Introduction The agriculture industry is changing in recent years due to technological and data analytics breakthroughs, which are enabling data-driven problem solving of real-time issues. Crop yield prediction is one of the key areas where advanced technologies are applied. Based on yield estimates, farmers, the national food security department, policy makers, and stockholders can make educated judgments. It also oversees the best use of resources and the reduction of production-related risks in agriculture.
Objective To get exposures to big data techniques. To explore Big Query ML of google cloud for yield prediction. To create insightful dashboard for crop yield analysis using Google locker studio.
Flow chart of crop yield prediction
Step1:Data finding and downloading.
Step2:Create Tabel using SQL ML
Step3:Data importing in Big Query
Step4: Create regression models
Step5:Yield prediction Using different regression models
Step6:ML model evaluation
Step7:Dashboard creation