Vegetation Detection Using Deep Learning and Conventional Methods

Ayhan, Bulent; Kwan, Chiman; Budavari, Bence; Kwan, Li-Yun Martin; Lu, Yan; Pérez, Daniel; Li, Jiang; Skarlatos, Dimitrios; Vlachos, Marinos

doi:10.3390/rs12152502

Cited by 65 publications

(56 citation statements)

References 40 publications

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“…Some studies suggested that ML classifiers are more practical than DL classifiers in vegetation segmentation, although the later may outperform ML classifiers. This is due to that high-accurate DL classifiers normally require larger training datasets and computational capacity when compared with ML classifiers, and DL networks also need to be trained for each different site and growth stage (Ayhan et al 2020;Bhatnagar et al 2020b).…”

Section: Discussionmentioning

confidence: 99%

Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops

Chapman

Zheng

2021

Funct. Plant Biol.

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Ground coverage (GC) allows monitoring of crop growth and development and is normally estimated as the ratio of vegetation to the total pixels from nadir images captured by visible-spectrum (RGB) cameras. The accuracy of estimated GC can be significantly impacted by the effect of 'mixed pixels', which is related to the spatial resolution of the imagery as determined by flight altitude, camera resolution and crop characteristics (fine vs coarse textures). In this study, a two-step machine learning method was developed to improve the accuracy of GC of wheat (Triticum aestivum L.) estimated from coarse-resolution RGB images captured by an unmanned aerial vehicle (UAV) at higher altitudes. The classification tree-based per-pixel segmentation (PPS) method was first used to segment fine-resolution reference images into vegetation and background pixels. The reference and their segmented images were degraded to the target coarse spatial resolution. These degraded images were then used to generate a training dataset for a regression tree-based model to establish the sub-pixel classification (SPC) method. The newly proposed method (i.e. PPS-SPC) was evaluated with six synthetic and four real UAV image sets (SISs and RISs, respectively) with different spatial resolutions. Overall, the results demonstrated that the PPS-SPC method obtained higher accuracy of GC in both SISs and RISs comparing to PPS method, with root mean squared errors (RMSE) of less than 6% and relative RMSE (RRMSE) of less than 11% for SISs, and RMSE of less than 5% and RRMSE of less than 35% for RISs. The proposed PPS-SPC method can be potentially applied in plant breeding and precision agriculture to balance accuracy requirement and UAV flight height in the limited battery life and operation time.

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Section: Discussionmentioning

confidence: 99%

Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops

Chapman

Zheng

2021

Funct. Plant Biol.

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“…Unlike the methods mentioned above, an alternative approach was developed by selecting limited band information from the hyperspectral image and increasing features by morphological calculation. In the remote sensing area, Kwan et al [38] proposed to utilize RGB-NIR bands and augment features by EMAP [45] which enlarges feature map considering morphological attribute for land cover classification and achieved better performance than using hyperspectral images.…”

Section: Related Workmentioning

confidence: 99%

Optical Filter Net: A Spectral-Aware RGB Camera Framework for Effective Green Pepper Segmentation

Kurihara

Shu

2021

IEEE Access

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Detecting and identifying objects of similar color is a challenging task in computer vision. Green peppers in a natural environment can be found using the abundant information provided by a hyperspectral camera in the spectral domain, but the hyperspectral camera is an expensive device. Therefore, we propose a novel framework called Optical Filter Net, which enables the design of an optical filter that improves the performance of green pepper segmentation by a specific red-green-blue (RGB) camera system. When installed with the optical filter, the system can efficiently utilize the spectral information in the visible wavelength to distinguish green pepper and foliage without requiring an expensive hyperspectral camera. A main finding is the similarity between the transmission curve of the optical filter and the depth-wise convolution kernel without bias. Accordingly, we can treat the transmission curve of the optical filter as one layer of a deep neural network. The whole structure can be trained in an end-to-end manner. To comply with the physical requirement of the optical filter, we further constrain the training process to achieve a non-negative and smooth transmission curve. In an experimental evaluation on our dataset, our proposed spectral-aware RGB camera system outperformed the RGB camera system without an optical filter.INDEX TERMS Optical filter, transmission curve, green pepper segmentation, deep neural network

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“…For carrying out phase-1, the experimentation on stateof-the-art SR techniques for the test images [1][2][3][4] was performed. This incorporation renders finely grained spectral homogenous patterns in regular shapes to define vegetation regions accurately.…”

Section: Sr Experimentation For Test Imagementioning

confidence: 99%

“…Land cover classification, emphasizing chlorophyll-rich vegetation identification, is critical for monitoring and planning urban development, autonomous navigation, drone mapping, biodiversity conservation, and other applications. The vegetation detection methods were used on RGB and nearinfrared (NIR) bands in high-resolution airborne color images with the application of applying the normalized difference vegetation index (NDVI) for vegetation detection [4]. However, the leaves' properties, the stems, other canopy components, and the background soil depend on vegetation canopies' spectral signatures.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of Super-Resolution Technique on Vegetation Indices

Rohith

Kumar

2021

IEEE Access

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The identification and interpretation of remote sensed (RS) objects in an image depend on how well the sensor captures the region. In rare cases, RS images may be vulnerable to a lack of interpretability issues in some parts of the image due to the sensor's limits and preprocessing techniques. Conventionally, the interpretation of the land cover pattern's shape and size is apparent when the distance between the sensor and object is closer to the visualization level of objects and viable with digital airborne imagery. In this paper, integration of the Super-Resolution (SR) technique in the high-resolution imagery to achieve the closer visualization level for mapping the vegetation is proposed. This approach enables the higher interpretive potential to define the land pattern's shape and size with very high spatial resolution, closer proximity, detailed and distinguishable patterns. This approach helps to precisely predict the total vegetated study area for land use and land cover changes (LULCC) and chlorophyll-rich vegetation applications. The proposed algorithm is carried out in two phases. In the first phase, the SR technique applied test images are tested for vegetation detection and mapping the vegetation information in a region with fourteen vegetation metrics. In the second phase, similar testing is done without applying SR for input test images. The experiment revealed that test images using the SR technique yielded higher average values of 5 percent and 1.1 percent for the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), respectively, as compared to images tested using the non-SR technique.

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Vegetation Detection Using Deep Learning and Conventional Methods

Cited by 65 publications

References 40 publications

Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops

Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops

Optical Filter Net: A Spectral-Aware RGB Camera Framework for Effective Green Pepper Segmentation

Effectiveness of Super-Resolution Technique on Vegetation Indices

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