Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory
This work aims to construct an artificial neural network (ANN) ant colony algorithm (ACA)-based fine recognition system for plant factory seedling phenotypes. To address the problems of complexity and high delay of the plant recognition system in plant factories, first, multiple cameras at different...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/13/4/888 |
| _version_ | 1797606762266755072 |
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| author | Kaikang Chen Bo Zhao Liming Zhou Yongjun Zheng |
| author_facet | Kaikang Chen Bo Zhao Liming Zhou Yongjun Zheng |
| author_sort | Kaikang Chen |
| collection | DOAJ |
| description | This work aims to construct an artificial neural network (ANN) ant colony algorithm (ACA)-based fine recognition system for plant factory seedling phenotypes. To address the problems of complexity and high delay of the plant recognition system in plant factories, first, multiple cameras at different positions are employed to collect images of seedlings and construct 3D images. Then, the mask region convolutional neural networks (MRCNN) algorithm is adopted to analyze plant phenotypes. Finally, the optimized ACA is employed to optimize the process timing in the plant factory, thereby constructing a plant factory seedling phenotype fine identification system via ANN combined with ACA. Moreover, the model performance is analyzed. The results show that plants have four stages of phenotypes, namely, the germination stage, seedling stage, rosette stage, and heading stage. The accuracy of the germination stage reaches 97.01%, and the required test time is 5.64 s. Additionally, the optimization accuracy of the process timing sequence of the proposed model algorithm is maintained at 90.26%, and the delay and energy consumption are stabilized at 20.17 ms and 17.71, respectively, when the data volume is 6000 Mb. However, the problem of image acquisition occlusion in the process of 3D image construction still needs further study. Therefore, the constructed ANN-ACA-based fine recognition system for plant seedling phenotypes can optimize the process timing in a more real-time and lower energy consumption way and provide a reference for the integrated progression of unmanned intelligent recognition systems and complete sets of equipment for plant plants in the later stage. |
| first_indexed | 2024-03-11T05:19:40Z |
| format | Article |
| id | doaj.art-2c82055d6b6148679d7f378371bf9912 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-11T05:19:40Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-2c82055d6b6148679d7f378371bf99122023-11-17T17:54:55ZengMDPI AGAgriculture2077-04722023-04-0113488810.3390/agriculture13040888Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant FactoryKaikang Chen0Bo Zhao1Liming Zhou2Yongjun Zheng3Department of Electrical and Mechanical Engineering, College of Engineering, China Agricultural University, Beijing 100089, ChinaNational Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, ChinaNational Key Laboratory of Agricultural Equipment Technology, Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, ChinaDepartment of Electrical and Mechanical Engineering, College of Engineering, China Agricultural University, Beijing 100089, ChinaThis work aims to construct an artificial neural network (ANN) ant colony algorithm (ACA)-based fine recognition system for plant factory seedling phenotypes. To address the problems of complexity and high delay of the plant recognition system in plant factories, first, multiple cameras at different positions are employed to collect images of seedlings and construct 3D images. Then, the mask region convolutional neural networks (MRCNN) algorithm is adopted to analyze plant phenotypes. Finally, the optimized ACA is employed to optimize the process timing in the plant factory, thereby constructing a plant factory seedling phenotype fine identification system via ANN combined with ACA. Moreover, the model performance is analyzed. The results show that plants have four stages of phenotypes, namely, the germination stage, seedling stage, rosette stage, and heading stage. The accuracy of the germination stage reaches 97.01%, and the required test time is 5.64 s. Additionally, the optimization accuracy of the process timing sequence of the proposed model algorithm is maintained at 90.26%, and the delay and energy consumption are stabilized at 20.17 ms and 17.71, respectively, when the data volume is 6000 Mb. However, the problem of image acquisition occlusion in the process of 3D image construction still needs further study. Therefore, the constructed ANN-ACA-based fine recognition system for plant seedling phenotypes can optimize the process timing in a more real-time and lower energy consumption way and provide a reference for the integrated progression of unmanned intelligent recognition systems and complete sets of equipment for plant plants in the later stage.https://www.mdpi.com/2077-0472/13/4/888artificial neural networkplant factoryplant phenotypeant colony algorithmMRCNN |
| spellingShingle | Kaikang Chen Bo Zhao Liming Zhou Yongjun Zheng Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory Agriculture artificial neural network plant factory plant phenotype ant colony algorithm MRCNN |
| title | Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory |
| title_full | Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory |
| title_fullStr | Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory |
| title_full_unstemmed | Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory |
| title_short | Artificial Neural Network-Based Seedling Phenotypic Information Acquisition of Plant Factory |
| title_sort | artificial neural network based seedling phenotypic information acquisition of plant factory |
| topic | artificial neural network plant factory plant phenotype ant colony algorithm MRCNN |
| url | https://www.mdpi.com/2077-0472/13/4/888 |
| work_keys_str_mv | AT kaikangchen artificialneuralnetworkbasedseedlingphenotypicinformationacquisitionofplantfactory AT bozhao artificialneuralnetworkbasedseedlingphenotypicinformationacquisitionofplantfactory AT limingzhou artificialneuralnetworkbasedseedlingphenotypicinformationacquisitionofplantfactory AT yongjunzheng artificialneuralnetworkbasedseedlingphenotypicinformationacquisitionofplantfactory |