High Performance EDA and LDA Analysis: An Application for Wheat Yield Estimation

Kumar, Deepak; Kumar, Yash; Kukreja, Vinay; Bansal, Ankit; Bhattacherjee, Abhishek

doi:10.1109/access57397.2023.10200446

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other2

Article1

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Age Estimation from Gait Patterns Using a Convolutional Neural Network and Support Vector Machine Hybrid Model

Urvashi,

Kumar,

Malhotra

2024

2024 International Conference on Cybernation and Computation (CYBERCOM)

View full text Add to dashboard Cite

Age Estimation from Gait Patterns Using a Convolutional Neural Network and Support Vector Machine Hybrid Model

Urvashi,

Kumar,

Malhotra

2024

2024 International Conference on Cybernation and Computation (CYBERCOM)

View full text Add to dashboard Cite

qCrop: An IoT Based Framework to Enhance Crop Productivity in Smart Agriculture

Shamsa,

Rachakonda,

Mohanty

et al. 2024

2024 IFIP/IEEE 32nd International Conference on Very Large Scale Integration (VLSI-SoC)

View full text Add to dashboard Cite

Optimizing Crop Yield Prediction: Data-Driven Analysis and Machine Learning Modeling Using USDA Datasets

Yadav,

Seth,

Dembla

2024

Curr Agri Res Jour

View full text Add to dashboard Cite

This research uses a variety of machine learning models and exploratory data analysis (EDA) to forecast crop yields using USDA information from 2003 to 2013 in an effort to achieve precision agriculture. Not only did we want to predict agricultural output, but we also wanted to identify the underlying factors that affect yield. By means of thorough EDA, which encompassed a wide range of agricultural data, including weather patterns and USDA-sourced soil composition, we were able to gain important insights into the variables that impact differences in crop output. The thorough investigation that followed served as the basis for our machine learning modelling. We thoroughly assessed and contrasted the performance of a variety of machine learning algorithms, including Bagging Regressor, KNN, Decision Trees, Gradient Boost, Random Forest, and Linear Regression. The accuracy of the models varied noticeably, as the results showed: the Random Forest, Decision Trees, and Bagging Regressor models showed great accuracy, with respective values of 98.56%, 97.62%, and 98.59%. Conversely, KNN and Linear Regression showed reduced accuracy, indicating their limits in this situation. The robustness of our results was further improved by applying k-fold cross-validation, highlighting the significance of model validation in crop yield prediction. Some models showed changes in accuracy during cross-validation, which revealed more about their dependability. In addition to providing a thorough investigation of the variables affecting agricultural productivity, this study highlights the diverse forecasting powers of machine learning models. Our findings provide a path for well-informed agricultural decision-making by utilizing technology to optimize crop production estimates. The ultimate goal of this research is to support stakeholders in optimizing agricultural productivity and enable sustainable practices.

show abstract

High Performance EDA and LDA Analysis: An Application for Wheat Yield Estimation

Cited by 3 publications

References 12 publications

Age Estimation from Gait Patterns Using a Convolutional Neural Network and Support Vector Machine Hybrid Model

Age Estimation from Gait Patterns Using a Convolutional Neural Network and Support Vector Machine Hybrid Model

qCrop: An IoT Based Framework to Enhance Crop Productivity in Smart Agriculture

Optimizing Crop Yield Prediction: Data-Driven Analysis and Machine Learning Modeling Using USDA Datasets

Contact Info

Product

Resources

About