Removal of greenhouse microclimate heterogeneity with conveyor system for indoor phenotyping

Ma, Di; Carpenter, Neal R.; Amatya, Suraj; Maki, Hideki; Wang, Liangju; Zhang, Libo; Neeno, Samantha; Tuinstra, Mitchell R.; Jin, Jian Xun

doi:10.1016/j.compag.2019.104979

Cited by 33 publications

(16 citation statements)

References 49 publications

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“…The image calibration was performed with the following equation Equation (1):

where

is the calibrated image,

is the raw hyperspectral leaf image,

is the dark reference image, and

is the hyperspectral image of the white reference. Then, the leaf tissue was segmented from the calibrated images using a segmentation procedure with a convolution methodology [ 12 ]. A vector of sequential integers from −20 to 20 was multiplied by the reflectance intensity vector from the red-edged region (680–720 nm).…”

Section: Methodsmentioning

confidence: 99%

“…Plant imaging models have been developed to predict plants’ physiological features, such as the plant’s biomass [ 1 ], field yield [ 2 ], chlorophyll content [ 3 ], nitrogen content [ 3 ], relative water content [ 4 , 5 ], and disease symptoms [ 6 , 7 ]. In conventional plant phenotyping analyses, most studies rely on the averaged spectrum across all the segmented plant pixels of the whole plant [ 2 , 4 , 8 , 9 , 10 , 11 , 12 ]. Researchers firstly segment the target plant tissue from the image.…”

Section: Introductionmentioning

confidence: 99%

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Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality

Wang

Zhang

et al. 2020

Sensors

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High-throughput imaging technologies have been developing rapidly for agricultural plant phenotyping purposes. With most of the current crop plant image processing algorithms, the plant canopy pixels are segmented from the images, and the averaged spectrum across the whole canopy is calculated in order to predict the plant’s physiological features. However, the nutrients and stress levels vary significantly across the canopy. For example, it is common to have several times of difference among Soil Plant Analysis Development (SPAD) chlorophyll meter readings of chlorophyll content at different positions on the same leaf. The current plant image processing algorithms cannot provide satisfactory plant measurement quality, as the averaged color cannot characterize the different leaf parts. Meanwhile, the nutrients and stress distribution patterns contain unique features which might provide valuable signals for phenotyping. There is great potential to develop a finer level of image processing algorithm which analyzes the nutrients and stress distributions across the leaf for improved quality of phenotyping measurements. In this paper, a new leaf image processing algorithm based on Random Forest and leaf region rescaling was developed in order to analyze the distribution patterns on the corn leaf. The normalized difference vegetation index (NDVI) was used as an example to demonstrate the improvements of the new algorithm in differentiating between different nitrogen stress levels. With the Random Forest method integrated into the algorithm, the distribution patterns along the corn leaf’s mid-rib direction were successfully modeled and utilized for improved phenotyping quality. The algorithm was tested in a field corn plant phenotyping assay with different genotypes and nitrogen treatments. Compared with the traditional image processing algorithms which average the NDVI (for example) throughout the whole leaf, the new algorithm more clearly differentiates the leaves from different nitrogen treatments and genotypes. We expect that, besides NDVI, the new distribution analysis algorithm could improve the quality of other plant feature measurements in similar ways.

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“…The image calibration was performed with the following equation Equation (1):

where

is the calibrated image,

is the raw hyperspectral leaf image,

is the dark reference image, and

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality

Wang

Zhang

et al. 2020

Sensors

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“…But since most conveyor HT3Ps often need to transport plants to the specific imaging room, this introduced microclimatic heterogeneity likely influences the plants' growth and response to environmental changes, rendering the phenotypic data collected inaccurate. Fortunately, the HT3P built by Purdue University overcomes this interference of a differential microclimate (Ma et al, 2019 ), in that plants are grown on cyclic conveyor belts throughout their whole growth cycle, thus exposing them to the same heat and radiation conditions. Huazhong University of Science and Technology and Huazhong Agricultural University (Wuhan, China) jointly developed a high-throughput rice phenotyping facility (HRPF) with an image analysis pipeline, able to perform color imaging and X-ray computed tomography (CT); it can monitor 15 agronomic traits of 1,920 rice plants (Yang et al, 2014 ).…”

Section: Ht3p For Indoor Phenotyping Under Strictly Controlled Enviromentioning

confidence: 99%

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

Quan

Song

et al. 2021

Front. Bioeng. Biotechnol.

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Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.

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“…To minimize the effects of the microclimate in the greenhouse, all pots (one corn plant per pot) were placed on a closed-loop conveyor system. The system was programmed to run for only 30 s with every 30 min break phase to avoid shaking disturbance to plant growth [29]. Two different water regimes (well-watered and drought-stressed) and two nitrogen levels (low and high) were applied to the plants.…”

Section: Plant Samplesmentioning

confidence: 99%

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

Wang

Duan

Zhang

et al. 2020

Sensors

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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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Removal of greenhouse microclimate heterogeneity with conveyor system for indoor phenotyping

Cited by 33 publications

References 49 publications

Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality

Stress Distribution Analysis on Hyperspectral Corn Leaf Images for Improved Phenotyping Quality

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

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

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