Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture

Yeom, Junho; Jung, Jinha; Chang, Anjin; Ashapure, Akash; Maeda, Murilo M.; Maeda, Andrea; Landivar, Juan

doi:10.3390/rs11131548

Cited by 78 publications

(39 citation statements)

References 43 publications

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“…On the one hand, RGB cameras mounted on a multi-rotor drone can capture much finer spatial resolution imagery, which increases accuracy of CNN models [ 8 ], but covering smaller areas (due to battery limitations), which results in more expensive imagery per hectare. On the other hand, multispectral cameras mounted on fixed-wing drones can capture coarser spatial resolution imagery but on larger areas, which decreases the cost per hectare, and with the benefit of incorporating plant reflectance in the near-infrared, and red-edge, which better relate to photosynthetic activity than just RGB [ 47 ]. Fusing both sources of data could join the advantage of both approaches, i.e., increase CNN accuracy, decrease the cost per hectare, and incorporate photosynthetic activity information [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

Safonova

Guirado

Maglinets

et al. 2021

Sensors

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Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world’s olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index—NDVI—and green normalized difference vegetation index—GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel. All trained Mask R-CNN-based models showed high performance in the tree crown segmentation, particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%). The comparison in a subset of trees of our estimated biovolume with ground truth measurements showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV images.

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Section: Discussionmentioning

confidence: 99%

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

Safonova

Guirado

Maglinets

et al. 2021

Sensors

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“…Once the stereo pairs of the images are taken from the UAV camera sensors, these are processed using known control points and orthorectified based on the digital surface model (DSM) produced by the triangulation of the stereo pairs [9]. In many applications, the detection of vegetated areas is essential, as in the case of monitoring agricultural areas or forests [10][11][12][13]. Even if vegetation is not a goal of a study, vegetation needs to be masked out to produce a digital elevation model (DEM) and provide realistic contours of the area.…”

Section: Introductionmentioning

confidence: 99%

Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

Αγαπίου

2020

Drones

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Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing can be quite challenging. Usually, healthy vegetation can be extracted at the near-infrared part of the spectrum (approximately between 760–900 nm), which is not captured by the visible (RGB) cameras. In this study, we explore several visible (RGB) vegetation indices in different environments using various UAV sensors and cameras to validate their performance. For this purposes, openly licensed unmanned aerial vehicle (UAV) imagery has been downloaded “as is” and analyzed. The overall results are presented in the study. As it was found, the green leaf index (GLI) was able to provide the optimum results for all case studies.

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“…Alternatively, the VI calculated from remotely sensed data is a non-destructive method that is usually useful for chlorophyll content estimations and crop yield predictions. VI is commonly calculated using two or more remote-sensed bands that are in linear or non-linear combinations, and which are aimed at enhancing the properties of vegetation and the distributions of canopy structural variations [ 27 , 28 , 29 ]. Combined with evenly distributed, high-quality sampling data, the VI can be adopted to build regression models with the chlorophyll contents of crops.…”

Section: Introductionmentioning

confidence: 99%

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Guo

Wang

et al. 2020

Sensors

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The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R2s) were 0.462 and 0.570 in chlorophyll contents’ estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions.

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Comparison of Vegetation Indices Derived from UAV Data for Differentiation of Tillage Effects in Agriculture

Cited by 78 publications

References 43 publications

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

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