Abstract
The increasing of dates fruit development in Algeria becomes important for the next generations because it can enhance the national economy. To improve the production of this treasure more than more, we need to analyze and to monitor the previous production for giving the consequences that can make the best results. Since we have many farms with a large number of palms tree it is a difficult task to supervise and collect data in a short time. For this major problem, we need to integrate a set of components that can communicate together to support the farmers in collecting data. Therefore, the solution to this issue is to propose an intelligent architecture that uses a method that can help the expert to make decisions. In this work, we present a solution to forecast the dates fruit production based on historical data, in order to enhance the quality and the performance of the production in coming years. Moreover, to collect data in this work we use an intelligent technology with a drone to facilitate the collection operation. The implementation of this model has been provided in order to evaluate our system. The obtained results demonstrate the effectiveness of our proposed system.
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Bolman, B., Jak, R.G., van Hoof, L.: Unravelling the myth–the use of decisions support systems in marine management. Mar. Policy 87, 241–249 (2018)
Bhardwaj, R.K., Bhardwaj, V., Singh, D.P., Gautam, S.S., Saxena, R.R.: Modelling and forecasting of wheat production through structural time-series models in Chhattisgarh. Int. J. Pure App. Biosci. 5(5), 212–216 (2017)
Deshmukh, S.S., Paramasivam, R.: Forecasting of milk production in India with ARIMA and VAR time series models. Asian J. Dairy Food Res. 35(1), 17–22 (2016)
Amin, M., Amanullah, M., Akbar, A.: Time series modeling for forecasting wheat production of Pakistan. Plant Sci. 24(5), 1444–1451 (2014)
Lakshmanan, D., Meeran, A.N.: NFC logging mechanism—forensic analysis of NFC artefacts on android devices. In: Artificial Intelligence and Evolutionary Computations in Engineering Systems, pp. 93–101. Springer, Singapore (2017)
Floreano, D., Wood, R.J.: Science, technology and the future of small autonomous drones. Nature 521(7553), 460 (2015)
Yurish, S.Y., Gomes, M.T.: Smart sensors and MEMS: Proceedings of the NATO Advanced Study Institute on Smart Sensors and MEMS, Povoa de Varzim, Portugal, 8–19 September 2003, vol. 181. Springer Science & Business Media (2005)
Zouai, M., Kazar, O., Haba, B., Saouli, H., Benfenati, H.: IoT approach using multi-agent system for ambient intelligence. Int. J. Softw. Eng. Appl. 11(9), 15–32 (2017)
Cheung, W., Leung, L.C., Tam, P.C.F.: An intelligent decision support system for service network planning. Decis. Support Syst. 39, 415–428 (2005)
Wu, J.: Advances in K-means Clustering: a Data Mining Thinking. Springer, Heidelberg (2012)
Aloui, I., Kazar, O., Kahloul, L., Servigne, S.: A new itinerary planning approach among multiple mobile agents in wireless sensor networks (WSN) to reduce energy consumption. Int. J. Commun. Netw. Inf. Secur. (IJCNIS) 7(2), 116–122 (2015)
Bourbonnais, R., Michel, T.: Analyse des séries temporelles: applications à l’économie et à la gestion, edt. Dunod, Paris (2004)
Hamza, S., Abderaouf, G., Abdelhak, M., Okba, K.: A new cloud computing approach based SVM for relevant data extraction. In: Proceedings of the 2nd International Conference on Big Data, Cloud and Applications, p. 1. ACM, March 2017
Merizig, A., Kazar, O., Lopez-Sanchez, M.: A dynamic and adaptable service composition architecture in the cloud based on a multi-agent system. Int. J. Inf. Technol. Web Eng. (IJITWE) 13(1), 50–68 (2018)
Yang, T., Cuixia, L.: The study on livestock production prediction in heilongjiang province based on support vector machine. In: Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013), Published by Atlantis Press, Paris, France
Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific Model Dev. 7(3), 1247–1250 (2014)
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Merizig, A., Saouli, H., Zouai, M., Kazar, O. (2019). An Intelligent Approach for Enhancing the Agricultural Production in Arid Areas Using IoT Technology. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 911. Springer, Cham. https://doi.org/10.1007/978-3-030-11878-5_3
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DOI: https://doi.org/10.1007/978-3-030-11878-5_3
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