Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems
The agriculture importance is not restricted to our daily life; it is also an effective field that enhances the economic growth in any country. Therefore, developing the quality of the crop yields using recent technologies is a crucial procedure to obtain competitive crops. Nowadays, data mining is...
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| Format: | Article |
| Language: | English |
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Qubahan
2021-05-01
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| Series: | Qubahan Academic Journal |
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| Online Access: | https://journal.qubahan.com/index.php/qaj/article/view/54 |
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| author | Halbast Rashid Ismael Adnan Mohsin Abdulazeez Dathar A. Hasan |
| author_facet | Halbast Rashid Ismael Adnan Mohsin Abdulazeez Dathar A. Hasan |
| author_sort | Halbast Rashid Ismael |
| collection | DOAJ |
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The agriculture importance is not restricted to our daily life; it is also an effective field that enhances the economic growth in any country. Therefore, developing the quality of the crop yields using recent technologies is a crucial procedure to obtain competitive crops. Nowadays, data mining is an emerging research field in agriculture especially in the predicting and analysis of crop yield. This paper focuses on utilizing various data mining classification algorithms to predict the impact of various parameters such as area, season and production on the crop yield quality. The performance of the decision tree, naive Bayes, random forest, support vector machine and K-nearest neighbour is measured and compared to each other. The comparison involves measuring the error values and accuracy. The SVM algorithm achieved the highest accuracy value with 76.82%. while the lowest is achieved by the KNN algorithm with 35.76%. The highest error value was 111.8855 for KNN. Also, the prediction help farmer to increased and improved the income level.
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| format | Article |
| id | doaj-art-66907c1c391940bf9149b351b1530b6e |
| institution | Kabale University |
| issn | 2709-8206 |
| language | English |
| publishDate | 2021-05-01 |
| publisher | Qubahan |
| record_format | Article |
| series | Qubahan Academic Journal |
| spelling | doaj-art-66907c1c391940bf9149b351b1530b6e2025-02-03T10:12:51ZengQubahanQubahan Academic Journal2709-82062021-05-011210.48161/qaj.v1n2a5454Comparative Study for Classification Algorithms Performance in Crop Yields Prediction SystemsHalbast Rashid Ismael0Adnan Mohsin Abdulazeez1Dathar A. Hasan2Technical College of Informatics-Akre Duhok Polytechnic University Duhok, IraqResearch Center Duhok Polytechnic University Duhok, IraqShekhan Technical Institute Duhok Polytechnic University, Duhok, Iraq The agriculture importance is not restricted to our daily life; it is also an effective field that enhances the economic growth in any country. Therefore, developing the quality of the crop yields using recent technologies is a crucial procedure to obtain competitive crops. Nowadays, data mining is an emerging research field in agriculture especially in the predicting and analysis of crop yield. This paper focuses on utilizing various data mining classification algorithms to predict the impact of various parameters such as area, season and production on the crop yield quality. The performance of the decision tree, naive Bayes, random forest, support vector machine and K-nearest neighbour is measured and compared to each other. The comparison involves measuring the error values and accuracy. The SVM algorithm achieved the highest accuracy value with 76.82%. while the lowest is achieved by the KNN algorithm with 35.76%. The highest error value was 111.8855 for KNN. Also, the prediction help farmer to increased and improved the income level. https://journal.qubahan.com/index.php/qaj/article/view/54data miningclassificationAgriculturecrop yield |
| spellingShingle | Halbast Rashid Ismael Adnan Mohsin Abdulazeez Dathar A. Hasan Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems Qubahan Academic Journal data mining classification Agriculture crop yield |
| title | Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems |
| title_full | Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems |
| title_fullStr | Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems |
| title_full_unstemmed | Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems |
| title_short | Comparative Study for Classification Algorithms Performance in Crop Yields Prediction Systems |
| title_sort | comparative study for classification algorithms performance in crop yields prediction systems |
| topic | data mining classification Agriculture crop yield |
| url | https://journal.qubahan.com/index.php/qaj/article/view/54 |
| work_keys_str_mv | AT halbastrashidismael comparativestudyforclassificationalgorithmsperformanceincropyieldspredictionsystems AT adnanmohsinabdulazeez comparativestudyforclassificationalgorithmsperformanceincropyieldspredictionsystems AT datharahasan comparativestudyforclassificationalgorithmsperformanceincropyieldspredictionsystems |