新しいソフトウェアが作物の健康状態を測定する技術の精度を高める(New Software Boosts Accuracy of Tech to Measure Crop Health)

ad

2024-03-25 ノースカロライナ州立大学(NCState)

植物の葉の色を測定して健康を評価する電子デバイスの精度を向上させる新しいツールが、多分野の研究チームによって開発されました。この新技術は、センサーが色を認識する方法に影響を与える光の変動を考慮する能力を向上させることで機能します。このソフトウェアは、光の偏光度を測定し、センサーが感知した色と写真内の最も暗い波長の光の偏光度に基づいて葉の真の色を推定します。これにより、センサーが正確に葉の色を捉えることができます。

<関連情報>

ポラリメトリーを用いたハイパースペクトル画像における照明、葉、視野角依存性の軽減 Mitigating Illumination-, Leaf-, and View-Angle Dependencies in Hyperspectral Imaging Using Polarimetry

DANIEL KRAFFT , CLIFTON G. SCARBORO , WILLIAM HSIEH, COLLEEN DOHERTY , […], AND MICHAEL KUDENOV
Plant Phenomics  Published:22 Mar 2024
DOI:https://doi.org/10.34133/plantphenomics.0157

新しいソフトウェアが作物の健康状態を測定する技術の精度を高める(New Software Boosts Accuracy of Tech to Measure Crop Health)

Abstract

Automation of plant phenotyping using data from high-dimensional imaging sensors is on the forefront of agricultural research for its potential to improve seasonal yield by monitoring crop health and accelerating breeding programs. A common challenge when capturing images in the field relates to the spectral reflection of sunlight (glare) from crop leaves that, at certain solar incidences and sensor viewing angles, presents unwanted signals. The research presented here involves the convergence of 2 parallel projects to develop a facile algorithm that can use polarization data to decouple light reflected from the surface of the leaves and light scattered from the leaf’s tissue.

The first project is a mast-mounted hyperspectral imaging polarimeter (HIP) that can image a maize field across multiple diurnal cycles throughout a growing season. The second project is a multistatic fiber-based Mueller matrix bidirectional reflectance distribution function (mmBRDF) instrument which measures the polarized light-scattering behavior of individual maize leaves. The mmBRDF data was fitted to an existing model, which outputs parameters that were used to run simulations. The simulated data were then used to train a shallow neural network which works by comparing unpolarized 2-band vegetation index (VI) with linearly polarized data from the low-reflectivity bands of the VI. Using GNDVI and red-edge reflection ratio we saw an improvement of an order of magnitude or more in the mean error (ϵ) and a reduction spanning 1.5 to 2.7 in their standard deviation (ϵσ) after applying the correction network on the HIP sensor data.

1200農業一般
ad
ad
Follow
ad
タイトルとURLをコピーしました