Non-destructive Leaf Area Index estimation via guided optical imaging for large scale greenhouse environments
Non-destructive Leaf Area Index estimation via guided optical imaging for large scale greenhouse environments
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DOI：
10.1016/j.compag.2022.106911
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发表时间：
2022-04-04
影响因子：
8.3
通讯作者：
Watanabe, Shinya
中科院分区：
文献类型：
--
作者：
Baar, Stefan;Kobayashi, Yosuke;Watanabe, Shinya
提出了一种经济上可行的基于轨道的视频监测方法，该方法利用光学图像分割来估计温室番茄的冠层叶面积指数(LAI)。叶面积指数与作物生长的时效性直接相关，指示作物的健康状况和潜在的作物产量。一种轨道引导的移动摄像系统投入使用，通过扫描两种番茄植物的多行来记录连续图像，持续两年多。对单个图像帧进行UNT语义图像分割，以计算随时间推移的相对叶面积。本研究还描述了训练神经网络和评估分割结果所需的图像标注过程。结果被校准，并与种植者进行的基于落叶(破坏性)的叶面积指数估计进行比较。通过温室环境提供的受控环境和明确定义的边界条件以及受管理的测量条件，这一分段执行得很好。我们的结果与人工估算的叶面积指数的偏差不到10%。此外，我们能够最大限度地减少前景和背景植物以及其他障碍物之间的混淆，估计误差小于3%，这是产生可重现结果所必需的。
This paper presents a financially viable and non-destructive rail-based video monitoring method that utilizes optical image segmentation to estimate the canopy leaf area index (LAI) of greenhouse tomato plants. The LAI is directly related to the time-dependent crop growth and indicates plant health and potential crop yields. A railguided mobile camera system was commissioned that records continuous images by scanning multiple rows of two tomato plant species for over two years. UNET semantic image segmentation of the individual image frames was performed to compute the relative leaf area over time. This study also describes the image annotation process necessary to train the neural network and evaluate the segmentation results. The results are calibrated and compared to the defoliation-based (destructive) LAI estimation performed by the grower. This UNET segmentation performs well, which is enabled through the controlled environment and the well-defined boundary conditions provided by the greenhouse environment and the managed measurement conditions. Our results deviate from the manual LAI estimation by less than ten percent. Further, we are able to minimize confusion between foreground and background plants and other obstructions with an estimated error smaller than three percent, which is strictly necessary to produce reproducible results.