Smart Irrigation and Crop Yield Prediction Using Wireless Sensor Networks and Machine Learning

  • D. L. ShanthiEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1037)


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.


Agriculture Precision agriculture Wireless Sensor Networks Crop yield prediction 


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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.BMS Institute of Technology and ManagementBangaloreIndia

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