Measurement of Environmentally Influenced Variations in Anthocyanin Accumulations in Brassica rapa subsp. Chinensis (Bok Choy) Using Hyperspectral Imaging

Dietary supplements of anthocyanin-rich vegetables have been known to increase potential health benefits for humans. The optimization of environmental conditions to increase the level of anthocyanin accumulations in vegetables during the cultivation periods is particularly important in terms of the improvement of agricultural values in the indoor farm using artificial light and climate controlling systems. This study reports on the measurement of variations in anthocyanin accumulations in leaf tissues of four different cultivars in Brassica rapa var. chinensis (bok choy) grown under the different environmental conditions of the indoor farm using hyperspectral imaging. Anthocyanin accumulations estimated by hyperspectral imaging were compared with the measured anthocyanin accumulation obtained by destructive analysis. Between hyperspectral imaging and destructive analysis values, no significant differences in anthocyanin accumulation were observed across four bok choy cultivars grown under the anthocyanin stimulation environmental condition, whereas the estimated anthocyanin accumulations displayed cultivar-dependent significant differences, suggesting that hyperspectral imaging can be employed to measure variations in anthocyanin accumulations of different bok choy cultivars. Increased accumulation of anthocyanin under the stimulation condition for anthocyanin accumulation was observed in “purple magic” and “red stem” by both hyperspectral imaging and destructive analysis. In the different growth stages, no significant differences in anthocyanin accumulation were found in each cultivar by both hyperspectral imaging and destructive analysis. These results suggest that hyperspectral imaging can provide comparable analytic capability with destructive analysis to measure variations in anthocyanin accumulation that occurred under the different light and temperature conditions of the indoor farm. Leaf image analysis measuring the percentage of purple color area in the total leaf area displayed successful classification of anthocyanin accumulation in four bok choy cultivars in comparison to hyperspectral imaging and destructive analysis, but it also showed limitation to reflect the level of color saturation caused by anthocyanin accumulation under different environmental conditions in “red stem,” “white stem,” and “green stem.” Finally, our hyperspectral imaging system was modified to be applied onto the high-throughput plant phenotyping system, and its test to analyze the variation of anthocyanin accumulation in four cultivars showed comparable results with the result of the destructive analysis.

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Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Inventions ◽

10.3390/inventions6020042 ◽

2021 ◽

Vol 6 (2) ◽

pp. 42

Author(s):

Worasit Sangjan ◽

Arron H. Carter ◽

Michael O. Pumphrey ◽

Vadim Jitkov ◽

Sindhuja Sankaran

Keyword(s):

Real Time ◽

Imaging System ◽

Crop Growth ◽

Sensor System ◽

Plant Phenotyping ◽

Raspberry Pi ◽

The Internet ◽

Growth Stages ◽

Crop Breeding ◽

Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

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A New Compact VNIR Hyperspectral Imaging System for Non-Invasive Analysis in the FineArt and Architecture Fields

Proceedings e report - Electronic Imaging & the Visual Arts. EVA 2018 Florence ◽

10.36253/978-88-6453-707-8.16 ◽

2018 ◽

pp. 69-74

Author(s):

Marcello Picollo ◽

Andrea Casini ◽

Costanza Cucci ◽

Jouni Jussila ◽

Marco Poggesi ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Environmental Conditions ◽

Imaging System ◽

Non Invasive ◽

Nm Range ◽

Hyperspectral Camera

A new compact Specim IQ hyperspectral camera working in the 400-1000 nm range has been launched on the market. Its use in the investigation of different artworks and under diverse environmental conditions will be presented.

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Modeling of Environmental Impacts on Aerial Hyperspectral Images for Corn Plant Phenotyping

Remote Sensing ◽

10.3390/rs13132520 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2520

Author(s):

Dongdong Ma ◽

Tanzeel U. Rehman ◽

Libo Zhang ◽

Hideki Maki ◽

Mitchell R. Tuinstra ◽

...

Keyword(s):

Remote Sensing ◽

Hyperspectral Imaging ◽

Environmental Conditions ◽

Modeling Method ◽

Hyperspectral Images ◽

Solar Irradiation ◽

Plant Phenotyping ◽

Sensing Applications ◽

Aerial Remote Sensing ◽

Data Variation

Aerial imaging technologies have been widely applied in agricultural plant remote sensing. However, an as yet unexplored challenge with field imaging is that the environmental conditions, such as sun angle, cloud coverage, temperature, and so on, can significantly alter plant appearance and thus affect the imaging sensor’s accuracy toward extracting plant feature measurements. These image alterations result from the complicated interaction between the real-time environments and plants. Analysis of these impacts requires continuous monitoring of the changes through various environmental conditions, which has been difficult with current aerial remote sensing systems. This paper aimed to propose a modeling method to comprehensively understand and model the environmental influences on hyperspectral imaging data. In 2019, a fixed hyperspectral imaging gantry was constructed in Purdue University’s research farm, and over 8000 repetitive images of the same corn field were taken with a 2.5 min interval for 31 days. Time-tagged local environment data, including solar zenith angle, solar irradiation, temperature, wind speed, and so on, were also recorded during the imaging time. The images were processed for phenotyping data, and the time series decomposition method was applied to extract the phenotyping data variation caused by the changing environments. An artificial neural network (ANN) was then built to model the relationship between the phenotyping data variation and environmental changes. The ANN model was able to accurately predict the environmental effects in remote sensing results, and thus could be used to effectively eliminate the environment-induced variation in the phenotyping features. The test of the normalized difference vegetation index (NDVI) calculated from the hyperspectral images showed that variance in NDVI was reduced by 79%. A similar performance was confirmed with the relative water content (RWC) predictions. Therefore, this modeling method shows great potential for application in aerial remote sensing applications in agriculture, to significantly improve the imaging quality by effectively eliminating the effects from the changing environmental conditions.

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The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat

Sensors ◽

10.3390/s20164550 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4550

Author(s):

Huajian Liu ◽

Brooke Bruning ◽

Trevor Garnett ◽

Bettina Berger

Keyword(s):

Hyperspectral Imaging ◽

High Throughput ◽

Management Practices ◽

Leaf Tissue ◽

Plant Phenotyping ◽

Proximal Sensing ◽

N Content ◽

Hyperspectral Sensors ◽

Wheat Leaves ◽

Non Destructive

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

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Mapping of fatty acids composition in shelled almonds analysed in bulk using a Hyperspectral Imaging system

LWT ◽

10.1016/j.lwt.2020.110678 ◽

2021 ◽

Vol 138 ◽

pp. 110678

Author(s):

Irina Torres ◽

Dolores Pérez-Marín ◽

Miguel Vega-Castellote ◽

María-Teresa Sánchez

Keyword(s):

Fatty Acids ◽

Hyperspectral Imaging ◽

Imaging System ◽

Fatty Acids Composition

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Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Sensors ◽

10.3390/s20113208 ◽

2020 ◽

Vol 20 (11) ◽

pp. 3208 ◽

Cited By ~ 1

Author(s):

Liangju Wang ◽

Yunhong Duan ◽

Libo Zhang ◽

Tanzeel U. Rehman ◽

Dongdong Ma ◽

...

Keyword(s):

Machine Learning ◽

Professional Training ◽

Imaging System ◽

Estimation Method ◽

Machine Learning Algorithms ◽

Plant Phenotyping ◽

Band Pass Filter ◽

Pass Filter ◽

Corn Plants ◽

Hyperspectral Camera

The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.

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