High-precision estimation of grass quality and quantity using UAS-based VNIR and SWIR hyperspectral cameras and machine learning

Oliveira, Raquel Alves; Näsi, Roope; Korhonen, Panu; Mustonen, Arja; Niemeläinen, Oiva; Koivumäki, Niko; Hakala, Teemu; Suomalainen, Juha; Kaivosoja, Jere; Honkavaara, Eija

doi:10.1007/s11119-023-10064-2

Cited by 6 publications

(3 citation statements)

References 110 publications

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“…Furthermore, this study has not investigated grassland quality. Oliveira et al 49 recently demonstrated the potential of hyperspectral UAV imaging sensors in providing these quality parameters.…”

Section: Discussionmentioning

confidence: 99%

UAV LiDAR-based grassland biomass estimation for precision livestock management

Hütt,

Isselstein,

Komainda

et al. 2024

J. Appl. Rem. Sens.

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We present an approach for grassland management using uncrewed aerial vehicles (UAV) LIDAR data and statistical modeling techniques integrated within a softwarebased multi-level information system (SMI). The primary objective is to utilize UAV LiDAR data and statistical modeling techniques within an SMI to accurately estimate compressed sward height (CSH) and above-ground biomass for precision farming applications. As a case study, four UAV LiDAR flights were conducted over rotational grazing farmland, and the collected data were processed to a point cloud. A statistical model was developed to estimate CSH values (R 2 ¼ 0.59, RMSE = 5.9 cm) using LiDAR metrics of the point cloud data. In addition, destructive sampling of grassland facilitated the calibration process, enabling the modeling of biomass based on the CSH values, specifically expressed as above-ground herbage dry biomass (R 2 ¼ 0.89, RMSE ¼ 0.2669 Mg ha −1 ). The collected data further enabled the approximation of biomass across the entire area of interest, which covered ∼200 ha, utilizing a 2.5 × 2.5 m polygon grid. The data were subsequently transferred to an SMI, which operates on the same grid and complements the information, thus offering a comprehensive foundation for decision-making, optimizing grazing systems, and efficient resource allocation. We contribute to advancing precision farming and sustainable grassland management.

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Section: Discussionmentioning

confidence: 99%

UAV LiDAR-based grassland biomass estimation for precision livestock management

Hütt,

Isselstein,

Komainda

et al. 2024

J. Appl. Rem. Sens.

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“…The HS camera sampled spectral signatures with a relatively high spectral resolution (average of 8.98 nm) and FWHM (average of 6.9 nm) in the visible to near-infrared spectral range (500-900 nm). It is worth recognizing that the state-of-the-art technology offers enhanced-performance figures, for example, a spectral resolution of 2.6 nm and an FWHM of 5.5 nm in the spectral range of 400-1000 nm, as well as extending the spectral range beyond visible and near-infrared to the short-wave-infrared region [59]. Furthermore, this study does not apply spectral-band selection or handcrafted spectral features or indices that are commonly used in the context of classical machine learning approaches [13,20,59].…”

Section: Further Researchmentioning

confidence: 99%

“…It is worth recognizing that the state-of-the-art technology offers enhanced-performance figures, for example, a spectral resolution of 2.6 nm and an FWHM of 5.5 nm in the spectral range of 400-1000 nm, as well as extending the spectral range beyond visible and near-infrared to the short-wave-infrared region [59]. Furthermore, this study does not apply spectral-band selection or handcrafted spectral features or indices that are commonly used in the context of classical machine learning approaches [13,20,59]. Instead, we considered that the DNN models could find the relevant features from the spectral signatures.…”

Section: Further Researchmentioning

confidence: 99%

Comparison of Deep Neural Networks in the Classification of Bark Beetle-Induced Spruce Damage Using UAS Images

Turkulainen,

Honkavaara,

Näsi

et al. 2023

Remote Sensing

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The widespread tree mortality caused by the European spruce bark beetle (Ips typographus L.) is a significant concern for Norway spruce-dominated (Picea abies H. Karst) forests in Europe and there is evidence of increases in the affected areas due to climate warming. Effective forest monitoring methods are urgently needed for providing timely data on tree health status for conducting forest management operations that aim to prepare and mitigate the damage caused by the beetle. Unoccupied aircraft systems (UASs) in combination with machine learning image analysis have emerged as a powerful tool for the fast-response monitoring of forest health. This research aims to assess the effectiveness of deep neural networks (DNNs) in identifying bark beetle infestations at the individual tree level from UAS images. The study compares the efficacy of RGB, multispectral (MS), and hyperspectral (HS) imaging, and evaluates various neural network structures for each image type. The findings reveal that MS and HS images perform better than RGB images. A 2D-3D-CNN model trained on HS images proves to be the best for detecting infested trees, with an F1-score of 0.759, while for dead and healthy trees, the F1-scores are 0.880 and 0.928, respectively. The study also demonstrates that the tested classifier networks outperform the state-of-the-art You Only Look Once (YOLO) classifier module, and that an effective analyzer can be implemented by integrating YOLO and the DNN classifier model. The current research provides a foundation for the further exploration of MS and HS imaging in detecting bark beetle disturbances in time, which can play a crucial role in forest management efforts to combat large-scale outbreaks. The study highlights the potential of remote sensing and machine learning in monitoring forest health and mitigating the impacts of biotic stresses. It also offers valuable insights into the effectiveness of DNNs in detecting bark beetle infestations using UAS-based remote sensing technology.

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