Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture
- Title
- Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture
- Creator
- Nagesh O.S.; Budaraju R.R.; Kulkarni S.S.; Vinay M.; Ajibade S.-S.M.; Chopra M.; Jawarneh M.; Kaliyaperumal K.
- Description
- Due to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent. The Author(s) 2024.
- Source
- Discover Sustainability, Vol-5, No. 1
- Date
- 2024-01-01
- Publisher
- Springer Nature
- Subject
- Accuracy; AdaBoost Decision Tree; Boosting, Crop Yield Prediction; Feature Selection; Gray Level Co-occurrence Matrix
- Coverage
- Nagesh O.S., Department of CSE, Anurag University, Hyderabad, India; Budaraju R.R., Oracle Inc., Austin, United States; Kulkarni S.S., Department of Information Technology, Sinhgad Academy of Engineering, SavitribaiPhule Pune University, Pune, India; Vinay M., Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Ajibade S.-S.M., Department of Computer Engineering, Istanbul Ticaret University, Istanbul, Turkey; Chopra M., Vivekananda Institute of Professional Studies, New Delhi, India; Jawarneh M., Faculty of Computing Sciences, Gulf College, Muscat, Oman; Kaliyaperumal K., IoT - HH Campus, AMBO University, Ambo, Ethiopia
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 26629984
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Nagesh O.S.; Budaraju R.R.; Kulkarni S.S.; Vinay M.; Ajibade S.-S.M.; Chopra M.; Jawarneh M.; Kaliyaperumal K., “Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/12735.