A fog computing-based IoT framework for prediction of crop disease using big data analytics

Pests and crop diseases have remained a constant threat to overall crop production quality and quantity throughout the ages. Therefore, their timely and accurate predictions could significantly reduce worldwide economic losses while also reducing the harmful environmental impact of fertilizers and pesticides. Cloud computing and the IoT can build an interconnected network in this circumstance. These two frameworks are not yet able to solve the problems of computing. Fog computing aims to push processing capabilities closer to target consumers, prevent overuse of cloud resources, and further reduce operational loads. The proposed approach to fog computing is applicable to the evolving field of precision agriculture, along with all agricultural land management strategies. We now need a method of forecasting that can accurately predict crop disease from the symptoms that are monitored.