Unified Precision Farming: Advancing Crop Yield Prediction Using WSN and Edge Technology

Crop growth can be predicted based on rough estimation by farmers till now which results some time wrong calculations. Due to wrong prediction by their experiences and natural problems they could not succeed much in production. Several computer based solutions are available to predict crop stages from sowing to final harvesting. To give more accuracy in crop yield prediction scheme proposed a technology which comprises of WSN, UAV, Prediction algorithms and edge technology. Zigbee used to manage low data rates and low power consumption along with mess topology to communicate sensor nodes quickly and UAV (Unmanned Aerial Vehicle) is being used to kill dangerous insects in the crop with the help of sensor nodes connected to it. Model has been implemented extended Hybrid algorithms in sensor network for its connection processing. Finally the results are stored in cloud and the same analyzed to predict accuracy using both learning technique belongs to supervised and unsupervised. The final outcome of crop yield prediction using implemented model is 93 % accurate when compare to other existing model.