Ahn, J. and Lee, H. 2015. Smart farm using IoT that change the lives of rural people. Plan. Policy 5: 19-26.
Anantrasirichai, N., Hannuna, S. and Canagarajah, N. 2017. Automatic leaf extraction from outdoor images. arXiv 1709.06437.
Barbedo, J. G. A. 2018. Factors influencing the use of deep learning for plant disease recognition.
Biosyst. Eng 172: 84-91.
Bell, H., Wakefield, M., Macarthur, R., Stein, J., Collins, D., Hart, A. et al. 2014. Plant health surveys for the EU territory: an analysis of data quality and methodologies and the resulting uncertainties for pest risk assessment (PERSEUS) CFP/EFSA/PLH/2010/01.
EFSA Support. Publ 11: 676E
Bendre, M. R., Thool, R. C. and Thool, V. R. 2015. Big data in precision agriculture: weather forecasting for future farming. 2015 1st International Conference on Next Generation Computing Technologies (NGCT). Institute of Electrical and Electronics Engineers, New York, NY, USA. pp. 744-750.
Bera, T., Das, A., Sil, J. and Das, A. K. eds. by A. Abraham, P. Dutta, J. Mandal, A. Bhattacharya and S. Dutta, 2019. A survey on rice plant disease identification using image processing and data mining techniques. In: Emerging Technologies in Data Mining and Information Security, Advances in Intelligent Systems and Computing, Vol. 814, pp. 365-376. Springer, Singapore.
Bronson, K. and Knezevic, I. 2016. Big data in food and agriculture.
Big Data Soc 3: 1-5.
Carranza-Rojas, J., Goeau, H., Bonnet, P., Mata-Montero, E. and Joly, A. 2017. Going deeper in the automated identification of Herbarium specimens.
BMC Evol. Biol 17: 181
Chen, M., Brun, F., Raynal, M. and Makowski, D. 2020. Forecasting severe grape downy mildew attacks using machine learning.
PLoS ONE 15: e0230254
Chéné, Y., Rousseau, D., Lucidarme, P., Bertheloot, J., Caffier, V., Morel, P. et al. 2012. On the use of depth camera for 3D phenotyping of entire plants.
Comput. Electron. Agric 82: 122-127.
Cireşan, D. C., Meier, U., Masci, J., Gambardella, L. M. and Schmidhuber, J. ed. by W. Toby, 2011. Flexible, high performance convolutional neural networks for image classification. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, AAAI Press, Palo Alto, CA, USA. pp. 1237-1242.
Coble, K. H., Mishra, A. K., Ferrell, S. and Griffin, T. 2018. Big data in agriculture: a challenge for the future.
Appl. Econ. Perspect. Policy 40: 79-96.
de Oliveira Aparecido, L. E., de Souza Rolim, G., De Moraes, J. R. D. S. C., Costa, C. T. S. and de Souza, P. S. 2020. Machine learning algorithms for forecasting the incidence of
Coffea arabica pests and diseases.
Int. J. Biometeorol 64: 671-688.
Devlin, B. 2012. The Big Data Zoo-Taming the Beasts: The Need for an Integrated Platform for Enterprise Information. 9sight Consulting, Cape Town, South Africa. pp. 12 pp.
Dietterich, T. G. eds. by J. Kittler and F. Roli, 2000. Ensemble methods in machine learning. In: Multiple Classifier Systems, Vol. 1857 of Lecture Notes in Computer Science, Springer, Berlin/Heidelberg, Germany. pp. 1-15.
Ehler, L. E. 2006. Integrated pest management (IPM): definition, historical development and implementation, and the other IPM.
Pest Manag. Sci 62: 787-789.
Eum, H.-I., Kim, J. P. and Cho, J. 2018. High-resolution climate data from an improved GIS-based regression technique for South Korea.
KSCE J. Civil Eng 22: 5215-5228.
Fahrentrapp, J., Ria, F., Geilhausen, M. and Panassiti, B. 2019. Detection of gray mold leaf infections prior to visual symptom appearance using a five-band multispectral sensor.
Front. Plant Sci 10: 628
Fenu, G. and Malloci, F. M. 2019. An application of machine learning technique in forecasting crop disease. Proceedings of the 2019 3rd International Conference on Big Data Research. Association for Computing Machinery, New York, NY, USA. pp. 76-82.
