3. Patel, H. and Patel, D., A Brief survey of Data Mining Techniques Applied to Agricultural Data. Int. J. Comput. Appl., 9, 95, 6–8, 2014.
4. Mishra, S., Mishra, D., Santra, G.H., Applications of machine learning techniques in agricultural crop production: A review paper. Indian J. Sci. Technol., 9, 38, 1–14, 2016.
5. Ornella, L., Cervigni, G., Tapia, E., Applications of machine learning in breeding for stress tolerance in maize, in: Crop Stress and its Management: Perspectives and Strategies, 2012.
6. Dahikar, M.S. and Rode, D.V., Agricultural Crop Yield Prediction Using Artificial Neural Network Approach. Int. J. Innovat. Res. Electr. Electron. Instrum. Contr. Eng., 2, 684–686, 2014.
7. Stathakis, D. and Savin, I., Networks, F.N., Neuro-Fuzzy Modelling For Crop Yield Prediction. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 34, 1–4, 2006.
8. Qaddoum, K., Hines, E., Illiescu, D., Adaptive neuro-fuzzy modeling for crop yield prediction, AIKED’11: Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases,199–204, February, 2011.
9. Murmu, S. and Biswas, S., Application of Fuzzy Logic and Neural Network in Crop Classification: A Review. Aquat. Procedia, 4, Icwrcoe, 1203–1210, 2015.
10. Hartati, S. and Sitanggang, I.S., A fuzzy based decision support system for evaluating land suitability and selecting crops. J. Comput. Sci., 6, 417–424, 2010.
11. Qureshi, M.R.N., Singh, R.K., Hasan, M.A., Decision support model to select crop pattern for sustainable agricultural practices using fuzzy MCDM. Environ. Dev. Sustain., 6, 417–424, 2018.
12. Petridis, V. and Kaburlasos, V.G., FINkNN: A fuzzy interval number k–nearest neighbor classifier for prediction of sugar production from populations of samples. J. Mach. Learn. Res., 41, 539–545, 2004.
13. Uno, Y. et al., Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data. Comput. Electron. Agric., 47, 2, 149–161, 2005.
14. Veenadhari, S., Bharat Mishra, D., Singh, D.C., Soybean Productivity Modelling using Decision Tree Algorithms. Int. J. Comput. Appl., 27, 7, 11–15, 2011.
15. Veenadhari, S., Misra, B., Singh, C.D., Machine learning approach for forecasting crop yield based on climatic parameters, 2014 International Conference on Computer Communication and Informatics, Coimbatore, pp. 1–5, 2014.
16. Bitouk, D., Verma, R., Nenkova, A., Class-level spectral features for emotion recognition. Speech Commun., 52, 7–8, 613–625, 2010.
17. Tan, L., Cloud-based Decision Support and Automation for Precision Agriculture in Orchards. IFAC-PapersOnLine, 49, 330–335, 2016.
18. Horie, T., Yajima, M., Nakagawa, H., Yield forecasting. Agric. Syst., 40, 1–3, 211–236, 1992.
19. Basso, B., Cammarano, D., Carfagna, E., Review of Crop Yield Forecasting Methods and Early Warning Systems. First Meet. Sci. Advis. Comm. Glob. Strateg. to Improv. Agric. Rural Stat, 2013.
20. De La Rosa, D., Cardona, F., Almorza, J., Crop yield predictions based on properties of soils in Sevilla, Spain. Geoderma, 25, 3–4, 267–274, May, 1981.
21. Kaspar, T.C. et al., Relationship between six years of corn yields and terrain attributes. Precis. Agric., 4, 87–101, 2003.
22. Shibayama, M. and Akiyama, T., Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sens. Environ., 36, 1, 45–53, 1991.
23. Wilcox, A., Perry, N.H., Boatman, N.D., Chaney, K., Factors affecting the yield of winter cereals in crop margins. J. Agric. Sci., 135, 4, 335–346, 2000.
24. Lee, H., Bogner, C., Lee, S., Koellner, T., Crop selection under price and yield fluctuation: Analysis of agro-economic time series from South Korea. Agric. Syst., 148, 1–11, 2016.
25. Selvaraju, R., Meinke, H., Hansen, J., Approaches allowing smallholder farmers in India to benefit from seasonal climate forecasting. Crop Sci., 2004.
26. Groenendyk, D., Thorp, K., Ferré, T., Crow, W., Hunsaker, D., A k-means clustering approach to assess wheat yield prediction uncertainty with a HYDRUS-1D coupled crop model, 7th International Congress on Environmental Modelling and Software, iEMSs 2014 – San Diego, United States Duration: Jun 15, 2014 to Jun 19, 2014.
27. Teixeira de Lima, G.R. and Stephany, S., A new classification approach for detecting severe weather patterns. Comput. Geosci., 52, 34, 2013.
28. Challinor, A.J., Slingo, J.M., Wheeler, T.R., Craufurd, P.Q., Grimes, D.I.F., Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale. J. Appl. Meteorol., 175–192, 2003.
29. Singh, C.V., Pattern characteristics of Indian monsoon rainfall using principal component analysis (PCA). Atmos. Res., 79, 3–4, 317–326, 2006.
30. Canale, A. and Ruggiero, M., Bayesian nonparametric forecasting of monotonic functional time series. Electron. J. Stat., 10, 2, 3265–3286, 2016.
31. Hong-ying, L., Yan-lin, H., Yong-juan, Z., Hui-ming, Z., Crop Yield Forecasted Model Based on Time Series Techniques. J. Northeast Agric. Univ. (Engl. Ed.), 6, 4, 298–304, 2012.
32. Matis, J.H., Birkett, T., Boudreaux, D., An application of the Markov chain approach to forecasting cotton yields from surveys. Agric. Syst., 1989.
33. Jain, R. and Ramasubramanian, V., Forecasting of crop yields using second order Markov Chains. J. Indian Soc. Agric. Stat., 52, 2, 61–72, 1998.
*Corresponding author: [email protected]
2
Smart Farming Using Machine Learning and IoT
Alo Sen1, Rahul Roy1* and Satya Ranjan Dash2
1ei2 Classes and Technologies, Durgapur, India2School of Computer Application, KIIT University, Bhubaneswar, India
Abstract
From the early civilization till the date, three things: Shelter, Garment and Food are main mantra of a human being. People are quite advanced with modern houses and dresses. But with increased population of Earth, As per UN Food and Agriculture Organization, people will have to produce 70% more food in 2050 rather than it did in 2006. In recent years IoT had been used to meet the challenge of different industrial and technical purposes. Now it is the time to meet the demand of future farming which can only be accomplished by smart Agro-IoT tool. There is a need to boost the productivity and minimize the pitfalls of traditional farming which is the main backbone of World’s Economical growth. IoT will help in continuous monitoring of the field to give useful information to the farmers which will add a new era in future farming. IoT tool can be implemented for monitoring climate change, water management, land monitoring, increasing productivity, monitoring crops, controlling insecticides and pesticides, soil management, detecting plant diseases, increasing the rate of crop sale etc. In this book chapter we will focus on some case studies like monitoring of climate conditions, greenhouse automation, crop management, cattle monitoring and management for smart farming with IoT device which will provide a clear idea why to use the technique in agriculture rather than some pre existing agricultural tool developed earlier.
Keywords: Smart farming, agro-IoT, agricultural tool, efficiency, productivity, IoT