Agricultural Crop-Yield Prediction: Comparative Analysis Using Machine Learning Models
- Title
- Agricultural Crop-Yield Prediction: Comparative Analysis Using Machine Learning Models
- Creator
- Kumar K.P.; Kumar S.B.; Gupta A.D.; Johnson K.; Michael M.M.
- Description
- Machine learning (ML) is a crucial decision-support tool for predicting agricultural crop yields, enabling choices about which crops to grow and what to do while they are in the growing season. The research on agricultural production prediction has been supported by the application of several ML techniques. We employed a comparative analysis in this study to synthesize using three ML models, including linear regression, polynomial regression, and K-nearest neighbors (KNN), and extracted the results for the prediction of yield. Crop yield depends on a variety of aspects such as temperature, pesticide usage, rainfall, and even year due to changing climatic conditions. It is in our best interest to find out the crop yield based on these factors, as it will help in advancing the farming sector. These collected data have gone through preprocessing - i.e., cleaning, to ensure that no redundant or error data is used to train the ML models. Before we train the models, the dataset is divided into training and testing to provide the performance metrics of each model we use. The experimental results on predictions indicate KNN performs slightly better in comparison with linear regression and polynomial regression models. 2024 Taylor & Francis Group, LLC.
- Source
- Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture, pp. 158-177.
- Date
- 2024-01-01
- Publisher
- CRC Press
- Coverage
- Kumar K.P., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Kumar S.B., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Gupta A.D., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Johnson K., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India; Michael M.M., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-104003723-2; 978-103261892-0
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Kumar K.P.; Kumar S.B.; Gupta A.D.; Johnson K.; Michael M.M., “Agricultural Crop-Yield Prediction: Comparative Analysis Using Machine Learning Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/18080.