Abstract
Agriculture is the primary source of basic needs like food, raw material and fuel, which are considered as the basic building blocks for the economic growth of any nation. Agriculture products threatened by various factors including decline in pollinators, various diseases in crops, improper irrigation, technology, scarcity of water and many others. Deep learning has emerged as a promising technique that can be used for data intensive applications and computer vision tasks. It has a great potential and like other domains, it can also apply to agriculture domain. In this paper, a comprehensive review of research dedicated to applications of deep learning for precision agriculture is presented along with real time applications, tools and available datasets. The findings exhibit the high potential of applying deep learning techniques for precision agriculture.
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References
K. Jha, A. Doshi, P. Patel, M. Shah, A comprehensive review on automation in agriculture using artificial intelligence. Artif. Intell. Agric. 2, 1–12 (2019)
L.P. Saxena, L. Armstrong, A survey of image processing techniques for agriculture, in Asian Federation for Information Technology in Agriculture, Perth, W. A. Australian Society of Information and Communication Technologies in Agriculture, pp. 401–413 (2014)
Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–44 (2015)
D. Li, D. Yu, Deep Learning: methods and applications. Found. Trends Sig. Process. 7(3, 4), 197–387 (2014)
S.J. Pan, Q. Yang, A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
S.W. Chen, S.S. Shivakumar, S. Dcunha, J. Das, E. Okon, C. Qu, C.J. Taylor, V. Kumar, Counting apples and oranges with deep learning: a data driven approach. IEEE Robot. Autom. Lett. 1–1 (2017). https://doi.org/10.1109/lra.2017.2651944
A. Kamilaris, F.X. Prenafeta-Boldu, Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018)
M.M. Najafabadi, F. Villanustre, T.M. Khoshgoftaar, Deep learning applications and challenges in big data analytics. J. Big Data 2(1) (2015)
A. Canziani, A. Paszke. E. Culurciello, An analysis of deep neural network models for practical applications (2016)
S. Bahrampour, N. Ramakrishnan, L. Schott, M. Shah, Comparative study of caffe, neon, theano, and torch for deep learning. arXiv (2015)
J. Amara, B. Bouaziz, A. Algergawy, A deep learning-based approach for banana leaf diseases classification, in B. Mitschang, D. Nicklas, F. Leymann, H. Schöning, M. Herschel, J. Teubner, T. Härder, O. Kopp, M. Wieland (Hrsg.), Datenbanksysteme für Business, Technologie und Web (BTW 2017)—Workshopband. Bonn: Gesellschaft für Informatik e.V. (S. 79–88); X. Wang, C. Cai (2015). Weed seeds classification based on PCANet deep learning baseline. pp. 408–415 (2017). https://doi.org/10.1109/apsipa.2015.7415304
M. Rahnemoonfar, C. Sheppard, Deep count: fruit counting based on deep simulated learning. Sensors 17, 905 (2017)
X. Song, G. Zhang, F. Liu et al., Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. J. Arid Land. 8, 734–748 (2016)
H.T. Dinh, D. Ienco, R. Gaetano, N. Lalande, E. Ndikumana, F. Osman, P. Maurel, Deep recurrent neural networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1 (2017). arXiv arXiv:abs/1708.03694
W.-S. Jeon , S.-Y. Rhee, Plant leaf recognition using a convolution neural network, in Int. J. Fuzzy Logic Intell. Syst. 17, 26–34 (2017)
Y. Sun, Y. Liu, G. Wang, H. Zhang, Deep learning for plant identification in natural environment. Comput. Intell. Neurosci. 4, 1–6 (2017)
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, D. Stefanovic, Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 6, 1–11 (2016)
M. Dyrmann, H. Karstoft, H.S. Midtiby, Plant species classification using deep convolutional neural network. Biosyst. Eng. 151, 72–80 (2016)
M. Šulc, J. Matas, Fine-grained recognition of plants from images. Plant Methods 13 (2017)
M.B. Pound et al., Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience 6, (2016)
S.H. Lee, C.S. Chan, P. Wilkin, P. Remagnino, Deep-plant: plant identification with convolutional neural networks, in IEEE International Conference on Image Processing (ICIP), Quebec City, QC, 2015, pp. 452–456 (2015)
Y. Chen, Z. Lin, X. Zhao, G. Wang, Y. Gu, Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 7(6), 2094–2107 (2014)
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Ganatra, N., Patel, A. (2021). Deep Learning Methods and Applications for Precision Agriculture. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_51
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DOI: https://doi.org/10.1007/978-981-15-7106-0_51
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