Abstract
The scope of sensor networks and Internet of Things spanning rapidly to diversified domains but not limited to sports, health, and business trading. In recent past, the sensors and MEMS integrated Internet of Things are playing crucial role in diversified farming strategies like dairy farming, animal farming, and agriculture farming. The usage of sensors and IoT technologies in farming are coined in contemporary literature as smart farming or precision farming. At its early state of the smart farming, the practices applying in agriculture farming are limited to collect the data related to the context of the farming such as soil state, weather state, weed state, crop quality, and seed quality. These collections are to help the farmers, scientists to conclude the positive and negative factors of crop to initiate the required agricultural practices. However, the impact of these practices taken by the agriculturists depends on their experience. In this regard, the computer aided predictive analytics by machine learning and big data strategies are having inevitable scope. The emphasis of this manuscript is reviewing the existing set of computer aided methods of predictive analytics defined in related to precision farming, gaining insights into how distinct set of precision farming inputs are supporting the predictive analytics to help farming communities towards improvisation.
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Ahmed, S. (2014). FDI and precision agriculture in India. In Foreign direct investment, trade and economic growth (pp. 97–116). New Delhi: Routledge.
Ali, I., Greifeneder, F., Stamenkovic, J., Neumann, M., & Notarnicola, C. (2015). Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sensing,7(12), 16398–16421.
Antle, J. M., Jones, J. W., & Rosenzweig, C. (2017). Next generation agricultural system models and knowledge products: Synthesis and strategy. Agricultural Systems,155, 179–185.
Antonopoulou, E., Karetsos, S. T., Maliappis, M., & Sideridis, A. B. (2010). Web and mobile technologies in a prototype DSS for major field crops. Computers and Electronics in Agriculture,70(2), 292–301.
Athmaja, S., Hanumanthappa, M, & Kavitha, V. (2017). A survey of machine learning algorithms for big data analytics. In 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (pp. 1–4). IEEE.
Bendre, M. R., & Thool, V. R. (2016). Analytics, challenges and applications in big data environment: A survey. Journal of Management Analytics,3(3), 206–239.
Bendre, M. R., Thool, R. C., & Thool, V. R. (2015). Big data in precision agriculture: Weather forecasting for future farming. In 2015 1st International Conference on Next Generation Computing Technologies (NGCT) (pp. 744–750). IEEE.
Budati, A. K., & Polipalli, T. R. (2019). Performance analysis of HFDI computing algorithm in intelligent networks. International Journal of Computers and Applications,41(4), 255–261.
Budati, A. K., & Valiveti, H. (2019). Identify the user presence by GLRT and NP detection criteria in cognitive radio spectrum sensing. International Journal of Communication Systems. https://doi.org/10.1002/dac.4142.
Carberry, P. S., Hochman, Z., McCown, R. L., Dalgliesh, N. P., Foale, M. A., Poulton, P. L., et al. (2002). The FARMSCAPE approach to decision support: Farmers’, advisers’, researchers’ monitoring, simulation, communication and performance evaluation. Agricultural Systems,74(1), 141–177.
Chandel, N. S., Agrawal, K. N., Tripathi, H., & Garg, S. K. (2014). Development of yield maps in wheat using yield monitor. Bhartiya Krishi Anusandhan Patrika,29(3), 111–115.
Channe, H., Kothari, S., & Kadam, D. (2015). Multidisciplinary model for smart agriculture using internet-of-things (IoT), sensors, cloud-computing, mobile-computing & big-data analysis. International Journal of Computer Technology and Applications,6(3), 374–382.
Coble, K. H., Mishra, A. K., Ferrell, S., & Griffin, T. (2018). Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy,40(1), 79–96.
Da Xu, L., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics,10(4), 2233–2243.
Dixit, J., Dixit, A. K., Lohan, S. K., & Kumar, D. (2014). Importance, concept and approaches for precision farming in India. Precision farming: A new approach (pp. 12–35). Delhi: Daya Publishing House.
Dutta, R., Li, C., Smith, D., Das, A., & Aryal, J. (2015). Big data architecture for environmental analytics. In international symposium on environmental software systems, (pp. 578–588). Springer, Cham.
Emadi, M., Baghernejad, M., & Maftoun, M. (2008). Assessment of Some Soil Properties by Spatial Variability in Saline and Sodic Soils in Arsanjan Plain, Southern Iran. Pakistan Journal of Biological Sciences: PJBS,11(2), 238–243.
