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A Review on Crop Yield Prediction Using Learning Techniques


Affiliations
1 Research Scholar, Department of Computer Science, GNDU, Amritsar, India
2 Associate Professor, Department of Computer Science, GNDU, Amritsar, India
3 Assistant Professor, Department of Computer Engineering and Technology, GNDU, Amritsar, India
 

Food is regarded as a basic human requirement that is met through agriculture. Beyond meeting fundamental human requirements, agriculture is seen as a global source of employment in developing countries like India. Sustainable crop production is a persistent issue for farmers. Getting the best crop production has always been difficult for farmers since environmental circumstances are always changing. Land types, resource availability, and weather variability are the main causes of unpredictable crop yields. Therefore, scientists from all around the world are working to develop methods that can efficiently and accurately predict crop yields well in advance so that farmers may take the necessary steps to address upcoming issues. Crop production depends entirely on timely observation and advice. Farmers can reduce their losses if appropriate recommendations and information about the crop is provided. Machine learning is the prevailing technology that helps farmer to minimize agriculture losses. The study's primary goal is to explore different learning techniques used to predict crop yield. Reviews carried out in agriculture sector indicated a strong preference for deep learning methods and hybrid models for crop yield prediction.

Keywords

Machine Learning, Deep Learning, Crop Yield, Prediction.
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  • A Review on Crop Yield Prediction Using Learning Techniques

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Authors

Sandeep Kaur
Research Scholar, Department of Computer Science, GNDU, Amritsar, India
Gurvinder Singh
Associate Professor, Department of Computer Science, GNDU, Amritsar, India
Anil Kumar
Assistant Professor, Department of Computer Engineering and Technology, GNDU, Amritsar, India

Abstract


Food is regarded as a basic human requirement that is met through agriculture. Beyond meeting fundamental human requirements, agriculture is seen as a global source of employment in developing countries like India. Sustainable crop production is a persistent issue for farmers. Getting the best crop production has always been difficult for farmers since environmental circumstances are always changing. Land types, resource availability, and weather variability are the main causes of unpredictable crop yields. Therefore, scientists from all around the world are working to develop methods that can efficiently and accurately predict crop yields well in advance so that farmers may take the necessary steps to address upcoming issues. Crop production depends entirely on timely observation and advice. Farmers can reduce their losses if appropriate recommendations and information about the crop is provided. Machine learning is the prevailing technology that helps farmer to minimize agriculture losses. The study's primary goal is to explore different learning techniques used to predict crop yield. Reviews carried out in agriculture sector indicated a strong preference for deep learning methods and hybrid models for crop yield prediction.

Keywords


Machine Learning, Deep Learning, Crop Yield, Prediction.

References