Abstract
The advent of technology in the agriculture sector, such as precision agriculture, the Internet of Things (IoT) and machine learning has dramatically improved the experience of farming scenario. Apart from improving the farming conditions, there is a need for focused effort to achieve a balanced ecosystem in the supply chain of agrilogistics. Inefficient price signals conveyed to the farmer, erratic price fluctuations and inflation of the agri-produce coupled with the presence of several intermediaries, tend to imbalance the system. In this work, we propose an IoT based agrilogistic system coupled with a genetic programming algorithm to ensure fair prices across all the participants within. The system evolves and generates a set of programs that, in turn, generates the selling rate for every participant in the supply chain in a manner that confers fairness.
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Bock, C.H., Nutter Jr., F.W.: Detection and measurement of plant disease symptoms using visible-wavelength photography and image analysis. Plant Sci. Rev. 6, 73 (2012)
Chand, R.: Development policies and agricultural markets. Econ. Polit. Wkly. 47(52), 53–63 (2012). http://www.jstor.org/stable/41720551
Gebbers, R., Adamchuk, V.I.: Precision agriculture and food security. Science 327(5967), 828–831 (2010). https://doi.org/10.1126/science.1183899. https://science.sciencemag.org/content/327/5967/828
Gondchawar, N., Kawitkar, R.: IoT based smart agriculture. Int. J. Adv. Res. Comput. Commun. Eng. 5(6), 838–842 (2016)
He, Y., Zeng, H., Fan, Y., Ji, S., Wu, J.: Application of deep learning in integrated pest management: a real-time system for detection and diagnosis of oilseed rape pests. Mob. Inf. Syst. (2019). https://doi.org/10.1155/2019/4570808
Kapetanovic, Z., Vasisht, D., Won, J., Chandra, R., Kimball, M.: Experiences deploying an always-on farm network. GetMobile Mob. Comp. Commun. 21(2), 16–21 (2017). https://doi.org/10.1145/3131214.3131220
King, A.: Technology: the future of agriculture. Nature 544 (2017). https://doi.org/10.1038/544S21a
Koza, J.R., Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press, Cambridge (1992)
Mat, I., Kassim, M.R.M., Harun, A.N., Yusoff, I.M.: IoT in precision agriculture applications using wireless moisture sensor network. In: 2016 IEEE Conference on Open Systems (ICOS), pp. 24–29. IEEE (2016)
Mau, M.: Supply chain management in agriculture - including economics aspects like responsibility and transparency. In: European Association of Agricultural Economists, 2002 International Congress, 28–31 August 2002, Zaragoza, Spain (2002)
Mekala, M.S., Viswanathan, P.: A survey: smart agriculture IoT with cloud computing. In: 2017 International Conference on Microelectronic Devices, Circuits and Systems (ICMDCS), pp. 1–7, August 2017. https://doi.org/10.1109/ICMDCS.2017.8211551
Negi, S., Anand, N.: Supply chain of fruits & vegetables’ agribusiness in uttarakhand (India): major issues and challenges. J. Supply Chain Manag. Syst. 4(1), 43–57 (2015)
Ozsen, L., Coullard, C.R., Daskin, M.S.: Capacitated warehouse location model with risk pooling. Naval Res. Logistics (NRL) 55(4), 295–312 (2008)
Patil, K.A., Kale, N.R.: A model for smart agriculture using IoT. In: 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), pp. 543–545, December 2016. https://doi.org/10.1109/ICGTSPICC.2016.7955360
Patil, S.S., Thorat, S.A.: Early detection of grapes diseases using machine learning and IoT. In: 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), pp. 1–5. IEEE (2016)
Shekhar, Y., Dagur, E., Mishra, S., Sankaranarayanan, S.: Intelligent iot based automated irrigation system. Int. J. Appl. Eng. Res. 12(18), 7306–7320 (2017)
TeamYS: Agri-logistics in India: challenges and emerging solutions (2013). https://yourstory.com/2013/05/agri-logistics-in-india-challenges-and-emerging-solutions/
Vasisht, D., et al.: FarmBeats: an IoT platform for data-driven agriculture. In: 14th USENIX Symposium on Networked Systems Design and Implementation (NSDI 17), pp. 515–529 (2017)
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Kulkarni, D.D., Nair, S.B. (2020). Agrilogistics - A Genetic Programming Based Approach. In: Pereira, P., Ribeiro, R., Oliveira, I., Novais, P. (eds) Society with Future: Smart and Liveable Cities. SC4Life 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 318. Springer, Cham. https://doi.org/10.1007/978-3-030-45293-3_7
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DOI: https://doi.org/10.1007/978-3-030-45293-3_7
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