Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers
Agriculture is a growing field of research. In particular, crop prediction in agriculture is critical and is chiefly contingent upon soil and environment conditions, including rainfall, humidity, and temperature. In the past, farmers were able to decide on the crop to be cultivated, monitor its grow...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9721191/ |
| _version_ | 1819280389696389120 |
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| author | S. P. Raja Barbara Sawicka Zoran Stamenkovic G. Mariammal |
| author_facet | S. P. Raja Barbara Sawicka Zoran Stamenkovic G. Mariammal |
| author_sort | S. P. Raja |
| collection | DOAJ |
| description | Agriculture is a growing field of research. In particular, crop prediction in agriculture is critical and is chiefly contingent upon soil and environment conditions, including rainfall, humidity, and temperature. In the past, farmers were able to decide on the crop to be cultivated, monitor its growth, and determine when it could be harvested. Today, however, rapid changes in environmental conditions have made it difficult for the farming community to continue to do so. Consequently, in recent years, machine learning techniques have taken over the task of prediction, and this work has used several of these to determine crop yield. To ensure that a given machine learning (ML) model works at a high level of precision, it is imperative to employ efficient feature selection methods to preprocess the raw data into an easily computable Machine Learning friendly dataset. To reduce redundancies and make the ML model more accurate, only data features that have a significant degree of relevance in determining the final output of the model must be employed. Thus, optimal feature selection arises to ensure that only the most relevant features are accepted as a part of the model. Conglomerating every single feature from raw data without checking for their role in the process of making the model will unnecessarily complicate our model. Furthermore, additional features which contribute little to the ML model will increase its time and space complexity and affect the accuracy of the model’s output. The results depict that an ensemble technique offers better prediction accuracy than the existing classification technique. |
| first_indexed | 2024-12-24T00:43:01Z |
| format | Article |
| id | doaj.art-443174cfd4e4445192607da1d0fd8fd8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T00:43:01Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-443174cfd4e4445192607da1d0fd8fd82022-12-21T17:23:53ZengIEEEIEEE Access2169-35362022-01-0110236252364110.1109/ACCESS.2022.31543509721191Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and ClassifiersS. P. Raja0https://orcid.org/0000-0002-7216-2207Barbara Sawicka1https://orcid.org/0000-0002-8183-7624Zoran Stamenkovic2G. Mariammal3https://orcid.org/0000-0001-5515-3707School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Plant Production Technology and Commodities Science, University of Life Sciences in Lublin, Lublin, PolandIHP—Leibniz-Institut für innovative Mikroelektronik, Frankfurt (Oder), GermanyDepartment of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Tamil Nadu, IndiaAgriculture is a growing field of research. In particular, crop prediction in agriculture is critical and is chiefly contingent upon soil and environment conditions, including rainfall, humidity, and temperature. In the past, farmers were able to decide on the crop to be cultivated, monitor its growth, and determine when it could be harvested. Today, however, rapid changes in environmental conditions have made it difficult for the farming community to continue to do so. Consequently, in recent years, machine learning techniques have taken over the task of prediction, and this work has used several of these to determine crop yield. To ensure that a given machine learning (ML) model works at a high level of precision, it is imperative to employ efficient feature selection methods to preprocess the raw data into an easily computable Machine Learning friendly dataset. To reduce redundancies and make the ML model more accurate, only data features that have a significant degree of relevance in determining the final output of the model must be employed. Thus, optimal feature selection arises to ensure that only the most relevant features are accepted as a part of the model. Conglomerating every single feature from raw data without checking for their role in the process of making the model will unnecessarily complicate our model. Furthermore, additional features which contribute little to the ML model will increase its time and space complexity and affect the accuracy of the model’s output. The results depict that an ensemble technique offers better prediction accuracy than the existing classification technique.https://ieeexplore.ieee.org/document/9721191/Agricultureclassificationcrop predictionfeature selection |
| spellingShingle | S. P. Raja Barbara Sawicka Zoran Stamenkovic G. Mariammal Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers IEEE Access Agriculture classification crop prediction feature selection |
| title | Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers |
| title_full | Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers |
| title_fullStr | Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers |
| title_full_unstemmed | Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers |
| title_short | Crop Prediction Based on Characteristics of the Agricultural Environment Using Various Feature Selection Techniques and Classifiers |
| title_sort | crop prediction based on characteristics of the agricultural environment using various feature selection techniques and classifiers |
| topic | Agriculture classification crop prediction feature selection |
| url | https://ieeexplore.ieee.org/document/9721191/ |
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