对植物工厂作物产量预测模型的评估和优化
Yaoqi Peng1,2,3, Yudong Zheng4, Zengwei Zheng2,3
1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Plants (Basel, Switzerland)
|July 30, 2025
概括
这项研究提高了植物工厂的作物产量预测,使用先进的图像分析来准确测量作物树冠投影面积 (CCPA). 广泛神经网络模型为改善作物管理提供了高度准确和高效的预测.
科学领域:
- 农业科学 农业科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 工厂种植需要精确的监测,以获得最佳产量.
- 准确的作物树冠面积估计对于产量预测至关重要.
- 现有的方法面临背景干扰和预测准确性的挑战.
研究的目的:
- 开发一种可靠的方法来测量植物工厂的作物树冠投射面积 (CCPA).
- 确定最佳的机器学习模型,以准确预测作物产量.
- 为了实现在种植系统中高效实时部署产量预测模型.
主要方法:
- 精确捕捉作物树冠图像和消除背景干扰.
- 计算空间分辨率和准确的CCPA测定 (R2=0.98).
- 对28个预测模型进行比较分析,使用MSE,RMSE,MAE,MAPE和R2等指标.
主要成果:
- 广大神经网络模型表现出优异的性能,R2=0.95,RMSE=27.15g,MAPE=11.74%.
- 最佳模型实现了60234.9个观测/秒的预测速度.
- 该模型的小尺寸 (7039字节) 便于实用,实时的应用.
结论:
- 开发的图像分析和宽神经网络方法显著提高了植物工厂的作物产量预测准确度.
- 这种方法为改进种植过程和提高作物产量提供了基础.
- 该模型的效率和准确性支持实际的作物管理和决策.
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