Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse
Photosynthesis is one of the essential processes in plant physiology that produces glucose and oxygen to support plant growth. Nutrient stress conditions will affect the photosynthetic rate in plants. The model predicting photosynthetic rates based on environmental conditions, nutrients, and plant...
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| Format: | Article |
| Language: | English |
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Bogor Agricultural University
2024-12-01
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| Series: | Hayati Journal of Biosciences |
| Online Access: | https://journal.ipb.ac.id/index.php/hayati/article/view/55631 |
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| _version_ | 1841554856943288320 |
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| author | Yohanes Bayu Suharto Herry Suhardiyanto Anas Dinurroman Susila Supriyanto |
| author_facet | Yohanes Bayu Suharto Herry Suhardiyanto Anas Dinurroman Susila Supriyanto |
| author_sort | Yohanes Bayu Suharto |
| collection | DOAJ |
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Photosynthesis is one of the essential processes in plant physiology that produces glucose and oxygen to support plant growth. Nutrient stress conditions will affect the photosynthetic rate in plants. The model predicting photosynthetic rates based on environmental conditions, nutrients, and plant types will be highly beneficial for farmers in tweaking these variables to maximize plant photosynthesis. This research focused on assessing the impact of nutrient stress on the photosynthetic rate in leaf vegetable crops and aimed to create a model using artificial neural networks (ANN) to predict photosynthetic rates under nutrient-stress conditions. Leaf vegetable crops were cultivated in a greenhouse using the NFT hydroponic system with eight nutrient conditions. This paper introduces an ANN model featuring nine input variables, ten hidden layers, and a single output. This model aims to elucidate the relationship between these inputs and the output parameter. The statistical analysis revealed a notable disparity in the CO2 assimilation rate among leaf vegetable crops subjected to nutrient stress treatment. The constructed ANN model demonstrated strong performance, achieving an R2 value of 0.9416, an RMSE of 1.5898 during training, and an R2 value of 0.9271 with an RMSE of 1.9649 in validation. A combination of statistical analysis and ANN modeling accurately explained the relationship and influence of input parameters, especially nutrient stress conditions, on the photosynthetic rate of leaf vegetable plants cultivated hydroponically in a greenhouse.
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| format | Article |
| id | doaj-art-4716aaa80d284f99911bcbeccc92d485 |
| institution | Kabale University |
| issn | 1978-3019 2086-4094 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Bogor Agricultural University |
| record_format | Article |
| series | Hayati Journal of Biosciences |
| spelling | doaj-art-4716aaa80d284f99911bcbeccc92d4852025-01-08T09:00:49ZengBogor Agricultural UniversityHayati Journal of Biosciences1978-30192086-40942024-12-0132210.4308/hjb.32.2.300-309Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in GreenhouseYohanes Bayu Suharto0Herry Suhardiyanto1Anas Dinurroman Susila2Supriyanto3Graduate Study Program on Agricultural Engineering Science, IPB University, Bogor 16680, Indonesia. Department of Agricultural Mechanization Technology, Bogor Agricultural Development Polytechnic, Bogor, IndonesiaDepartment of Mechanical and Biosystem Engineering, IPB University, Bogor 16680, IndonesiaDepartment of Agronomy and Horticulture, IPB University, Bogor 16680, IndonesiaDepartment of Mechanical and Biosystem Engineering, IPB University, Bogor 16680, Indonesia Photosynthesis is one of the essential processes in plant physiology that produces glucose and oxygen to support plant growth. Nutrient stress conditions will affect the photosynthetic rate in plants. The model predicting photosynthetic rates based on environmental conditions, nutrients, and plant types will be highly beneficial for farmers in tweaking these variables to maximize plant photosynthesis. This research focused on assessing the impact of nutrient stress on the photosynthetic rate in leaf vegetable crops and aimed to create a model using artificial neural networks (ANN) to predict photosynthetic rates under nutrient-stress conditions. Leaf vegetable crops were cultivated in a greenhouse using the NFT hydroponic system with eight nutrient conditions. This paper introduces an ANN model featuring nine input variables, ten hidden layers, and a single output. This model aims to elucidate the relationship between these inputs and the output parameter. The statistical analysis revealed a notable disparity in the CO2 assimilation rate among leaf vegetable crops subjected to nutrient stress treatment. The constructed ANN model demonstrated strong performance, achieving an R2 value of 0.9416, an RMSE of 1.5898 during training, and an R2 value of 0.9271 with an RMSE of 1.9649 in validation. A combination of statistical analysis and ANN modeling accurately explained the relationship and influence of input parameters, especially nutrient stress conditions, on the photosynthetic rate of leaf vegetable plants cultivated hydroponically in a greenhouse. https://journal.ipb.ac.id/index.php/hayati/article/view/55631 |
| spellingShingle | Yohanes Bayu Suharto Herry Suhardiyanto Anas Dinurroman Susila Supriyanto Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse Hayati Journal of Biosciences |
| title | Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse |
| title_full | Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse |
| title_fullStr | Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse |
| title_full_unstemmed | Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse |
| title_short | Artificial Neural Networks Model for Photosynthetic Rate Prediction of Leaf Vegetable Crops under Normal and Nutrient-Stressed in Greenhouse |
| title_sort | artificial neural networks model for photosynthetic rate prediction of leaf vegetable crops under normal and nutrient stressed in greenhouse |
| url | https://journal.ipb.ac.id/index.php/hayati/article/view/55631 |
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