Using hyperspectral plant traits linked to photosynthetic efficiency to assess N and P partition

Watt, Michael S.; Buddenbaum, Henning; Leonardo, Ellen Mae C.; Estarija, Honey Jane; Bown, Horacio E.; Gómez-Gallego, Mireia; Hartley, Robin; Massam, Peter; Wright, Liam; Zarco‐Tejada, Pablo J.

doi:10.1016/j.isprsjprs.2020.09.006

Cited by 22 publications

(8 citation statements)

References 142 publications

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“…Recently, these indices have been used as input data for prediction and classification purposes alike, the spectrum of tree canopies can be considered a distinctive feature of the specific vegetation, thus making VIs useful for both vegetation identification in aerial photographs and for tree classification (Abdollahnejad and Panagiotidis, 2020;Imangholiloo et al, 2020;Yang and Kan, 2020;Guo et al, 2021;Arevalo-Ramirez et al, 2022;Cabrera-Ariza et al, 2022;Shovon et al, 2022). Photosynthetic pigments have a distinctive reflectance in some bands, thus the prediction of chlorophyll content and other pigments is suitable with the appropriate VI (Watt et al, 2020;Kopackova-Strnadováet al, 2021;Lou et al, 2021;Lu et al, 2021;Raddi et al, 2021;Raj et al, 2021;Zhuo et al, 2022). Another application using VIs is the prediction of biomass in different and (Morgan et al, 2021;Torre-Tojal et al, 2022;Yan et al, 2022).…”

Section: Vegetation Indicesmentioning

confidence: 99%

“…Researchers focus on these five bands since most of the reviewed works use commercial infrared cameras that capture the radiation at these wavelengths. Other indices take advantage of the full spectrum and not only on specific bands but these indices are also obtained with the aid of a hyper-spectral camera or by a laboratory or hand-held spectrometer (Abdollahnejad and Panagiotidis, 2020;Watt et al, 2020;Yang and Kan, 2020;de Almeida et al, 2021;Raj et al, 2021;Villacreś and Cheein, 2022;Wan et al, 2022;Yang and Kan, 2022). Li et al (2021) of phosphorus and nitrogen, which is related to photosynthetic efficiency (Watt et al, 2020;Raj et al, 2021), the information gathered by hyperspectral indices, allows the processing data models to make more accurate predictions.…”

Section: Vegetation Indicesmentioning

confidence: 99%

“…Forest structure parameters and tree phenotypic features are predicted using machine learning techniques with input data gathered from LiDAR, RGB, and Multi-spectral cameras (Shin et al, 2018;McClelland et al, 2019;Puliti et al, 2019;Abdollahnejad and Panagiotidis, 2020;Fan et al, 2020;Imangholiloo et al, 2020;Ahmad et al, 2021;Cai et al, 2021;Neuville et al, 2021;Sangjan and Sankaran, 2021;Yu et al, 2021). Predictions of leaf moisture, chlorophyll, and nitrogen content, have been achieved using machine learning methods (Watt et al, 2020;Lou et al, 2021;Raddi et al, 2021;Raj et al, 2021;Narmilan et al, 2022;Zhuo et al, 2022). The most common predictor is linear regression, but other common ones are support vector machine regression, random forest regression, and gradient boost machines (McClelland et al, 2019;Blanco-Sacristań et al, 2021;Fraser and Congalton, 2021b;Yu et al, 2021;Torre-Tojal et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

et al. 2023

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Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future.

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Section: Vegetation Indicesmentioning

confidence: 99%

Section: Vegetation Indicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

et al. 2023

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“…Although many studies have established the models of phosphorus content in leaves or soil, its universality may be limited due to the lack of spectral absorption characteristics related to phosphorus [19,20]. Therefore, it is very important to explore a non-destructive, timely, and rapid monitoring technology for acquiring the phosphorus status [21].…”

