Revolutionising Crop Yield Prediction: The Synergy of Remote Sensing and Artificial Intelligence Technologies

Crop yield prediction is becoming progressively essential due to increasing concern about food securitySecurity. For anticipating the amount of food that will be available for the expanding global population, early crop yield prediction performs a significant role in avoiding food crisis. Yield prediction is an extremely significant agricultural dilemma. But, nowadays advanced digital technologiesDigital technologies help us understand things better by constantly observing and measuring various physical events and generating a substantial amount of data, called big data. Agricultural big data is used for some important activities like weed recognition, pests and diseases detection and forecasting and risk management. It is evident that satellite remote sensing has capacity to generate a sufficient volume of data. The multispectral satellite's geographical and temporal resolution allows them to provide a wealth of data on agricultural productivityProductivity. The raw bands from remote sensing technologies are utilised to develop vegetation indices, and features are retrieved and then combined with machine learning to forecast agricultural yield. Machine learning and artificial intelligence (AI)Artificial Intelligence (AI) approaches have been started recently to study big data. India'sIndia agriculture contributes significantly to its GDPGDP. The current statistics show that 17.32% of India's GDPGDP comes from agriculture. Therefore, the government urgently needs an appropriate method for predicting crop yields before harvest. Utilising artificial intelligence technologies in agriculture has potential to completely change the way how policies are made. This chapter includes the work of several researchers so that the present state of remote sensing and AI approaches can be comprehended to know the applications of AI and remote sensing technologies in crop yield prediction.