Fisher, M. C., Henk, D. A., Briggs, C. J., Brownstein, J. S., Madoff, L. C., McCraw, S. L. et al. 2012. Emerging fungal threats to animal, plant and ecosystem health.
Nature 484: 186-194.
Giraud, T., Gladieux, P. and Gavrilets, S. 2010. Linking the emergence of fungal plant diseases with ecological speciation.
Trends Ecol. Evol 25: 387-395.
Grünwald, N. J. and Goss, E. M. 2011. Evolution and population genetics of exotic and re-emerging pathogens: novel tools and approaches.
Annu. Rev. Phytopathol 49: 249-267.
Hammer, C., Kostroch, D. C. and Quiros, G. STA Internal Group. 2017. Big Data: Potential, Challenges and Statistical Implications. International Monetary Fund, Washington DC, USA. pp. 41
Hillnhütter, C., Mahlein, A.-K., Sikora, R. A. and Oerke, E.-C. 2011. Remote sensing to detect plant stress induced by
Heterodera schachtii and
Rhizoctonia solani in sugar beet fields.
Field Crops Res 122: 70-77.
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-R., Jaitly, N. et al. 2012. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups.
IEEE Signal Process Mag 29: 82-97.
Ip, R. H. L., Ang, L.-M., Seng, K. P., Broster, J. C. and Pratley, J. E. 2018. Big data and machine learning for crop protection.
Comput. Electron. Agric 151: 376-383.
Ishii, K. 2014. Big data analysis in medicine, agriculture and environmental sciences. Seibutsu-kogaku Kaishi 92: 92-93.
Jeon, S. 2011. Mechanisms of labor transition during agricultural transformation: the cases of South Korea and Indonesia. 2011 International Conference on Asia Agriculture and Animal IPCBEE, Vol. 13. IACSIT Press, Singapore. pp. 21-26.
Kim, C.-G., Lee, S.-M., Jeong, H.-K., Jang, J.-K., Kim, Y.-H. and Lee, C.-K. 2010. Impacts of Climate Change on Korean Agriculture and Its Counterstrategies. Korea Rural Economic Institute, Naju, Korea. pp. 306 pp.
Kim, M.-K., Han, M.-S., Jang, D.-H., Baek, S.-G., Lee, W.-S. and Kim, Y.-J. 2012. A production method for historical climate data with 1-km-resolution grids. Clim. Res 7: 55-68.
Kim, S., Lee, M. and Shin, C. 2018a. IoT-based strawberry disease prediction system for smart farming.
Sensors 18: 4051
Kim, Y., Roh, J.-H. and Kim, H. Y. 2018b. Early forecasting of rice blast disease using long short-term memory recurrent neural networks.
Sustainability 10: 34
Kumar, A. 2015. Science of omics for agricultural productivity: future perspective: a national conference report. Int. J. Comput. Bioinform. In Silico Model 4: 607-610.
LeCun, Y., Bengio, Y. and Hinton, G. 2015. Deep learning.
Nature 521: 436-444.
LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P. 1998. Gradient-based learning applied to document recognition.
Proc. IEEE 86: 2278-2324.
Li, X.-F., Chen, S.-H. and Guo, L.-F. 2014. Technological innovation of agricultural information service in the age of big data. J. Agric. Sci. Technol 16: 10-15.
Luo, W., Pietravalle, S., Parnell, S., Van den Bosch, F., Gottwald, T. R., Irey, M. S. et al. 2012. An improved regulatory sampling method for mapping and representing plant disease from a limited number of samples.
Epidemics 4: 68-77.
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G. and Johnson, B. A. 2019. Deep learning in remote sensing applications: a meta-analysis and review.
ISPRS J. Photogramm. Remote Sens 152: 166-177.
Magarey, R. D., Sutton, T. B. and Thayer, C. L. 2005. A simple generic infection model for foliar fungal plant pathogens.
Phytopathology 95: 92-100.
Mahlein, A.-K. 2016. Plant disease detection by imaging sensors: parallels and specific demands for precision agriculture and plant phenotyping.
Plant Dis 100: 241-251.
McRoberts, N., Hall, C., Madden, L. V. and Hughes, G. 2011. Perceptions of disease risk: From social construction of subjective judgments to rational decision making.
Phytopathology 101: 654-665.
Milgroom, M. G. and Fry, W. E. 1997. Contributions of population genetics to plant disease epidemiology and management.
