Paddy yield prediction based on 2D images of rice panicles using regression techniques

Pankaj,; Kumar, Brajesh; Bharti, P. K.; Vishnoi, Vibhor Kumar; Kumar, Krishan; Mohan, Shashank; Singh, Krishan Pal

doi:10.1007/s00371-023-03092-6

Vis Comput

2023

DOI: 10.1007/s00371-023-03092-6

|View full text |Cite

Paddy yield prediction based on 2D images of rice panicles using regression techniques

Pankaj,

Brajesh Kumar,

P. K. Bharti

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Enhancing Rice Production Prediction in Indonesia Using Advanced Machine Learning Models

Erlin,

Yunianta,

Wulandhari

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Enhancing Rice Production Prediction in Indonesia Using Advanced Machine Learning Models

Erlin,

Yunianta,

Wulandhari

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Progress in Research on Deep Learning-Based Crop Yield Prediction

Wang,

Zhang,

et al. 2024

Agronomy

View full text Add to dashboard Cite

In recent years, crop yield prediction has become a research hotspot in the field of agricultural science, playing a decisive role in the economic development of every country. Therefore, accurate and timely prediction of crop yields is of great significance for the national formulation of relevant economic policies and provides a reasonable basis for agricultural decision-making. The results obtained through prediction can selectively observe the impact of factors such as crop growth cycles, soil changes, and rainfall distribution on crop yields, which is crucial for predicting crop yields. Although traditional machine learning methods can obtain an estimated crop yield value and to some extent reflect the current growth status of crops, their prediction accuracy is relatively low, with significant deviations from actual yields, and they fail to achieve satisfactory results. To address these issues, after in-depth research on the development and current status of crop yield prediction, and a comparative analysis of the advantages and problems of domestic and foreign yield prediction algorithms, this paper summarizes the methods of crop yield prediction based on deep learning. This includes analyzing and summarizing existing major prediction models, analyzing prediction methods for different crops, and finally providing relevant views and suggestions on the future development direction of applying deep learning to crop yield prediction research.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Paddy yield prediction based on 2D images of rice panicles using regression techniques

Cited by 2 publications

References 50 publications

Enhancing Rice Production Prediction in Indonesia Using Advanced Machine Learning Models

Enhancing Rice Production Prediction in Indonesia Using Advanced Machine Learning Models

Progress in Research on Deep Learning-Based Crop Yield Prediction

Contact Info

Product

Resources

About