Optimization Ensemble Learning Techniques for Reliable Crop Yield Prediction using ML
- Title
- Optimization Ensemble Learning Techniques for Reliable Crop Yield Prediction using ML
- Creator
- Upreti K.; Lingareddy N.; Deepika S.; Kumar N.; Parashar J.; Divakaran P.
- Description
- The agricultural sector's increasing reliance on technology has paved the way for advanced data-driven methodologies, with crop yield prediction emerging as a critical focus. This study dives into the complex landscape of crop yield prediction, employing a comprehensive approach that involves data preprocessing, model development, and performance evaluation. This research goes into enhancing crop yield prediction through a thorough data-driven approach. Beginning with comprehensive data preprocessing, including outlier analysis and feature scaling, the study ensures dataset integrity. Ensemble learning, employing Gradient Boosting Regressor, Random Forest Regressor and Decision Tree Regressor, captures intricate relationships within the dataset. Model performance, assessed through R-squared scores, demonstrates promising predictive capabilities. Subsequent outlier analysis and hyperparameter tuning yield substantial improvements, contributing valuable insights for agricultural decision-making. The research not only advances crop yield prediction but also offers practical guidance for integrating machine learning into agriculture, promising transformative outcomes for sustainable practices. The research also highlights how significant interpretability is to machine learning models so that stakeholders can comprehend and embrace them. This allows for a smooth integration of the models into current agricultural practices and encourages openness and reliability in decision-making. 2024 IEEE.
- Source
- Proceedings - 2024 1st International Conference on Technological Innovations and Advance Computing, TIACOMP 2024, pp. 431-436.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Decision Tree Regressor; Ensemble Learning; Gradient Boosting Regressor; Hyperparameter tuning; Outlier Analysis; R-squared Score; Random Forest Regressor
- Coverage
- Upreti K., Christ (Deemed to Be University), Dept. of Computer Science, Delhi NCR, India; Lingareddy N., Vignan Institute of Technology and Science, Department of Cse, Telangana, India; Deepika S., Sreyas Institute of Engineering and Technology, Department of Cse, Telangana, Hyderabad, India; Kumar N., Christ (Deemed to Be University), School of Business and Management, Bangalore, India; Parashar J., Adgitm, Dept. of Computer Science & Engg, India; Divakaran P., Himalayan University, Arunachal Pradesh, Itanagar, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835039211-1
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Upreti K.; Lingareddy N.; Deepika S.; Kumar N.; Parashar J.; Divakaran P., “Optimization Ensemble Learning Techniques for Reliable Crop Yield Prediction using ML,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19025.