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无人机高度变化对马铃薯作物生长精确估计的影响。

Effect of varying UAV height on the precise estimation of potato crop growth.

作者信息

Njane Stephen Njehia, Tsuda Shogo, van Marrewijk Bart M, Polder Gerrit, Katayama Kenji, Tsuji Hiroyuki

机构信息

Hokkaido Agricultural Research Center, National Agriculture and Food Research Organization, Memurocho, Kasaigun, Hokkaido, Japan.

Wageningen Greenhouse Horticulture, Wageningen University and Research, Wageningen, Netherlands.

出版信息

Front Plant Sci. 2023 Aug 17;14:1233349. doi: 10.3389/fpls.2023.1233349. eCollection 2023.

Abstract

A phenotyping pipeline utilising DeepLab was developed for precisely estimating the height, volume, coverage and vegetation indices of European and Japanese varieties. Using this pipeline, the effect of varying UAV height on the precise estimation of potato crop growth properties was evaluated. A UAV fitted with a multispectral camera was flown at a height of 15 m and 30 m in an experimental field where various varieties of potatoes were grown. The properties of plant height, volume and NDVI were evaluated and compared with the manually obtained parameters. Strong linear correlations with R of 0.803 and 0.745 were obtained between the UAV obtained plant heights and manually estimated plant height when the UAV was flown at 15 m and 30 m respectively. Furthermore, high linear correlations with an R of 0.839 and 0.754 were obtained between the UAV-estimated volume and manually estimated volume when the UAV was flown at 15 m and 30 m respectively. For the vegetation indices, there were no observable differences in the NDVI values obtained from the UAV flown at the two heights. Furthermore, high linear correlations with R of 0.930 and 0.931 were obtained between UAV-estimated and manually measured NDVI at 15 m and 30 m respectively. It was found that UAV flown at the lower height had a higher ground sampling distance thus increased resolution leading to more precise estimation of both the height and volume of crops. For vegetation indices, flying the UAV at a higher height had no effect on the precision of NDVI estimates.

摘要

开发了一种利用深度卷积神经网络(DeepLab)的表型分析流程,用于精确估计欧洲和日本品种的高度、体积、覆盖率和植被指数。使用该流程,评估了无人机高度变化对马铃薯作物生长特性精确估计的影响。在种植各种马铃薯品种的试验田中,一架配备多光谱相机的无人机分别在15米和30米的高度飞行。对株高、体积和归一化植被指数(NDVI)的特性进行了评估,并与手动获取的参数进行了比较。当无人机分别在15米和30米高度飞行时,无人机获取的株高与手动估计的株高之间获得了较强的线性相关性,相关系数R分别为0.803和0.745。此外,当无人机分别在15米和30米高度飞行时,无人机估计的体积与手动估计的体积之间获得了较高线性相关性,相关系数R分别为0.839和0.754。对于植被指数,在两个高度飞行的无人机获得的NDVI值没有明显差异。此外,在15米和30米高度时,无人机估计的NDVI与手动测量的NDVI之间分别获得了较高的线性相关性,相关系数R分别为0.930和0.931.研究发现,在较低高度飞行的无人机具有更高的地面采样距离,从而提高了分辨率,能够更精确地估计作物的高度和体积。对于植被指数而言,在较高高度飞行无人机对NDVI估计的精度没有影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4e4a/10470036/8cfee5b4bcf7/fpls-14-1233349-g001.jpg

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