Smart Irrigation and Crop Yield Prediction Using Wireless Sensor Networks and Machine Learning
Agriculture decides the monetary development of country and is known to be its backbone. Farmers, specialists, and specialized makers are joining endeavors to discover more effective answers for taking care of different distinctive issues in agriculture to enhance current generation and procedures Precision. The proposed structure for exactness agriculture utilizes ease natural sensors, an Arduino Uno prototyping board and a couple of remote handsets (XBee ZB S2) alongside inciting circuit to give robotized water system and checking of harvests. The proposed model uses XBee convention which depends on ZigBee innovation. The vital attributes of ZigBee innovation ideal for accuracy agriculture are; low information rate, low power utilization and bigger scope region. Along these lines, because of previously mentioned attributes, ZigBee innovation happens to be the main decision for actualizing exactness farming.
The recently developing innovation i.e. Wireless Sensor Networks spread quickly into numerous fields resembles therapeutic, living space observing, bio-innovation and so forth. Yield forecast is an intricate phenomenon that is affected by agro-climatic information parameters. Agriculture input parameters shifts from field to field and rancher to agriculturist. Gathering such data on a bigger zone is an overwhelming errand. The colossal such informational indexes can be utilized for foreseeing their impact on significant yields of that specific region or place. There are diverse estimating strategies created and assessed by the analysts everywhere throughout the world in the field of agriculture or related sciences. Here we are providing comparative analysis of the results using different models.
KeywordsAgriculture Precision agriculture Wireless Sensor Networks Crop yield prediction
- 1.Math, R.K., Dharwadkar, N.V.: A wireless sensor network based low cost and energy efficient framework for precision agriculture. In: International Conference on Nascent Technologies in the Engineering Field (ICNTE-2017), pp. 1–6, 978-1-5090-2794-1/17Google Scholar
- 2.Veenadhari, S., Misra, B., Singh, C.D.: Machine learning approach for forecasting crop yield based on climatic parameters. In: International Conference on Computer Communication and Informatics (ICCCI-2014), Coimbatore, India, 03–05 January 2014Google Scholar
- 3.Kumbhar, H.: Wireless sensor network using XBee on Arduino platform an experimental study. In: International Conference on Computing Communication Control and Automation (ICCUBEA) (2016). https://doi.org/10.1109/iccubea.2016.7860081
- 4.Sahitya, G., Balaji, N., Naidu, C.D., Abinaya, S.: Designing a wireless sensor network for precision agriculture using ZigBee. In: International Advance Computing Conference (IACC) (2017). ISSN 2473-3571. https://doi.org/10.1109/iacc.2017.0069
- 5.Veenadhari, S., Misra, B., Singh, C.D.: Machine learning approach for forecasting crop yield based on climatic parameters. In: International Conference on Computer Communication and Informatics (ICCCI-2014)Google Scholar
- 7.Georgieva, T., Paskova, N., Gaazi, B., Todorov, G., Daskalov, P.: Design of wireless sensor network for monitoring of soil quality parameters. In: 5th International Conference on Agriculture for Life, Life for Agriculture, pp. 431–437 (2016). ScienceDirect. Agricultural and Agricultural Science Procedia 10CrossRefGoogle Scholar
- 8.Hamouda, Y.E.M., Elhabil, B.H.Y.: Precision agriculture for greenhouses using a wireless sensor network. In: Palestinian International Conference on Information and Communication Technology, pp. 78–83 (2017). https://doi.org/10.1109/picict.2017.20
- 9.Saraf, S.B., Gawali, D.H.: IoT based smart irrigation monitoring and controlling system. In: 2nd IEEE International Conference On Recent Trends in Electronics Information and Communication Technology (RTEICT), India, pp. 815–819, 19–20 May 2017Google Scholar
- 10.Mat, I., Kassim, M.R.M., Harun, A.N., Yusoff, I.M.: IoT in precision agriculture applications using wireless moisture sensor network. In: IEEE Conference on Open Systems (ICOS), Langkawi, Malaysia, pp. 24–29, 10–12 October 2016Google Scholar
- 11.Menak, K., Yuvaraj, N.: A survey on crop yield prediction models. Indian J. Innovations Dev. 5(12), 1–7 (2016)Google Scholar