Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics
In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Chemosensors |
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| Online Access: | https://www.mdpi.com/2227-9040/10/2/45 |
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| author | Mohamed Farag Taha Alwaseela Abdalla Gamal ElMasry Mostafa Gouda Lei Zhou Nan Zhao Ning Liang Ziang Niu Amro Hassanein Salim Al-Rejaie Yong He Zhengjun Qiu |
| author_facet | Mohamed Farag Taha Alwaseela Abdalla Gamal ElMasry Mostafa Gouda Lei Zhou Nan Zhao Ning Liang Ziang Niu Amro Hassanein Salim Al-Rejaie Yong He Zhengjun Qiu |
| author_sort | Mohamed Farag Taha |
| collection | DOAJ |
| description | In the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics. |
| first_indexed | 2024-03-09T22:19:15Z |
| format | Article |
| id | doaj.art-76db38790270481ca8b655340e65fc3d |
| institution | Directory Open Access Journal |
| issn | 2227-9040 |
| language | English |
| last_indexed | 2024-03-09T22:19:15Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chemosensors |
| spelling | doaj.art-76db38790270481ca8b655340e65fc3d2023-11-23T19:17:00ZengMDPI AGChemosensors2227-90402022-01-011024510.3390/chemosensors10020045Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in AquaponicsMohamed Farag Taha0Alwaseela Abdalla1Gamal ElMasry2Mostafa Gouda3Lei Zhou4Nan Zhao5Ning Liang6Ziang Niu7Amro Hassanein8Salim Al-Rejaie9Yong He10Zhengjun Qiu11College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaAgricultural Engineering Department, Faculty of Agriculture, Suez Canal University, Ismailia 41522, EgyptCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaDepartment of Environmental Science & Technology, University of Maryland, College Park, MD 20742, USADepartment of Pharmacology & Toxicology, College of Pharmacy, King Saud University, Riyadh 11564, Saudi ArabiaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaIn the aquaponic system, plant nutrients bioavailable from fish excreta are not sufficient for optimal plant growth. Accurate and timely monitoring of the plant’s nutrient status grown in aquaponics is a challenge in order to maintain the balance and sustainability of the system. This study aimed to integrate color imaging and deep convolutional neural networks (DCNNs) to diagnose the nutrient status of lettuce grown in aquaponics. Our approach consists of multi-stage procedures, including plant object detection and classification of nutrient deficiency. The robustness and diagnostic capability of proposed approaches were evaluated using a total number of 3000 lettuce images that were classified into four nutritional classes—namely, full nutrition (FN), nitrogen deficiency (N), phosphorous deficiency (P), and potassium deficiency (K). The performance of the DCNNs was compared with traditional machine learning (ML) algorithms (i.e., Simple thresholding, K-means, support vector machine; SVM, k-nearest neighbor; KNN, and decision Tree; DT). The results demonstrated that the deep proposed segmentation model obtained an accuracy of 99.1%. Also, the deep proposed classification model achieved the highest accuracy of 96.5%. These results indicate that deep learning models, combined with color imaging, provide a promising approach to timely monitor nutrient status of the plants grown in aquaponics, which allows for taking preventive measures and mitigating economic and production losses. These approaches can be integrated into embedded devices to control nutrient cycles in aquaponics.https://www.mdpi.com/2227-9040/10/2/45deep convolutional neural networks (DCNNs)deep learningimage processingaquaponicsnutrient deficiency |
| spellingShingle | Mohamed Farag Taha Alwaseela Abdalla Gamal ElMasry Mostafa Gouda Lei Zhou Nan Zhao Ning Liang Ziang Niu Amro Hassanein Salim Al-Rejaie Yong He Zhengjun Qiu Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics Chemosensors deep convolutional neural networks (DCNNs) deep learning image processing aquaponics nutrient deficiency |
| title | Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics |
| title_full | Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics |
| title_fullStr | Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics |
| title_full_unstemmed | Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics |
| title_short | Using Deep Convolutional Neural Network for Image-Based Diagnosis of Nutrient Deficiencies in Plants Grown in Aquaponics |
| title_sort | using deep convolutional neural network for image based diagnosis of nutrient deficiencies in plants grown in aquaponics |
| topic | deep convolutional neural networks (DCNNs) deep learning image processing aquaponics nutrient deficiency |
| url | https://www.mdpi.com/2227-9040/10/2/45 |
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