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 Low-Cost Three-Dimensional Modeling of Crop Plants

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2883 ◽  
Author(s):  
Jorge Martinez-Guanter ◽  
Ángela Ribeiro ◽  
Gerassimos G. Peteinatos ◽  
Manuel Pérez-Ruiz ◽  
Roland Gerhards ◽  
...  
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Ground Truth ◽  
Reconstruction Algorithm ◽  
3D Models ◽  
Crop Plants ◽  
Plant Phenotyping ◽  
Area Index ◽  
Ground Truth Data ◽  
The Creation

Plant modeling can provide a more detailed overview regarding the basis of plant development throughout the life cycle. Three-dimensional processing algorithms are rapidly expanding in plant phenotyping programmes and in decision-making for agronomic management. Several methods have already been tested, but for practical implementations the trade-off between equipment cost, computational resources needed and the fidelity and accuracy in the reconstruction of the end-details needs to be assessed and quantified. This study examined the suitability of two low-cost systems for plant reconstruction. A low-cost Structure from Motion (SfM) technique was used to create 3D models for plant crop reconstruction. In the second method, an acquisition and reconstruction algorithm using an RGB-Depth Kinect v2 sensor was tested following a similar image acquisition procedure. The information was processed to create a dense point cloud, which allowed the creation of a 3D-polygon mesh representing every scanned plant. The selected crop plants corresponded to three different crops (maize, sugar beet and sunflower) that have structural and biological differences. The parameters measured from the model were validated with ground truth data of plant height, leaf area index and plant dry biomass using regression methods. The results showed strong consistency with good correlations between the calculated values in the models and the ground truth information. Although, the values obtained were always accurately estimated, differences between the methods and among the crops were found. The SfM method showed a slightly better result with regard to the reconstruction the end-details and the accuracy of the height estimation. Although the use of the processing algorithm is relatively fast, the use of RGB-D information is faster during the creation of the 3D models. Thus, both methods demonstrated robust results and provided great potential for use in both for indoor and outdoor scenarios. Consequently, these low-cost systems for 3D modeling are suitable for several situations where there is a need for model generation and also provide a favourable time-cost relationship.

scholarly journals Affordable Imaging Lab for Noninvasive Analysis of Biomass and Early Vigour in Cereal Crops

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Rita Armoniené ◽  
Firuz Odilbekov ◽  
Vivekanand Vivekanand ◽  
Aakash Chawade
Keyword(s):  
Imaging System ◽  
Low Cost ◽  
Dry Weight ◽  
Automated Analysis ◽  
Coefficient Of Determination ◽  
Plant Phenotyping ◽  
Cereal Crops ◽  
Small Scale ◽  
Noninvasive Analysis ◽  
Early Vigour

Plant phenotyping by imaging allows automated analysis of plants for various morphological and physiological traits. In this work, we developed a low-cost RGB imaging phenotyping lab (LCP lab) for low-throughput imaging and analysis using affordable imaging equipment and freely available software. LCP lab comprising RGB imaging and analysis pipeline is set up and demonstrated with early vigour analysis in wheat. Using this lab, a few hundred pots can be photographed in a day and the pots are tracked with QR codes. The software pipeline for both imaging and analysis is built from freely available software. The LCP lab was evaluated for early vigour analysis of five wheat cultivars. A high coefficient of determination (R2 0.94) was obtained between the dry weight and the projected leaf area of 20-day-old wheat plants and R2 of 0.9 for the relative growth rate between 10 and 20 days of plant growth. Detailed description for setting up such a lab is provided together with custom scripts built for imaging and analysis. The LCP lab is an affordable alternative for analysis of cereal crops when access to a high-throughput phenotyping facility is unavailable or when the experiments require growing plants in highly controlled climate chambers. The protocols described in this work are useful for building affordable imaging system for small-scale research projects and for education.