Farid, H. U., Bakhsh, A., Ahmad, N., & Ahmad, A. (2013). Evaluation of management zones for site-specific application of crop inputs. Pakistan Journal of Life and Social Sciences (Pakistan),11, 29–35.
Gope, H. L., Das, P. K., Islam, M. J., & Seddiqui, M. H. (2014). Medical document classification from OHSUMED dataset. IJCSN International Journal of Computer Science and Network,3(4), 215–219.
Huang, Y., Chen, Z. X., Tao, Y. U., Huang, X. Z., & Gu, X. F. (2018). Agricultural remote sensing big data: Management and applications. Journal of Integrative Agriculture,17(9), 1915–1931.
Ip, R. H., Ang, L. M., Seng, K. P., Broster, J. C., & Pratley, J. E. (2018). Big data and machine learning for crop protection. Computers and Electronics in Agriculture,151, 376–383.
Jayashree, L. S., Palakkal, N., Papageorgiou, E. I., & Papageorgiou, K. (2015). Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region. Neural Computing and Applications,26(8), 1963–1978.
Jones, J. W., Antle, J. M., Basso, B., Boote, K. J., Conant, R. T., Foster, I., et al. (2017). Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science. Agricultural Systems,155, 269–288.
Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture. Computers and Electronics in Agriculture,143, 23–37.
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in Agriculture,147, 70–90.
Karl, H., & Willig, A. (2007). Protocols and architectures for wireless sensor networks. Hoboken: Wiley.
Kempenaar, C., Lokhorst, C., Bleumer, E. J. B., Veerkamp, R. F., Been, T., van Evert, F. K., et al. (2016). Big Data analysis for smart farming: Results of TO2 project in theme food security. Wageningen: Wageningen University & Research.
Kumar, B. A., & Rao, P. T. (2017). MDI-SS: matched filter detection with inverse covariance matrix-based spectrum sensing in cognitive radio. International Journal of Internet Technology and Secured Transactions,7(4), 353–363.
Kumar, G., & Chinara, S. (2015). Development of energy efficient wireless sensor networks protocol for precision agriculture. Journal of Basic and Applied Engineering Research,2, 360–364.
Kumar, H., & Menakadevi, T. (2017). A review on big data analytics in the field of agriculture. International Journal of Latest Transactions in Engineering and Science.,1(4), 1–10.
Kushwaha, M., & Raghuveer, V. R. (2017). Survey of impact of technology on effective implementation of precision farming in India. International Journal on Recent and Innovation Trends in Computing and Communication,5(6), 1300–1310.
Lokers, R., Knapen, R., Janssen, S., van Randen, Y., & Jansen, J. (2016). Analysis of Big Data technologies for use in agro-environmental science. Environmental Modelling & Software,84, 494–504.
Mahmud, M. S. A., Buyamin, S., Mokji, M. M., & Abidin, M. Z. (2018). Internet of things based smart environmental monitoring for mushroom cultivation. Indonesian Journal of Electrical Engineering and Computer Science,10(3), 847–852.
Mandal, S. K., & Maity, A. (2013). Precision farming for small agricultural farm: Indian scenario. American Journal of Experimental Agriculture,3(1), 200.
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials,20, 2923–2960.
Mondal, P., & Basu, M. (2009). Adoption of precision agriculture technologies in India and in some developing countries: Scope, present status and strategies. Progress in Natural Science,19(6), 659–666.
Mondal, P., Basu, M., Bhadoria, P. B. S., Emam, A. A., Salih, M. H., & Adegbite, A. A. (2011). Critical review of precision agriculture technologies and its scope of adoption in India. American Journal of Experimental Agriculture,1(3), 49–68.
Morota, G., Ventura, R. V., Silva, F. F., Koyama, M., & Fernando, S. C. (2018). Big Data analytics and precision animal agriculture symposium: Machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science,96(4), 1540–1550.
Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Computers and Electronics in Agriculture,118, 66–84.
Paraforos, D. S., Vassiliadis, V., Kortenbruck, D., Stamkopoulos, K., Ziogas, V., Sapounas, A. A., et al. (2016). A farm management information system using future internet technologies. IFAC-Papers OnLine,49, 324–329.
