Leaf Scanner: A portable and low-cost multispectral corn leaf scanning device for precise phenotyping

Zhang, Libo; Wang, Liangju; Wang, Jialei; Song, Zhihang; Rehman, Tanzeel U.; Bureetes, Thirawat; Ma, Di; Chen, Ziling; Neeno, Samantha; Jin, Jian Xun

doi:10.1016/j.compag.2019.105069

Cited by 18 publications

(10 citation statements)

References 33 publications

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“…However, a hybrid system can be envisioned and improvements should be further evaluated in field conditions such as the use of a spectralon (reference reflectance spectra) to correct for light sources (Sankaran et al 2012, Gold et al 2019) and background removal using CCNs. Another possible solution is to use a portable flatbed scanner, which enables lighting control and homogeneous background conditions (Zhang et al 2019).…”

Section: Discussionmentioning

confidence: 99%

RGB-based phenotyping of foliar disease severity under controlled conditions

Alves

Guimarães

Ascari

et al. 2021

Trop. plant pathol.

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Plant diseases induce visible modifications on leaves with the advance of infection and colonization, thus altering their spectral reflectance pattern. In this study, we evaluated the visible spectral region of symptomatic leaves of five plant diseases: soybean rust (SBR), calonectria leaf blight (CLB), wheat leaf blast (WLB), Nicotiana tabacum-Xylella fastidiosa (NtXf), and potato late blight (PLB). Ten spectral indices were calculated from the RGB channels (red, green, and blue) of images of leaves varying in percent severity, which were obtained under controlled lighting and homogeneous background. Image processing was automated for background removal and pixel-level index calculation. Each index was averaged across pixels at the leaf level. We found high levels of correlation between leaf severity and the majority of the spectral indices. The most highly associated spectral indices were HUE, VARI, NGRDI, HI, and SCI. The leaf-level mean value of each of the ten indices and digital numbers on the RGB channels were gathered and used to train boosted regression trees models for predicting the leaf severity of each disease. Models for SBR, CLB, and WLB achieved high prediction accuracies (>97%) on the testing dataset (20% of the original dataset). Models for NtXf and PLB had prediction accuracies below 90%. The performance of each model may be directly related to the symptomatology of each disease. The method can be automated if the images are obtained under controlled light and homogeneous background, but improvements should be made in the method for using fieldor greenhouse-acquired images which would require similar conditions.

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Section: Discussionmentioning

confidence: 99%

RGB-based phenotyping of foliar disease severity under controlled conditions

Alves

Guimarães

Ascari

et al. 2021

Trop. plant pathol.

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“…NDVI was selected as an example feature, since it is widely used to predict the nutrient status (water, nitrogen content, etc.) of plants, mainly from leaf mesurements [33,34,38]. A handheld hyperspectral scanner called LeafSpec was used to scan and collect the hyperspectral images of the top-collared leaves of 96 corn plants with different nitrogen treatments and genotypes.…”

Section: Discussionmentioning

confidence: 99%

“…To efficiently utilize the stress distribution information from the obtained NDVI images, we mainly focused on the mid-rib direction of the leaf (the horizontal direction (X) in Figure 3), which had the major variance in the stress distribution [20,38]. After averaging the NDVI values along each scanning line (the vertical direction (Y) in Figure 3), a further rescaling process was applied in order to rescale all the leaf images to the same length.…”

Section: Preprocessing Of Ndvi Imagesmentioning

confidence: 99%

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 NoIR camera provides more information on plant health status, where the healthy plants absorb more visible light and reflect more near-infrared in general [39]. This allows the extraction of vegetation indices, such as NDVI, in addition to others, allowing better scope to extract more crop traits [40]. Moreover, this plant property also allows other benefits such as easier segmentation of the plants of interest from a background (e.g., weeds, soil, etc.).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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Leaf Scanner: A portable and low-cost multispectral corn leaf scanning device for precise phenotyping

Cited by 18 publications

References 33 publications

RGB-based phenotyping of foliar disease severity under controlled conditions

RGB-based phenotyping of foliar disease severity under controlled conditions

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

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

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