文献类型： 外文期刊

作者： Peng, Yaoqi ¹ ; Zheng, Yudong ⁴ ; Zheng, Zengwei ² ; He, Yong ¹ ;

作者机构： 1.Zhejiang Univ, Coll Biosyst Engn & Food Sci, Hangzhou 310058, Peoples R China

2.Hangzhou City Univ, Sch Comp & Comp Sci, Hangzhou 310015, Peoples R China

3.Zhejiang Prov Engn Res Ctr Intelligent Plant Facto, Hangzhou 310015, Peoples R China

4.Hebei Acad Agr & Forestry Sci, Inst Dryland Farming, Key Lab Crop Drought Resistance Res Hebei Prov, Hengshui 053000, Peoples R China

关键词： crop canopy image; plant factory; crop yield prediction; Wide Neural Network

期刊名称：PLANTS-BASEL （ 影响因子：4.1； 五年影响因子：4.5 ）

ISSN： 2223-7747

年卷期： 2025 年 14 卷 14 期

页码：

收录情况： SCI

摘要： 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 (R2) 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, R2, 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 R2 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.