Patil, V. C., Nadagouda, B. T., & Al-Gaadi, K. A. (2013). Spatial variability and precision nutrient management in sugarcane. Journal of the Indian Society of Remote Sensing,41(1), 183–189.
Pivoto, D., Waquil, P. D., Talamini, E., Finocchio, C. P. S., Dalla Corte, V. F., & de Vargas Mores, G. (2018). Scientific development of smart farming technologies and their application in Brazil. Information Processing in Agriculture,5, 21–32.
Qiu, W., Dong, L., Wang, F., & Yan, H. (2014). Design of intelligentgreenhouse environment monitoring system based on ZigBee and embedded technology. In 2014 IEEE international conference on consumer electronics-China (pp. 1–3). IEEE.
Reddy, I. S., Rao, D. N., Babu, A. N., Ratnam, M. V., Kishore, P., & Rao, S. V. B. (2005). Studies on atmospheric gravity wave activity in the troposphere and lower stratosphere over a tropical station at Gadanki. Annales Geophysicae,23(10), 3237–3260.
Sankpal, A., & Warhade, K. K. (2015). Review of optoelectronic detection methods for the analysis of soil nutrients. International Journal of Advanced Computing and Electronics Technology (IJACET),2(2), 26–31.
Sharma, D., Bhondekar, A. P., Ojha, A., Shukla, A. K., & Ghanshyam, C. (2016). A technical assessment of IOT for Indian agriculture sector. In: 47th Mid-Term Symposium on Modern Information and Communication Technologies for Digital India. Chandigarh.
Shivanna, A. M., & Nagendrappa, G. (2014). Chemical analysis of soil samples to evaluate the soil fertility status of selected command areas of three tanks in Tiptur Taluk of Karnataka, India. Crops,6, 7.
Shu, H. (2016). Big data analytics: Six techniques. Geo-spatial Information Science,19(2), 119–128.
Singh, N. P. (2017). Application of data warehouse and big data technology in agriculture in India. In proceedings of VII seventh international conference on agricultural statistics (pp. 24–26), Rome, October.
Soman, S., Byju, G., & Bharathan, R. (2013). GIS based decision support system for precision farming of cassava in India. Acta BiologicaIndica,2(2), 394–399.
Takeshima, H., & Joshi, P. K. (2019). Protected agriculture, precision agriculture, and vertical farming: Brief reviews of issues in the literature focusing on the developing region in Asia. Washington: International Food Policy Research Institute.
Tiwari, A., & Jaga, P. K. (2012). Precision farming in India—A review. Outlook on Agriculture,41(2), 139–143.
Vinod, P. G. (2017). Development of topographic position index based on Jenness algorithm for precision agriculture at Kerala, India. Spatial Information Research,25(3), 381–388.
Wang, S. W., Feng, J., Liu, G., & Zhang, T. J. (2013). Multi-nesting spatial scales of soil heavy metals in farmland. NongyeJixieXuebao = Transactions of the Chinese Society for Agricultural Machinery,44(6), 128–135.
White, B. J., Amrine, D. E., & Larson, R. L. (2018). Big Data analytics and precision animal agriculture symposium: Data to decisions. Journal of Animal Science,96(4), 1531–1539.
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming—A review. Agricultural Systems,153, 69–80.
Yadav, V., & Yadav, P. (2014). Precision farming: A sustainable approach for organic horticulture production. Indian Horticulture Journal,4(1), 72–79.
Yang, X.-l., Zhu, B., & Li, Y.-l. (2013). Spatial and temporal patterns of soil nitrogen distribution under different land uses in a watershed in the hilly area of purple soil, China. Journal of Mountain science,10(3), 410–417.
Yin, Z., Lei, T. W., & Dong, Y. Q. (2013). Design and experiment of near infrared sensor for soil moisture measurement. NongyeJixieXuebao = Transactions of the Chinese Society for Agricultural Machinery,44(7), 72–73.
Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculturea worldwide overview. Computers and Electronics in Agriculture,36(2–3), 113–132.
Zhang, Q. (2015). Precision agriculture technology for crop farming. Boca Raton: CRC Press.
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Kolipaka, V.R.R. Predictive analytics using cross media features in precision farming. Int J Speech Technol 23, 57–69 (2020). https://doi.org/10.1007/s10772-020-09669-z
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DOI: https://doi.org/10.1007/s10772-020-09669-z