Artificial neural network in predicting rice yield
Rice production is one of the major sectors that play an important role on the national economy. Hence, site specific nutrient management is crucial for a sustainable agriculture. Therefore, precision agriculture and information technology is really important to balance crop productivity. The applic...
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| Format: | Conference or Workshop Item |
| Language: | English |
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Faculty of Engineering, Universiti Putra Malaysia
2012
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| Online Access: | http://psasir.upm.edu.my/id/eprint/50663/ http://psasir.upm.edu.my/id/eprint/50663/1/_TechnicalPapers_CAFEi2012_74.pdf |
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| author | Mustaffha, Samihah Bejo, Siti Khairunniza Wan Ismail, Wan Ishak |
| author_facet | Mustaffha, Samihah Bejo, Siti Khairunniza Wan Ismail, Wan Ishak |
| author_sort | Mustaffha, Samihah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Rice production is one of the major sectors that play an important role on the national economy. Hence, site specific nutrient management is crucial for a sustainable agriculture. Therefore, precision agriculture and information technology is really important to balance crop productivity. The application of neural network to the task of predicting crop yield is essential. The objectives of this paper were to: 1) investigate whether artificial neural network (ANN) model could predict rice yield based on soil parameters; 2) determine the most affected soil properties towards rice yield; 3) compare the effectiveness of multiple linear regression model to ANN. Models were developed using historical data collected in Block C, Sawah Sempadan, Selangor, Malaysia for two continuous seasons. Season 1 is dry season while Season 2 is wet season. External factors such as weather, farmer’s practices etc. were not being considered in this study. ANN showed more accurate results than regression model. ANN model resulted in r2 of 0.71 and 0.69 for Season1 and Season 2 respectively. While in linear regression, r2=0.12 and 0.02 for Season1 and Season 2 respectively. The results show that ANN model is more reliable than regression model in predicting rice yield. It can be conclude that ANN model is simple yet accurate. |
| first_indexed | 2025-11-15T10:25:03Z |
| format | Conference or Workshop Item |
| id | upm-50663 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:25:03Z |
| publishDate | 2012 |
| publisher | Faculty of Engineering, Universiti Putra Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-506632017-03-02T06:11:00Z http://psasir.upm.edu.my/id/eprint/50663/ Artificial neural network in predicting rice yield Mustaffha, Samihah Bejo, Siti Khairunniza Wan Ismail, Wan Ishak Rice production is one of the major sectors that play an important role on the national economy. Hence, site specific nutrient management is crucial for a sustainable agriculture. Therefore, precision agriculture and information technology is really important to balance crop productivity. The application of neural network to the task of predicting crop yield is essential. The objectives of this paper were to: 1) investigate whether artificial neural network (ANN) model could predict rice yield based on soil parameters; 2) determine the most affected soil properties towards rice yield; 3) compare the effectiveness of multiple linear regression model to ANN. Models were developed using historical data collected in Block C, Sawah Sempadan, Selangor, Malaysia for two continuous seasons. Season 1 is dry season while Season 2 is wet season. External factors such as weather, farmer’s practices etc. were not being considered in this study. ANN showed more accurate results than regression model. ANN model resulted in r2 of 0.71 and 0.69 for Season1 and Season 2 respectively. While in linear regression, r2=0.12 and 0.02 for Season1 and Season 2 respectively. The results show that ANN model is more reliable than regression model in predicting rice yield. It can be conclude that ANN model is simple yet accurate. Faculty of Engineering, Universiti Putra Malaysia 2012 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/50663/1/_TechnicalPapers_CAFEi2012_74.pdf Mustaffha, Samihah and Bejo, Siti Khairunniza and Wan Ismail, Wan Ishak (2012) Artificial neural network in predicting rice yield. In: International Conference on Agricultural and Food Engineering for Life (Cafei2012), 26-28 Nov. 2012, Palm Garden Hotel, Putrajaya. (pp. 232-234). http://cafei.upm.edu.my/download.php?filename=/TechnicalPapers/CAFEi2012_74.pdf |
| spellingShingle | Mustaffha, Samihah Bejo, Siti Khairunniza Wan Ismail, Wan Ishak Artificial neural network in predicting rice yield |
| title | Artificial neural network in predicting rice yield |
| title_full | Artificial neural network in predicting rice yield |
| title_fullStr | Artificial neural network in predicting rice yield |
| title_full_unstemmed | Artificial neural network in predicting rice yield |
| title_short | Artificial neural network in predicting rice yield |
| title_sort | artificial neural network in predicting rice yield |
| url | http://psasir.upm.edu.my/id/eprint/50663/ http://psasir.upm.edu.my/id/eprint/50663/ http://psasir.upm.edu.my/id/eprint/50663/1/_TechnicalPapers_CAFEi2012_74.pdf |