a model-based control approach. Protected Hort. Plant Fac. 23:181-191.
DOI
|
26 |
Hornik, K., M. Stinchcombe, and H. White. 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2:359-366.
DOI
|
27 |
Hornik, K. 1991. Approximation capabilities of multilayer feedforward networks. Neural Networks 4:251-257.
DOI
|
28 |
Jolliet, O. 1994. HORTITRANS, a model for predicting and optimizing humidity and transpiration in greenhouses. J. Agr. Eng. Res. 57:23-37.
DOI
|
29 |
Kingma, D. and J. Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980v9.
|
30 |
Krizhevsky, A., I. Sutskever, and G.E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing Systems, December, Lake Tahoe, NV, USA, 1097-1105.
|
31 |
LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep learning. Nature 521:436.
DOI
|
32 |
Mnih, V., K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.
|
33 |
Lee, J.W., J.H. Shin., J.H. Kim., H.W. Park, I.H. Yu., and J.E. Son. 2014. Analysis of light environments in reclaimed land and estimation of spatial light distributions in greenhouse by 3-D model. Protected Hort. Plant Fac. 23:303-308.
DOI
|