研究基于环境因素和外观表现型的结合的西红质量预测模型
Longwei Liang1,2, Zhaoyuan Wang1, Kaige Liu1,2
1College of Agriculture, Shihezi University, Shihezi 832003, China.
Plants (Basel, Switzerland)
|December 11, 2025
概括
这项研究引入了一种新的,低成本的方法,可以使用AI和标准RGB图像来预测西红的质量. 它可以实时,非破坏性监控精密农业.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 目前的非破坏性番茄质量评估方法昂贵,依赖专用设备,并与动态监测作斗争.
- 需要为温室番茄质量管理提供可访问的实时解决方案.
研究的目的:
- 开发一个集成的,低成本的,非破坏性的模型来预测多个番茄质量参数.
- 克服现有的光谱和动态监测技术的局限性.
主要方法:
- 一个综合模型,结合了环境预测的长短期记忆 (LSTM),成熟度预测的注意力机制 (GRU-AT) 的门式循环单元 (Gated Recurrent Unit),以及质量评估的深度神经网络 (DNN).
- 使用标准RGB图像作为所有预测模块的输入.
主要成果:
- 在环境预测方面,LSTM取得了很高的准确性 (R2 > 0.9559).
- GRU-AT模型在色比预测方面表现出色 (R2 > 0.86).
- DNN模型准确地预测了柳科,度和可溶性固体含量 (R2 > 0.811).
结论:
- 拟议的综合模型仅使用RGB图像准确预测关键番茄质量参数.
- 这种方法提供了一个低成本,低复杂度的解决方案,用于实时,在精密农业中进行非破坏性的质量监测.
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