Journal: HardwareX

Article Title: SPOT: Scanning plant IoT facility for high-throughput plant phenotyping

doi: 10.1016/j.ohx.2023.e00468

Figure Lengend Snippet: The hyperspectral imaging sensor constitutes the bulk of the cost of the system. Depending on the use case, other suitable sensors are available for a fraction of the cost.

Article Snippet: Hyperspectral camera , Headwall Nano-Hyperspec® hyperspectral imaging sensor , 1 , $25,750.00 , $25,750.00 , Headwall Photonics , Electronics.

Techniques: Imaging, Blocking Assay

Journal: HardwareX

Article Title: SPOT: Scanning plant IoT facility for high-throughput plant phenotyping

doi: 10.1016/j.ohx.2023.e00468

Figure Lengend Snippet: Radiometric calibration pipeline. In addition to establishing a white reference and dark reference with the SPOT experimental setup, many post-processing steps are needed to ensure that the information contained in the remaining hyperspectral data contains only phytochemically relevant data.

Article Snippet: Hyperspectral camera , Headwall Nano-Hyperspec® hyperspectral imaging sensor , 1 , $25,750.00 , $25,750.00 , Headwall Photonics , Electronics.

Techniques:

Journal: HardwareX

Article Title: SPOT: Scanning plant IoT facility for high-throughput plant phenotyping

doi: 10.1016/j.ohx.2023.e00468

Figure Lengend Snippet: Raw hyperspectral data collected from SPOT (left), after the radiance correction is applied (center), and after the reflectance correction is applied (right). Representative reflectance panel pixel (red) and plant pixel (blue) show appropriate scaling. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Article Snippet: Hyperspectral camera , Headwall Nano-Hyperspec® hyperspectral imaging sensor , 1 , $25,750.00 , $25,750.00 , Headwall Photonics , Electronics.

Techniques:

Journal: Frontiers in Plant Science

Article Title: Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline

doi: 10.3389/fpls.2017.01238

Figure Lengend Snippet: Hyperspectral imaging system (A) and schematic diagram (B) .

Article Snippet: Three leaves were selected from the main stem of each rice plant and scanned using the hyperspectral imaging system, which consisted of 4 major parts (Figure ): a halogen lamp, a translation stage, a hyperspectral camera (HyperspecTM VNIR, Headwall Photonics, USA), and a computer (OXPCO3, Dell, USA).

Techniques: Imaging

Journal: Frontiers in Plant Science

Article Title: Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline

doi: 10.3389/fpls.2017.01238

Figure Lengend Snippet: Main parameters of the hyperspectral imaging system.

Article Snippet: Three leaves were selected from the main stem of each rice plant and scanned using the hyperspectral imaging system, which consisted of 4 major parts (Figure ): a halogen lamp, a translation stage, a hyperspectral camera (HyperspecTM VNIR, Headwall Photonics, USA), and a computer (OXPCO3, Dell, USA).

Techniques: Imaging

Journal: Frontiers in Plant Science

Article Title: Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline

doi: 10.3389/fpls.2017.01238

Figure Lengend Snippet: Flow chart of data processing. (A) System diagram. (B) Binary data stream acquired by the hyperspectral imaging system. (C) Hyperspectral images reorganized from the binary data stream. (D) Reflectance of the rice leaf. (E) Divided results (float image) of the two hyperspectral images. (F) Conversion of the float image to 8-bit grayscale. (G) Image binarization. (H) Horizontal open operation. (I) Removal of the large area. (J) Removal of small noise. (K) Image masking. (L) Region growing. (M) Region extraction. (N) All extractive images. (O) Manual measurement of the pigments. (P) Modeling and validation.

Article Snippet: Three leaves were selected from the main stem of each rice plant and scanned using the hyperspectral imaging system, which consisted of 4 major parts (Figure ): a halogen lamp, a translation stage, a hyperspectral camera (HyperspecTM VNIR, Headwall Photonics, USA), and a computer (OXPCO3, Dell, USA).

Techniques: Imaging, Extraction, Biomarker Discovery

Journal: Frontiers in Plant Science

Article Title: Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline

doi: 10.3389/fpls.2017.01238

Figure Lengend Snippet: Flow chart of integrated hyperspectral image analysis pipeline.

Article Snippet: Three leaves were selected from the main stem of each rice plant and scanned using the hyperspectral imaging system, which consisted of 4 major parts (Figure ): a halogen lamp, a translation stage, a hyperspectral camera (HyperspecTM VNIR, Headwall Photonics, USA), and a computer (OXPCO3, Dell, USA).

