Mechanization of Agricultural Production in Developing Countries

Onwude, Daniel I.; Chen, Guangnan; Hashim, Norhashila; Esdaile, Jeff R.; Gomes, Chandima; Khaled, Alfadhl Yahya; Alonge, Akindele Folarin; Ikrang, Elijah

doi:10.1201/9781351132398-1

Advances in Agricultural Machinery and Technologies 2018

DOI: 10.1201/9781351132398-1

|View full text |Cite

Mechanization of Agricultural Production in Developing Countries

Daniel I. Onwude¹,

Guangnan Chen²,

Norhashila Hashim³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 31 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, developing countries have huge barriers to implement new technologies on their tractors. In these countries, farmers' income is very low which forces farmers to use very old tractors that are incompatible to use with new high-tech PA devices (Onwunde et al, 2018).…”

mentioning

confidence: 99%

Development of an IoT based (LoRaWAN) Tractor Tracking System

Civelek¹

2021

Tarım Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

mentioning

confidence: 99%

Development of an IoT based (LoRaWAN) Tractor Tracking System

Civelek¹

2021

Tarım Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

Comparing Machine Learning Algorithms for Rice Yield Prediction in Adamawa and Cross Rivers States of Nigeria

Abunimye Ingio,

Shey Nsang,

Iorliam

2024

Gazi University Journal of Science Part A: Engineering and Innovation

View full text Add to dashboard Cite

Rice production is critical for global food security, and accurate yield prediction empowers informed decision-making. This paper investigates machine learning (ML) techniques for rice yield prediction in Adamawa and Cross River states, with distinct agroclimatic conditions. Traditional yield prediction methods that are commonly used often have limitations such as less insights into the available data and reduced accuracy. Hence, this research explores the potential of machine learning for improved prediction accuracy. We leverage climatic data and historical rice yields to train and evaluate Decision Trees, Random Forest, Support Vector Regressor, Polynomial Regressor, Multiple Linear Regression and Long Short-Term Memory (LSTM) models. Performance is compared using Mean Squared Error, Root Mean Squared Error, Coefficient of Determination, Mean Absolute Error, and Mean Absolute Percentage Error. Feature selection identifies All-sky Photosynthetically Active Radiation (PAR) as the most influential factor. Linear Regression emerges as the superior model, achieving an R² of 0.90 (Adamawa) and 0.91 (Cross River), demonstrating robust generalizability across regions. This research contributes to the development of ML-powered Agro-information systems for two Nigerian regions, enhancing agricultural practices and food security.

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.

Mechanization of Agricultural Production in Developing Countries

Cited by 2 publications

References 31 publications

Development of an IoT based (LoRaWAN) Tractor Tracking System

Development of an IoT based (LoRaWAN) Tractor Tracking System

Comparing Machine Learning Algorithms for Rice Yield Prediction in Adamawa and Cross Rivers States of Nigeria

Contact Info

Product

Resources

About