Section: Introductionmentioning

confidence: 99%

Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels

Baiyu

Liu

et al. 2022

Remote Sensing

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As an essential element, the effect of Phosphorus (P) on plant growth is very significant. In the early growth stage of maize, it has a high sensitivity to the deficiency of phosphorus. The main purpose of this paper is to monitor the maize status under two phosphorus levels in soil by a nondestructive testing method and identify different phosphorus treatments by spectral data. Here, the Analytical Spectral Devices (ASD) spectrometer was used to obtain canopy spectral data of 30 maize inbred lines in two P-level fields, whose reflectance differences were compared and the sensitive bands of P were discovered. Leaf Area Index (LAI) and yield under two P levels were quantitatively analyzed, and the responses of different varieties to P content in soil were observed. In addition, the correlations between 13 vegetation indexes and eight phenotypic parameters were compared under two P levels so as to find out the best vegetation index for maize characteristics estimation. A Back Propagation (BP) neural network was used to evaluate leaf area index and yield, and the corresponding prediction model was established. In order to classify different P levels of soil, the method of support vector machine (SVM) was applied. The results showed that the sensitive bands of P for maize canopy included 763 nm, 815 nm, and 900–1000 nm. P-stress had a significant effect on LAI and yield of most varieties, whose reduction rate reached 41% as a whole. In addition, it was found that the correlations between vegetation indexes and phenotypic parameters were weakened under low-P level. The regression coefficients of 0.75 and 0.5 for the prediction models of LAI and yield were found by combining the spectral data under two P levels. For the P-level identification in soil, the classification accuracy could reach above 86%. These abilities potentially allow for phenotypic parameters prediction of maize plants by spectral data and different phosphorus contents identification with unknown phosphorus fertilizer status.

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“…Regardless of the high spectral accuracy, the time-consuming ASD measurement with a leaf clip only records signals at several positions within leaves, constraining its high-throughput agricultural application. Alternatively, hyperspectral sensors and cameras board on field-based phenotyping platforms [3,24,25] or manned aircraft and unmanned aerial vehicle remote sensing platforms [26] have a great advantage in estimating field-based photosynthetic capacity due to their flexibility and efficiency. Despite these commercial spectral systems coupled with high-throughput plant phenotyping platforms that can record spectral information with high spatial resolution, their application is still confounded mainly by two factors: (1) The recorded signals are easily complicated by soil background and canopy structure, thus the masking of these confounding factors to plant reflectance results in the lower signal-to-noise ratio of the recorded signal [27][28][29];…”

Section: Introductionmentioning

confidence: 99%

Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity

Niu

Xiao

et al. 2021

Remote Sensing

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High-throughput measurement of plant photosynthesis ability presents a challenge for the breeding process aimed to improve crop yield. As a novel technique, hyperspectral lidar (HSL) has the potential to characterize the spatial distribution of plant photosynthesis traits under less confounding factors. In this paper, HSL reflectance spectra of maize leaves were utilized for estimating the maximal velocity of Rubisco carboxylation (Vcmax) and maximum rate of electron transport at a specific light intensity (J) based on both reflectance-based and trait-based methods, and the results were compared with the commercial Analytical Spectral Devices (ASD) system. A linear combination of the Lambertian model and the Beckmann law was conducted to eliminate the angle effect of the maize point cloud. The results showed that the reflectance-based method (R2 ≥ 0.42, RMSE ≤ 28.1 for J and ≤4.32 for Vcmax) performed better than the trait-based method (R2 ≥ 0.31, RMSE ≤ 33.7 for J and ≤5.17 for Vcmax), where the estimating accuracy of ASD was higher than that of HSL. The Lambertian–Beckmann model performed well (R2 ranging from 0.74 to 0.92) for correcting the incident angle at different wavelength bands, so the spatial distribution of photosynthesis traits of two maize plants was visually displayed. This study provides the basis for the further application of HSL in high-throughput measurements of plant photosynthesis.

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Using hyperspectral plant traits linked to photosynthetic efficiency to assess N and P partition

Cited by 22 publications

References 142 publications

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

Machine learning assisted remote forestry health assessment: a comprehensive state of the art review

Maize Characteristics Estimation and Classification by Spectral Data under Two Soil Phosphorus Levels

Estimation of Maize Photosynthesis Traits Using Hyperspectral Lidar Backscattered Intensity

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