A Hybrid Kalman-LSTM Framework for Real-Time Crop Yield  Prediction  Under Dynamic Environmental Conditions

Prabhkant Dwivedi,Roopali Gupta,Sindhu Kumar,Rajesh Choudhary

A Hybrid Kalman-LSTM Framework for Real-Time Crop Yield Prediction Under Dynamic Environmental Conditions

Authors

Prabhkant Dwivedi,Roopali Gupta,Sindhu Kumar,Rajesh Choudhary

Keywords:

Abstract

Accurate crop yield prediction is critical for food security, resource management,and agricultural planning, particularly under dynamically changing environmentalconditions. Traditional models often struggle to capture temporal dependencies and abrupt environmental variations

References

FAO, The State of Food Security and Nutrition in the World, Food and Agriculture Organization, 2020.

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Published

2023-09-20

How to Cite

Prabhkant Dwivedi,Roopali Gupta,Sindhu Kumar,Rajesh Choudhary. (2023). A Hybrid Kalman-LSTM Framework for Real-Time Crop Yield Prediction Under Dynamic Environmental Conditions. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2272–2282. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/4381