Adv. Bot. Res 24: 1-30.
Mohanty, S. P., Hughes, D. P. and Salathé, M. 2016. Using deep learning for image-based plant disease detection.
Front. Plant Sci 7: 1419
Mokhtar, U., Ali, M. A. S., Hassanien, A. E. and Hefny, H. eds. by J. K. Mandal, S. C. Satapathy, M. K. Sanyal, P. P. Sarkar and A. Mukhopadhyay, 2015. Identifying two of tomatoes leaf viruses using support vector machine. In: Information Systems Design and Intelligent Applications, Springer, New Delhi, India. pp. 771-782.
Montone, V. O., Fraisse, C. W., Peres, N. A., Sentelhas, P. C., Gleason, M., Ellis, M. et al. 2016. Evaluation of leaf wetness duration models for operational use in strawberry disease-warning systems in four US states.
Int. J. Biometeorol 60: 1761-1774.
Moschini, G. and Hennessy, D. A. eds. by B. L. Gardner and G. C. Rausser, 2001. Uncertainty, risk aversion, and risk management for agricultural producers. In: Handbook of Agricultural Economics, Elsevier, Amsterdam, Netherlands. pp. 87-153.
Nelson, M. R., Orum, T. V., Jaime-Garcia, R. and Nadeem, A. 1999. Applications of geographic information systems and geostatistics in plant disease epidemiology and management.
Plant Dis 83: 308-319.
Nettleton, D. F., Katsantonis, D., Kalaitzidis, A., Sarafijanovic-Djukic, N., Puigdollers, P. and Confalonieri, R. 2019. Predicting rice blast disease: machine learning versus process-based models.
BMC Bioinformatics 20: 514
Ojiambo, P. S., Yuen, J., Van den Bosch, F. and Madden, L. V. 2017. Epidemiology: past, present, and future impacts on understanding disease dynamics and improving plant disease management: a summary of focus issue articles.
Phytopathology 107: 1092-1094.
Paoletti, M. E., Haut, J. M., Plaza, J. and Plaza, A. 2019. Deep learning classifiers for hyperspectral imaging: a review.
ISPRS J. Photogramm. Remote Sens 158: 279-317.
Pascual, L., Romo, J. and Ruiz, E. 2006. Bootstrap prediction for returns and volatilities in GARCH models.
Comput. Stat. Data Anal 50: 2293-2312.
Pavan, W., Fraisse, C. W. and Peres, N. A. 2009. A web-based decision support tool for timing fungicide applications in strawberry. EDIS 9: 1-5.
Polak, L. and Milos, J. 2020. Performance analysis of LoRa in the 2.4 GHz ISM band: coexistence issues with Wi-Fi.
Telecommun. Syst 74: 299-309.
Raza, S.-, Prince, G., Clarkson, J. P. and Rajpoot, N. M. 2015. Automatic detection of diseased tomato plants using thermal and stereo visible light images.
PLoS ONE 10: e0123262
Rose, D. C. and Chilvers, J. 2018. Agriculture 4.0: broadening responsible innovation in an era of smart farming.
Front. Sustain. Food Syst 2: 87
Rowlandson, T., Gleason, M., Sentelhas, P., Gillespie, T., Thomas, C. and Hornbuckle, B. 2015. Reconsidering leaf wetness duration determination for plant disease management.
Plant Dis 99: 310-319.
Ruane, A. C., Goldberg, R. and Chryssanthacopoulos, J. 2015. Climate forcing datasets for agricultural modeling: merged products for gap-filling and historical climate series estimation.
Agric. For. Meteorol 200: 233-248.
Ryan, S. F., Adamson, N. L., Aktipis, A., Andersen, L. K., Austin, R., Barnes, L. et al. 2018. The role of citizen science in addressing grand challenges in food and agriculture research.
Proc. Biol. Sci 285: 20181977
Sabarina, K. and Priya, N. 2015. Lowering data dimensionality in big data for the benefit of precision agriculture.
Procedia Comput. Sci 48: 548-554.
Sannakki, S. S., Rajpurohit, V. S., Nargund, V. B., Kumar, R. A. and Yallur, P. S. 2011. Leaf disease grading by machine vision and fuzzy logic. Int. J. Comput. Technol. Appl 2: 1709-1716.
Scherm, H., Ngugi, H. K. and Ojiambo, P. S. 2006. Trends in theoretical plant epidemiology.
