Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory
This study focuses on enhancing crop yield prediction in plant factory environments through precise crop canopy image capture and background interference removal. This method achieves highly accurate recognition of the crop canopy projection area (CCPA), with a coefficient of determination (R<sup...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/14/14/2140 |
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| author | Yaoqi Peng Yudong Zheng Zengwei Zheng Yong He |
| author_facet | Yaoqi Peng Yudong Zheng Zengwei Zheng Yong He |
| author_sort | Yaoqi Peng |
| collection | DOAJ |
| description | This study focuses on enhancing crop yield prediction in plant factory environments through precise crop canopy image capture and background interference removal. This method achieves highly accurate recognition of the crop canopy projection area (CCPA), with a coefficient of determination (R<sup>2</sup>) of 0.98. A spatial resolution of 0.078 mm/pixel was derived by referencing a scale ruler and processing pixel counts, eliminating outliers in the data. Image post-processing focused on extracting the canopy boundary and calculating the crop canopy area. By incorporating crop yield data, a comparative analysis of 28 prediction models was performed, assessing performance metrics such as MSE, RMSE, MAE, MAPE, R<sup>2</sup>, prediction speed, training time, and model size. Among them, the Wide Neural Network model emerged as the most optimal. It demonstrated remarkable predictive accuracy with an R<sup>2</sup> of 0.95, RMSE of 27.15 g, and MAPE of 11.74%. Furthermore, the model achieved a high prediction speed of 60,234.9 observations per second, and its compact size of 7039 bytes makes it suitable for efficient, real-time deployment in practical applications. This model offers substantial support for managing crop growth, providing a solid foundation for refining cultivation processes and enhancing crop yields. |
| format | Article |
| id | doaj-art-0a4cd51b1efd4d9e901f5228d6343d57 |
| institution | Kabale University |
| issn | 2223-7747 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Plants |
| spelling | doaj-art-0a4cd51b1efd4d9e901f5228d6343d572025-08-20T03:32:27ZengMDPI AGPlants2223-77472025-07-011414214010.3390/plants14142140Evaluation and Optimization of Prediction Models for Crop Yield in Plant FactoryYaoqi Peng0Yudong Zheng1Zengwei Zheng2Yong He3College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaKey Laboratory of Crop Drought Resistance Research of Hebei Province, Institute of Dryland Farming, Hebei Academy of Agriculture and Forestry Sciences, Hengshui 053000, ChinaSchool of Computer and Computing Science, Hangzhou City University, Hangzhou 310015, ChinaCollege of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaThis study focuses on enhancing crop yield prediction in plant factory environments through precise crop canopy image capture and background interference removal. This method achieves highly accurate recognition of the crop canopy projection area (CCPA), with a coefficient of determination (R<sup>2</sup>) of 0.98. A spatial resolution of 0.078 mm/pixel was derived by referencing a scale ruler and processing pixel counts, eliminating outliers in the data. Image post-processing focused on extracting the canopy boundary and calculating the crop canopy area. By incorporating crop yield data, a comparative analysis of 28 prediction models was performed, assessing performance metrics such as MSE, RMSE, MAE, MAPE, R<sup>2</sup>, prediction speed, training time, and model size. Among them, the Wide Neural Network model emerged as the most optimal. It demonstrated remarkable predictive accuracy with an R<sup>2</sup> of 0.95, RMSE of 27.15 g, and MAPE of 11.74%. Furthermore, the model achieved a high prediction speed of 60,234.9 observations per second, and its compact size of 7039 bytes makes it suitable for efficient, real-time deployment in practical applications. This model offers substantial support for managing crop growth, providing a solid foundation for refining cultivation processes and enhancing crop yields.https://www.mdpi.com/2223-7747/14/14/2140crop canopy imageplant factorycrop yield predictionWide Neural Network |
| spellingShingle | Yaoqi Peng Yudong Zheng Zengwei Zheng Yong He Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory Plants crop canopy image plant factory crop yield prediction Wide Neural Network |
| title | Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory |
| title_full | Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory |
| title_fullStr | Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory |
| title_full_unstemmed | Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory |
| title_short | Evaluation and Optimization of Prediction Models for Crop Yield in Plant Factory |
| title_sort | evaluation and optimization of prediction models for crop yield in plant factory |
| topic | crop canopy image plant factory crop yield prediction Wide Neural Network |
| url | https://www.mdpi.com/2223-7747/14/14/2140 |
| work_keys_str_mv | AT yaoqipeng evaluationandoptimizationofpredictionmodelsforcropyieldinplantfactory AT yudongzheng evaluationandoptimizationofpredictionmodelsforcropyieldinplantfactory AT zengweizheng evaluationandoptimizationofpredictionmodelsforcropyieldinplantfactory AT yonghe evaluationandoptimizationofpredictionmodelsforcropyieldinplantfactory |