Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry

Oliveira, Raquel Alves de; Näsi, Roope; Niemeläinen, Oiva; Nyholm, Laura; Alhonoja, Katja; Kaivosoja, Jere; Jauhiainen, Lauri; Viljanen, Niko; Nezami, Somayeh; Markelin, Lauri; Hakala, Teemu; Honkavaara, Eija

doi:10.1016/j.rse.2020.111830

Cited by 73 publications

(73 citation statements)

References 55 publications

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“…Therefore, some machine algorithms were compared to clarify which algorithms were effective for evaluating three photosynthetic pigments and their ratios. Random forests (RF) is an extremely successful algorithm for the classification and regression method [37] and has been used for chl estimation in previous studies [23,38]. The support vector machine (SVM) is another successful algorithm and has been widely used with a Gaussian kernel function [39], and it could be used as a benchmark with RF.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

et al. 2020

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Japanese horseradish (wasabi) grows in very specific conditions, and recent environmental climate changes have damaged wasabi production. In addition, the optimal culture methods are not well known, and it is becoming increasingly difficult for incipient farmers to cultivate it. Chlorophyll a, b and carotenoid contents, as well as their allocation, could be an adequate indicator in evaluating its production and environmental stress; thus, developing an in situ method to monitor photosynthetic pigments based on reflectance could be useful for agricultural management. Besides original reflectance (OR), five pre-processing techniques, namely, first derivative reflectance (FDR), continuum-removed (CR), de-trending (DT), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV), were compared to assess the accuracy of the estimation. Furthermore, five machine learning algorithms—random forest (RF), support vector machine (SVM), kernel-based extreme learning machine (KELM), Cubist, and Stochastic Gradient Boosting (SGB)—were considered. To classify the samples under different pH or sulphur ion concentration conditions, the end of the red edge bands was effective for OR, FDR, DT, MSC, and SNV, while a green-peak band was effective for CR. Overall, KELM and Cubist showed high performance and incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy. The best combinations were found to be DT–KELM for chl a (RPD = 1.511–5.17, RMSE = 1.23–3.62 μg cm−2) and chl a:b (RPD = 0.73–3.17, RMSE = 0.13–0.60); CR–KELM for chl b (RPD = 1.92–5.06, RMSE = 0.41–1.03 μg cm−2) and chl a:car (RPD = 1.31–3.23, RMSE = 0.26–0.50); SNV–Cubist for car (RPD = 1.63–3.32, RMSE = 0.31–1.89 μg cm−2); and DT–Cubist for chl:car (RPD = 1.53–3.96, RMSE = 0.27–0.74).

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Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

et al. 2020

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“…On the other hand, plant greenness can also relate to the digestibility of forage grasses. Digestibility can be measured routinely with NIRS (near-infrared spectroscopy) analyses, from which parameters as the D-value (digestibility of organic matter in dry matter), WSC (water soluble carbohydrates) or similar feed quality features can be predicted [96]. However, NIRS uses destructive sampling and is too costly and time-intensive for large scale application in breeding programs [97].…”

Section: Future Perspectivesmentioning

confidence: 99%

“…Several studies indicate that hand-held hyperspectral sensors can provide a non-destructive and fast alternative, possibly suited for breeding purposes [97][98][99]. Hyperspectral sensing from UAVs has been demonstrated to be suitable for estimating forage grass digestibility [96]. However, more research is needed to overcome several remaining challenges, which are particularly related to the poor general applicability of the established empirical regressions across the seasons and years [100,101].…”

Section: Future Perspectivesmentioning

confidence: 99%

Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

Swaef¹,

Maes

Aper³

et al. 2021

Remote Sensing

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The persistence and productivity of forage grasses, important sources for feed production, are threatened by climate change-induced drought. Breeding programs are in search of new drought tolerant forage grass varieties, but those programs still rely on time-consuming and less consistent visual scoring by breeders. In this study, we evaluate whether Unmanned Aerial Vehicle (UAV) based remote sensing can complement or replace this visual breeder score. A field experiment was set up to test the drought tolerance of genotypes from three common forage types of two different species: Festuca arundinacea, diploid Lolium perenne and tetraploid Lolium perenne. Drought stress was imposed by using mobile rainout shelters. UAV flights with RGB and thermal sensors were conducted at five time points during the experiment. Visual-based indices from different colour spaces were selected that were closely correlated to the breeder score. Furthermore, several indices, in particular H and NDLab, from the HSV (Hue Saturation Value) and CIELab (Commission Internationale de l’éclairage) colour space, respectively, displayed a broad-sense heritability that was as high or higher than the visual breeder score, making these indices highly suited for high-throughput field phenotyping applications that can complement or even replace the breeder score. The thermal-based Crop Water Stress Index CWSI provided complementary information to visual-based indices, enabling the analysis of differences in ecophysiological mechanisms for coping with reduced water availability between species and ploidy levels. All species/types displayed variation in drought stress tolerance, which confirms that there is sufficient variation for selection within these groups of grasses. Our results confirmed the better drought tolerance potential of Festuca arundinacea, but also showed which Lolium perenne genotypes are more tolerant.

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“…After weighing, the sample is dried in an oven to determine the amount of dry biomass [67]. Cut and dry is the most common reference data collection method in biomass estimation studies, and it has been used in various recent studies [69][70][71][72][73]. The method provides objective reference data, but the cutting phase can include lot of variation.…”

Section: Differences With Other Uav Imaging Applicationsmentioning

confidence: 99%

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

et al. 2021

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The development of UAV (unmanned aerial vehicle) imaging technologies for precision farming applications is rapid, and new studies are published frequently. In cases where measurements are based on aerial imaging, there is the need to have ground truth or reference data in order to develop reliable applications. However, in several precision farming use cases such as pests, weeds, and diseases detection, the reference data can be subjective or relatively difficult to capture. Furthermore, the collection of reference data is usually laborious and time consuming. It also appears that it is difficult to develop generalisable solutions for these areas. This review studies previous research related to pests, weeds, and diseases detection and mapping using UAV imaging in the precision farming context, underpinning the applied reference measurement techniques. The majority of the reviewed studies utilised subjective visual observations of UAV images, and only a few applied in situ measurements. The conclusion of the review is that there is a lack of quantitative and repeatable reference data measurement solutions in the areas of mapping pests, weeds, and diseases. In addition, the results that the studies present should be reflected in the applied references. An option in the future approach could be the use of synthetic data as reference.

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Machine learning estimators for the quantity and quality of grass swards used for silage production using drone-based imaging spectrometry and photogrammetry

Cited by 73 publications

References 55 publications

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses

Reference Measurements in Developing UAV Systems for Detecting Pests, Weeds, and Diseases

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