Eur. J. Plant Pathol 115: 61-73.
Schmidhuber, J. and Tubiello, F. N. 2007. Global food security under climate change.
Proc. Natl. Acad. Sci. U. S. A 104: 19703-19708.
Schönfeld, M. V., Heil, R. and Bittner, L. eds. by T. Hoeren and B. Kolany-Raiser, 2018. Big data on a farm: smart farming. In: Big Data in Context, Springer, Cham, Switzerland. pp. 109-120.
Seo, Y. J. 2016. Koreás smart agriculture status and major challenges. World Agric 185: 51-71. (In Korean)
Shtienberg, D. 2013. Will decision-support systems be widely used for the management of plant diseases?
Annu. Rev. Phytopathol 51: 1-16.
Sonka, S. 2015. Big Data: from hype to agricultural tool. Farm Policy J 12: 1-9.
Sperschneider, J. 2019. Machine learning in plant-pathogen interactions: empowering biological predictions from field scale to genome scale.
New Phytol 228: 35-41.
Stubbs, M. 2016. Big Data in U.S. Agriculture. Report R44331. Congressional Research Service, Washington, DC, USA. pp. 14 pp.
Surendrababu, V., Sumathi, C. P. and Umapathy, E. 2014. Detection of rice leaf diseases using chaos and fractal dimension in image processing. Int. J. Comput. Sci. Eng 6: 69-74.
Tantalaki, N., Souravlas, S. and Roumeliotis, M. 2019. Data-driven decision making in precision agriculture: the rise of Big Data in agricultural systems.
J. Agric. Food Inf 20: 344-380.
Too, E. C., Yujian, L., Njuki, S. and Yingchun, L. 2019. A comparative study of fine-tuning deep learning models for plant disease identification.
Comput. Electron. Agric 161: 272-279.
Tripathy, A. K., Adinarayana, J., Vijayalakshmi, K., Merchant, S. N., Desai, U. B., Ninomiya, S. et al. 2014. Knowledge discovery and leaf spot dynamics of groundnut crop through wireless sensor network and data mining techniques.
Comput. Electron. Agric 107: 104-114.
Vashi, S., Ram, J., Modi, J., Verma, S. and Prakash, C. 2017. Internet of Things (IoT): a vision, architectural elements, and security issues. 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). Institute of Electrical and Electronics Engineers, New York, USA. pp. 492-496.
Verhoosel, J., van Bekkum, M. and Verwaart, T. eds. by G. Schiefer and U. Rickert, 2016. HortiCube: a platform for transparent, trusted data sharing in the food supply chain. In: Proceedings in System Dynamics and Innovation in Food Networks 2016, Universitat Bonn-ILB Press, Bonn, Germany. pp. 384-388.
Wahabzada, M., Mahlein, A.-K., Bauckhage, C., Steiner, U., Oerke, E.-C. and Kersting, K. 2015. Metro maps of plant disease dynamics: automated mining of differences using hyperspectral images.
PLoS ONE 10: e0116902
Weersink, A., Fraser, E., Pannell, D., Duncan, E. and Rotz, S. 2018. Opportunities and challenges for big data in agricultural and environmental analysis.
Annu. Rev. Resour. Econ 10: 19-37.
Wetterich, C. B., Kumar, R., Sankaran, S., Junior, J. B., Ehsani, R. and Marcassa, L. G. 2013. A comparative study on application of computer vision and fluorescence imaging spectroscopy for detection of citrus huanglongbing disease in USA and Brazil. Frontiers in Optics 2013/Laser Science. Optical Society of America, Washington, DC, USA. JW3A-26.
Wolfert, S., Ge, L., Verdouw, C. and Bogaardt, M.-J. 2017. Big data in smart farming: a review.
Agric. Syst 153: 69-80.
World Meteorological Organization. 2010. Guide to Agricultural Meteorological Practices. World Meteorological Organization No. 134. World Meteorological Organization, Geneva, Switzerland. pp. 799 pp.
Yan, J., Risacher, S. L., Shen, L. and Saykin, A. J. 2018. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data.
Brief. Bioinform 19: 1370-1381.
Yang, X. and Guo, T. 2017. Machine learning in plant disease research.
Eur. J. BioMed. Res 3: 6-9.
Yeo, H. 2019. Overseas agricultural big data utilization status. World Agric 266: 37-52. (In Korean)