Crop Yield Prediction Enhancement Utilizing Deep Learning and Ensemble Algorithms

Khin, Ohnmar; Lee, Sung Keun

doi:10.1109/ictc55196.2022.9952748

Cited by 4 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This finding aligns with the Agricultural Knowledge and Information Systems (AKIS) framework, which underscores the pivotal role of knowledge dissemination in agricultural development (Tiedeman et al, 2022). Our results reinforce the proposition that well-informed farmers, empowered through training, contribute significantly to improved crop yield, validating the theoretical underpinnings of human capital theory in the agricultural domain (Cubillas et al, 2022;Khin & Lee, 2022).…”

Section: Discussionsupporting

confidence: 85%

Analyzing the Interplay of Technology Adoption, Farmer Training, Market Access, and Crop Yield: A Quantitative Survey in Agribusiness

Rusmayadi,

Salawati,

Widi

et al. 2024

IJBLE

View full text Add to dashboard Cite

This research delves into the intricate interplay of Technology Adoption (TA), Farmer Training (FT), Market Access (MA), and Crop Yield (CY) within the contemporary agribusiness landscape. With the global population burgeoning and climate change reshaping traditional paradigms, the need to enhance agricultural efficiency and resilience is paramount. Through a quantitative survey and Partial Least Squares Structural Equation Modeling (PLS SEM) analysis, the study explores the relationships among these pivotal factors. The findings underscore the significant impact of technology adoption, farmer training, and market access on crop yield, supported by established theories. The diverse respondent demographic, methodological rigor, and hypothesis testing further fortify the study's credibility. This research not only contributes to academic knowledge but also provides practical insights for policymakers and industry stakeholders, fostering a harmonious integration of key elements to enhance agribusiness sustainability.

show abstract

Section: Discussionsupporting

confidence: 85%

Analyzing the Interplay of Technology Adoption, Farmer Training, Market Access, and Crop Yield: A Quantitative Survey in Agribusiness

Rusmayadi,

Salawati,

Widi

et al. 2024

IJBLE

View full text Add to dashboard Cite

show abstract

“…Khin and Lee (2022) proposed a deep learning design based on ANNs. For the moved harvest dataset, the provided ANN structure was used, as well as other widely used methods such as the extra‐tees classifier, the stochastic gradient boosting (SGB) classifier, the linear discriminant analysis (LDA) classifier, the bagging classifier and the Classifier square discriminant analysis (QDA) used by India.…”

Section: Literature Surveymentioning

confidence: 99%

An Effective Soil Analysis and Crop Yield Prediction Based on Optimised Light GBM in Smart Agriculture

Parganiha,

Verma

2024

J Agronomy Crop Science

View full text Add to dashboard Cite

In the agricultural sector, crop yield prediction plays an important role as it helps farmers make decisions about the growing season and type of crops to get better yield. The main goal in the agricultural sector is to reduce operating costs and pollution by improving crop yields and quality. This paper proposes an effective method for soil analysis and crop yield prediction for intelligent agriculture. The collected data are preprocessed using missing value interpolation and data normalisation techniques. Feature selection is performed on the preprocessed data using the Aquila‐based adaptive optimisation algorithm, which selects the best trait subset for yield prediction. An improved lightweight gradient‐boosting machine based on the Battle Royale Optimisation technique is used for classification. The performance of the proposed system is evaluated using mean absolute error, root mean square error, R‐squared, mean square error, mean square logarithmic error and mean absolute percentage error, and the proposed system achieved an accuracy of 97%. The proposed system accurately predicts crop yields, improving crop production and quality.

show abstract

Crop Yield Prediction in India: A Comparative Analysis of Ensemble Techniques

Waghela,

Makadia,

Sheth

et al. 2024

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

Crop Yield Prediction Enhancement Utilizing Deep Learning and Ensemble Algorithms

Cited by 4 publications

References 2 publications

Analyzing the Interplay of Technology Adoption, Farmer Training, Market Access, and Crop Yield: A Quantitative Survey in Agribusiness

Analyzing the Interplay of Technology Adoption, Farmer Training, Market Access, and Crop Yield: A Quantitative Survey in Agribusiness

An Effective Soil Analysis and Crop Yield Prediction Based on Optimised Light GBM in Smart Agriculture

Crop Yield Prediction in India: A Comparative Analysis of Ensemble Techniques

Contact Info

Product

Resources

About