通过多式联机深度学习检测番茄 (Solanum lycopersicum L.) 的水状态的研究
Zhiyu Zuo1,2, Jindong Mu1, Wenjie Li1
1School of Agricultural Engineering, Jiangsu University, Zhenjiang, China.
Frontiers in plant science
|June 16, 2023
概括
检测番茄水的状态对于精确的灌至关重要. 多模式深度学习融合了RGB,NIR和深度图像,实现了高精度 (高达99.18%) 在番茄中非破坏性检测水状态.
科学领域:
- 园艺和作物科学 园艺和作物科学
- 计算机科学 计算机科学
- 农业工程 农业工程
背景情况:
- 准确检测番茄水的状态对于优化灌和确保作物产量至关重要.
- 评估植物水状态的传统方法可能是劳动密集型和破坏性的.
- 深度学习为植物生理条件的非破坏性监测提供了一个有希望的途径.
研究的目的:
- 开发和评估一种基于深度学习的方法,用于检测番茄植物的水状态.
- 研究融合多种图像模式 (RGB,NIR,深度) 的有效性,以改进水状态检测.
- 为了比较单模对比多模深度学习方法的性能.
主要方法:
- 在五个不同的灌水位下种植番茄,模拟各种水资源赤字和余条件.
- 获取了番茄植物的RGB,近红外 (NIR) 和深度图像.
- 训练并测试单模 (VGG-16,Resnet-50在单个图像类型上) 和多模深度学习模型 (图像类型的组合与VGG-16,Resnet-50).
主要成果:
- 单模深度学习模型的检测准确度从88.97%到93.09%不等.
- 多模式深度学习模型的准确性明显更高,从93.09%到99.18%不等.
- 一个最佳的模型结合了RGB的ResNet-50和深度和NIR图像的VGG-16,产生了最高的精度.
结论:
- 通过多式模式深度学习融合RGB,NIR和深度图像数据,与单式模式方法相比,大大提高了番茄水状况检测的准确性.
- 这项研究提出了一种新的,非破坏性的方法,用于实时监测番茄水的状态.
- 这些发现为在番茄种植中实施精确的灌策略提供了宝贵的参考.
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