Techniques:

Journal: Frontiers in Plant Science

Article Title: Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline

doi: 10.3389/fpls.2017.01238

Figure Lengend Snippet: Table 2 Definition and calculation formulas of 634,615 hyperspectral indices.

Article Snippet: Three leaves were selected from the main stem of each rice plant and scanned using the hyperspectral imaging system, which consisted of 4 major parts (Figure ): a halogen lamp, a translation stage, a hyperspectral camera (HyperspecTM VNIR, Headwall Photonics, USA), and a computer (OXPCO3, Dell, USA).

Techniques:

Journal: Frontiers in Plant Science

Article Title: Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline

doi: 10.3389/fpls.2017.01238

Figure Lengend Snippet: Hyperspectral indices that displayed the highest r values selected from all indices or primary indices for the 4 pigments and comparison with published indices.

Article Snippet: Three leaves were selected from the main stem of each rice plant and scanned using the hyperspectral imaging system, which consisted of 4 major parts (Figure ): a halogen lamp, a translation stage, a hyperspectral camera (HyperspecTM VNIR, Headwall Photonics, USA), and a computer (OXPCO3, Dell, USA).

Techniques: Comparison

Journal: Frontiers in Plant Science

Article Title: Accurate Digitization of the Chlorophyll Distribution of Individual Rice Leaves Using Hyperspectral Imaging and an Integrated Image Analysis Pipeline

doi: 10.3389/fpls.2017.01238

Figure Lengend Snippet: The correlation coefficient (r) between the SPAD value and the nitrogen content (A) , between the nitrogen content and hyperspectral measurements with 4 indices (B) , and between the nitrogen content and hyperspectral measurements with 1 index (C) .

Article Snippet: Three leaves were selected from the main stem of each rice plant and scanned using the hyperspectral imaging system, which consisted of 4 major parts (Figure ): a halogen lamp, a translation stage, a hyperspectral camera (HyperspecTM VNIR, Headwall Photonics, USA), and a computer (OXPCO3, Dell, USA).

Techniques:

Journal: PeerJ

Article Title: Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

doi: 10.7717/peerj.6926

Figure Lengend Snippet: (A) UAV. (B) Hyperspectral sensor (Photograph credit: Xiangyu Ge).

Article Snippet: The flight platform selected in this study was the DJI Matrice 600 Pro ® (Shenzhen Dajiang Innovation Technology Co., Ltd., China), which is a six-rotor UAV equipped with the Headwall Nano-Hyperspec ® hyperspectral sensor (Headwall Photonics Inc., Bolton, MA, USA) ( ).

Techniques:

Journal: PeerJ

Article Title: Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

doi: 10.7717/peerj.6926

Figure Lengend Snippet: (A) Hyperspectral image cube. (B) Image based on R. (C) Spectral curve based on R. (D) Image based on FDR. (E) Spectral curve based on FDR. (F) Image based on SDR. (G) Spectral curve based on SDR. (H) Image based on CR. (I) Spectral curve based on CR. (J) Image based on A. (K) Spectral curve based on A. (L) Image based on FDA. (M) Spectral curve based on FDA. (N) Image based on SDA. (O) Spectral curve based on SDA. The hyperspectral images and spectral curves based on different pretreatments (the red line represents the average spectrum and the gray region represents the standard deviation). The images are RGB images, where the red, green and blue bands are R 659 , R 550 , and R 479 , respectively.

Article Snippet: The flight platform selected in this study was the DJI Matrice 600 Pro ® (Shenzhen Dajiang Innovation Technology Co., Ltd., China), which is a six-rotor UAV equipped with the Headwall Nano-Hyperspec ® hyperspectral sensor (Headwall Photonics Inc., Bolton, MA, USA) ( ).

Techniques: Standard Deviation

Journal: bioRxiv

Article Title: Characterizing the effect of small-scale topographic variability on co-existing native and invasive species in a heterogeneous grassland using airborne hyperspectral remote sensing

doi: 10.1101/2021.04.11.439344

Figure Lengend Snippet: Sensor installation and flight mission. Upper left) the Bell 206B JetRanger aircraft, upper right) the micro HDPU, bottom left) the Micro-Hyperspec VNIR sensor, and bottom right a picture of the landscape from the aircraft.

Article Snippet: The Mirco-Hyperspec VNIR (A-Series) push-broom hyperspectral sensor ( ) from Headwall Photonics Inc, USA, the manned Bell 206B JetRanger helicopter, was used in this experiment.

Techniques: