scholarly journals PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping

2021 ◽  
Vol 12 ◽  
Author(s):  
Lingbo Liu ◽  
Lejun Yu ◽  
Dan Wu ◽  
Junli Ye ◽  
Hui Feng ◽  
...  
Keyword(s):  
Plant Traits ◽  
Low Cost ◽  
Leaf Traits ◽  
Smartphone Application ◽  
Plant Phenotyping ◽  
Phenotypic Characterization ◽  
Whole Plant ◽  
Outdoor Environments ◽  
Calibration Algorithm ◽  
Trait Measurement

A low-cost portable wild phenotyping system is useful for breeders to obtain detailed phenotypic characterization to identify promising wild species. However, compared with the larger, faster, and more advanced in-laboratory phenotyping systems developed in recent years, the progress for smaller phenotyping systems, which provide fast deployment and potential for wide usage in rural and wild areas, is quite limited. In this study, we developed a portable whole-plant on-device phenotyping smartphone application running on Android that can measure up to 45 traits, including 15 plant traits, 25 leaf traits and 5 stem traits, based on images. To avoid the influence of outdoor environments, we trained a DeepLabV3+ model for segmentation. In addition, an angle calibration algorithm was also designed to reduce the error introduced by the different imaging angles. The average execution time for the analysis of a 20-million-pixel image is within 2,500 ms. The application is a portable on-device fast phenotyping platform providing methods for real-time trait measurement, which will facilitate maize phenotyping in field and benefit crop breeding in future.

How well do seed production traits correlate with leaf traits, whole-plant traits and plant ecological strategies?

Plant Ecology ◽  
2014 ◽  
Vol 215 (11) ◽  
pp. 1351-1359 ◽  
Author(s):  
Simon Pierce ◽  
Arianna Bottinelli ◽  
Ilaria Bassani ◽  
Roberta M. Ceriani ◽  
Bruno E. L. Cerabolini
Keyword(s):  
Seed Production ◽  
Plant Traits ◽  
Leaf Traits ◽  
Production Traits ◽  
Ecological Strategies ◽  
Whole Plant ◽  
Plant Ecological

scholarly journals Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3150
Author(s):  
Riccardo Rossi ◽  
Claudio Leolini ◽  
Sergi Costafreda-Aumedes ◽  
Luisa Leolini ◽  
Marco Bindi ◽  
...  
Keyword(s):  
Processing Time ◽  
Goodness Of Fit ◽  
Plant Traits ◽  
Low Cost ◽  
3D Models ◽  
Herbaceous Plants ◽  
Coefficient Of Determination ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
Olive Trees

This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.

scholarly journals A Nondestructive Method to Estimate Plant Height, Stem Diameter and Biomass of Rice under Field Conditions Using Digital Image Analysis

2017 ◽  
Vol 10 (1) ◽  
pp. 1-7 ◽  
Author(s):  
M Mano ◽  
M Igawa
Keyword(s):  
Plant Height ◽  
Plant Traits ◽  
Agricultural Research ◽  
Low Cost ◽  
Ground Surface ◽  
Time Lapse ◽  
Nondestructive Method ◽  
Stem Diameter ◽  
Plant Phenotyping ◽  
Continuous Data

Plant phenotyping intends measuring complex plant traits, and is important in agricultural research for enhancing yield improvement. Manual plant phenotyping is laborious and destructive, and hence a less-laborious and non-destructive method is required. Here, we proposed a nondestructive method to estimate continuous data of plant traits such as height, stem diameter and biomass using a low cost time-lapse camera. The camera was installed at a rice field in Japan, and captured images for four target plants every three hour. The plant height and stem diameter were determined from the images by referencing scale bars that were placed next to the target plants and above the ground surface. Both the height and the diameter were compared to directly measured ones, and the relationships between those were in good agreement. Plant volumes were estimated from the height and stem diameter assuming a shape of rice plant is cylindrical. Above ground biomass without panicles was determined by rice plants sampled from the field. The determined biomass increased in proportion to the plant volume, and its relationship used to produce continuous data of the rice biomass. The results suggest that the proposed method can be considered as a useful tool of the plant phenotyping.J. Environ. Sci. & Natural Resources, 10(1): 1-7 2017

scholarly journals Traits controlling shade tolerance in tropical montane trees

2019 ◽  
Vol 40 (2) ◽  
pp. 183-197 ◽  
Author(s):  
Elisée Bahati Ntawuhiganayo ◽  
Félicien K Uwizeye ◽  
Etienne Zibera ◽  
Mirindi E Dusenge ◽  
Camille Ziegler ◽  
...  
Keyword(s):  
Tree Species ◽  
Shade Tolerance ◽  
Plant Traits ◽  
Plant Biomass ◽  
Leaf Traits ◽  
Interspecific Variation ◽  
Clear Understanding ◽  
Tolerant Species ◽  
Whole Plant ◽  
Shade Tolerant Species

Abstract Tropical canopies are complex, with multiple canopy layers and pronounced gap dynamics contributing to their high species diversity and productivity. An important reason for this complexity is the large variation in shade tolerance among different tree species. At present, we lack a clear understanding of which plant traits control this variation, e.g., regarding the relative contributions of whole-plant versus leaf traits or structural versus physiological traits. We investigated a broad range of traits in six tropical montane rainforest tree species with different degrees of shade tolerance, grown under three different radiation regimes (under the open sky or beneath sparse or dense canopies). The two distinct shade-tolerant species had higher fractional biomass in leaves and branches while shade-intolerant species invested more into stems, and these differences were greater under low radiation. Leaf respiration and photosynthetic light compensation point did not vary with species shade tolerance, regardless of radiation regime. Leaf temperatures in open plots were markedly higher in shade-tolerant species due to their low transpiration rates and large leaf sizes. Our results suggest that interspecific variation in shade tolerance of tropical montane trees is controlled by species differences in whole-plant biomass allocation strategy rather than by difference in physiological leaf traits determining leaf carbon balance at low radiation.

scholarly journals Studies of NH4+ and NO3- uptake ability of subalpine plants and resource-use strategy identified by their functional traits

2018 ◽  
Author(s):  
Legay Nicolas ◽  
Grassein Fabrice ◽  
Arnoldi Cindy ◽  
Segura Raphaël ◽  
Laîné Philippe ◽  
...  
Keyword(s):  
Plant Traits ◽  
N Uptake ◽  
Perennial Grass ◽  
Root Traits ◽  
Leaf Traits ◽  
Grass Species ◽  
Leaf Economics Spectrum ◽  
Peak Biomass ◽  
Whole Plant ◽  
Environmental Variations

AbstractThe leaf economics spectrum (LES) is based on a suite of leaf traits related to plant functioning and ranges from resource-conservative to resource-acquisitive strategies. However, the relationships with root traits, and the associated belowground plant functioning such as N uptake, including nitrate (NO3-) and ammonium (NH4+), is still poorly known. Additionally, environmental variations occurring both in time and in space could uncouple LES from root traits. We explored, in subalpine grasslands, the relationships between leaf and root morphological traits for 3 dominant perennial grass species, and to what extent they contribute to the whole-plant economics spectrum. We also investigated the link between this spectrum and NO3- and NH4+ uptake rates, as well as the variations of uptake across four grasslands differing by the land-use history at peak biomass and in autumn. Although poorly correlated with leaf traits, root traits contributed to an economic spectrum at the whole plant level. Higher NH4+ and NO3- uptake abilities were associated with the resource-acquisitive strategy.Nonetheless, NH4+ and NO3- uptake within species varied between land-uses and with sampling time, suggesting that LES and plant traits are good, but still incomplete, descriptors of plant functioning. Although the NH4+: NO3- uptake ratio was different between plant species in our study, they all showed a preference for NH4+, and particularly the most conservative species. Soil environmental variations between grasslands and sampling times may also drive to some extent the NH4+ and NO3- uptake ability of species. Our results support the current efforts to build a more general framework including above- and below-ground processes when studying plant community functioning.

scholarly journals A direct comparison of ecological theories for predicting the relationship between plant traits and growth

2021 ◽  
Author(s):  
Ellie Goud ◽  
Anurag Agrawal ◽  
Jed Sparks
Keyword(s):  
Leaf Area ◽  
Plant Height ◽  
Growth Analysis ◽  
Plant Traits ◽  
Phylogenetic Analyses ◽  
Leaf Traits ◽  
Total Leaf Area ◽  
Leaf Economics Spectrum ◽  
Total Leaf ◽  
Whole Plant

Abstract Despite long-standing theory for classifying plant ecological strategies, limited data directly links organismal traits to whole-plant growth. We compared trait-growth relationships based on three prominent theories: growth analysis, Grime’s CSR triangle, and the leaf economics spectrum (LES). Under these schemes, growth is hypothesized to be predicted by traits related to biomass investments, leaf structure or gas exchange, respectively. In phylogenetic analyses of 30 diverse milkweeds (Asclepias spp.) and 21 morphological and ecophysiological traits, growth rate varied 50-fold and was best predicted by growth analysis and CSR traits, as well as total leaf area and plant height. Despite two LES traits correlating with growth, they contradicted predictions and leaf traits did not scale with root and stem characteristics. Thus, although combining leaf traits and whole-plant allocation best predicts growth, when destructive measures are not feasible, we suggest total leaf area and plant height, or easy-to-measure traits associated with the CSR classification.

scholarly journals CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements

Biosensors ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 16
Author(s):  
Bikram Pratap Banerjee ◽  
German Spangenberg ◽  
Surya Kant
Keyword(s):  
High Throughput ◽  
Agricultural Research ◽  
Low Cost ◽  
Plant Phenotyping ◽  
Phenotypic Characterization ◽  
Raspberry Pi ◽  
Phenotypic Traits ◽  
Small Data ◽  
Crop Species ◽  
Wheat Field

The phenotypic characterization of crop genotypes is an essential, yet challenging, aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agricultural research due to the diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. The phenotypic traits of crop fresh biomass, dry biomass, and plant height that were estimated by CBM data had high correlation with ground truth manual measurements in a wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

scholarly journals CBM: An IoT Enabled LiDAR Sensor for In-field Crop Height and Biomass Measurements

2021 ◽  
Author(s):  
Bikram Pratap Banerjee ◽  
German Spangenberg ◽  
Surya Kant
Keyword(s):  
High Throughput ◽  
Low Cost ◽  
Ground Truth ◽  
Plant Phenotyping ◽  
Phenotypic Characterization ◽  
Raspberry Pi ◽  
Phenotypic Traits ◽  
Small Data ◽  
Crop Species ◽  
Wheat Field

Phenotypic characterization of crop genotypes is an essential yet challenging aspect of crop management and agriculture research. Digital sensing technologies are rapidly advancing plant phenotyping and speeding-up crop breeding outcomes. However, off-the-shelf sensors might not be fully applicable and suitable for agriculture research due to diversity in crop species and specific needs during plant breeding selections. Customized sensing systems with specialized sensor hardware and software architecture provide a powerful and low-cost solution. This study designed and developed a fully integrated Raspberry Pi-based LiDAR sensor named CropBioMass (CBM), enabled by internet of things to provide a complete end-to-end pipeline. The CBM is a low-cost sensor, provides high-throughput seamless data collection in field, small data footprint, injection of data onto the remote server, and automated data processing. Phenotypic traits of crop fresh biomass, dry biomass, and plant height estimated by CBM data had high correlation with ground truth manual measurements in wheat field trial. The CBM is readily applicable for high-throughput plant phenotyping, crop monitoring, and management for precision agricultural applications.

scholarly journals Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro)

2016 ◽  
Vol 9 (11) ◽  
pp. 4227-4255 ◽  
Author(s):  
Bradley O. Christoffersen ◽  
Manuel Gloor ◽  
Sophie Fauset ◽  
Nikolaos M. Fyllas ◽  
David R. Galbraith ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Water Potential ◽  
Plant Traits ◽  
Coupled Model ◽  
Leaf Traits ◽  
Tree Size ◽  
Leaf Mass Per Area ◽  
Stem Water Potential ◽  
Plant Hydraulics ◽  
Individual Trees

Abstract. Forest ecosystem models based on heuristic water stress functions poorly predict tropical forest response to drought partly because they do not capture the diversity of hydraulic traits (including variation in tree size) observed in tropical forests. We developed a continuous porous media approach to modeling plant hydraulics in which all parameters of the constitutive equations are biologically interpretable and measurable plant hydraulic traits (e.g., turgor loss point πtlp, bulk elastic modulus ε, hydraulic capacitance Cft, xylem hydraulic conductivity ks,max, water potential at 50 % loss of conductivity for both xylem (P50,x) and stomata (P50,gs), and the leaf : sapwood area ratio Al : As). We embedded this plant hydraulics model within a trait forest simulator (TFS) that models light environments of individual trees and their upper boundary conditions (transpiration), as well as providing a means for parameterizing variation in hydraulic traits among individuals. We synthesized literature and existing databases to parameterize all hydraulic traits as a function of stem and leaf traits, including wood density (WD), leaf mass per area (LMA), and photosynthetic capacity (Amax), and evaluated the coupled model (called TFS v.1-Hydro) predictions, against observed diurnal and seasonal variability in stem and leaf water potential as well as stand-scaled sap flux. Our hydraulic trait synthesis revealed coordination among leaf and xylem hydraulic traits and statistically significant relationships of most hydraulic traits with more easily measured plant traits. Using the most informative empirical trait–trait relationships derived from this synthesis, TFS v.1-Hydro successfully captured individual variation in leaf and stem water potential due to increasing tree size and light environment, with model representation of hydraulic architecture and plant traits exerting primary and secondary controls, respectively, on the fidelity of model predictions. The plant hydraulics model made substantial improvements to simulations of total ecosystem transpiration. Remaining uncertainties and limitations of the trait paradigm for plant hydraulics modeling are highlighted.

scholarly journals Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6659
Author(s):  
Aryuanto Soetedjo ◽  
Evy Hendriarianti
Keyword(s):  
Execution Time ◽  
Low Cost ◽  
Infrared Camera ◽  
Raspberry Pi ◽  
Camera System ◽  
Detection And Counting ◽  
Outdoor Environments ◽  
Static Images ◽  
Leaf Detection ◽  
Non Destructive

A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground–background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.

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