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.

scholarly journals PREDICTING THE ACCURACY OF PHOTOGRAMMETRIC 3D RECONSTRUCTION FROM CAMERA CALIBRATION PARAMETERS THROUGH A MULTIVARIATE STATISTICAL APPROACH

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 479-486
Author(s):  
M. Saponaro ◽  
A. Capolupo ◽  
G. Caporusso ◽  
E. Borgogno Mondino ◽  
E. Tarantino
Keyword(s):  
Camera Calibration ◽  
Low Cost ◽  
3D Models ◽  
Calibration Procedure ◽  
Coefficient Of Determination ◽  
Strong Impact ◽  
Multivariate Statistical ◽  
Error Components ◽  
Synthetic Index ◽  
Calibration Parameters

Abstract. Several tools have been introduced to generate accurate 3D models. Among these, Unmanned Aerial Vehicles (UAVs) are an effective low-cost tool to go beyond on-fields effort limits since they allow to fly over areas difficult to reach and to reduce the time needed to collect and process photogrammetric pictures as well. Combining their versatility with Structure from Motion (SfM) techniques efficiency has provided a widely accessible approach to generate accurate photogrammetric products. However, the outcome resolution and coherences also depend on sensor traits. Therefore, UAVs are usually equipped with low-cost non-metric cameras, with the consequent requirement for a calibration procedure to increase the final 3D models accuracy. Although several researchers have highlighted the strong impact of camera calibration parameters on the photogrammetric outcomes, their linkage has not been explored yet. This paper is aimed at investigating their relationship and to propose a novel predicting function of 3D photogrammetric reconstruction accuracy. Such function was estimated thanks to the application of the Principal Components Analysis (PCA) technique. Four photogrammetric UAV flight surveys provided the input data of PCA while an extra dataset was used to validate the results. Once PCA was completed, a synthetic index was proposed and the coefficient of determination was calculated between the index and error components. Synthetic indices values for the various datasets were applied as baseline to detect a predictive function able to assess the northern and eastern error components with a deviation of 0.005 m and 0.003 m, respectively. The proposed approach shows promising and satisfying results for predicting 3D models accuracy.

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 MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Sheng Wu ◽  
Weiliang Wen ◽  
Yongjian Wang ◽  
Jiangchuan Fan ◽  
Chuanyu Wang ◽  
...  
Keyword(s):  
Data Management ◽  
High Throughput ◽  
Management System ◽  
Low Cost ◽  
Point Clouds ◽  
Data Management System ◽  
Plant Phenotyping ◽  
Phenotypic Traits ◽  
High Throughput Phenotyping ◽  
Multiview Stereo

Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform’s device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R2) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.

scholarly journals Evaluation of indigenous aromatic rice cultivars from sub-Himalayan Terai region of India for nutritional attributes and blast resistance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Debayan Mondal ◽  
Prudveesh Kantamraju ◽  
Susmita Jha ◽  
Gadge Sushant Sundarrao ◽  
Arpan Bhowmik ◽  
...  
Keyword(s):  
Nutritive Value ◽  
Amylose Content ◽  
Blast Resistance ◽  
Starch Content ◽  
Low Cost ◽  
Principal Component ◽  
Blast Disease ◽  
Basmati Rice ◽  
Phenotypic Traits ◽  
Rice Cultivars

AbstractIndigenous folk rice cultivars often possess remarkable but unrevealed potential in terms of nutritional attributes and biotic stress tolerance. The unique cooking qualities and blissful aroma of many of these landraces make it an attractive low-cost alternative to high priced Basmati rice. Sub-Himalayan Terai region is bestowed with great agrobiodiversity in traditional heirloom rice cultivars. In the present study, ninety-nine folk rice cultivars from these regions were collected, purified and characterized for morphological and yield traits. Based on traditional importance and presence of aroma, thirty-five genotypes were selected and analyzed for genetic diversity using micro-satellite marker system. The genotypes were found to be genetically distinct and of high nutritive value. The resistant starch content, amylose content, glycemic index and antioxidant potential of these genotypes represented wide variability and ‘Kataribhog’, ‘Sadanunia’, ‘Chakhao’ etc. were identified as promising genotypes in terms of different nutritional attributes. These cultivars were screened further for resistance against blast disease in field trials and cultivars like ‘Sadanunia’, ‘T4M-3-5’, ‘Chakhao Sampark’ were found to be highly resistant to the blast disease whereas ‘Kalonunia’, ‘Gobindabhog’, ‘Konkanijoha’ were found to be highly susceptible. Principal Component analysis divided the genotypes in distinct groups for nutritional potential and blast tolerance. The resistant and susceptible genotypes were screened for the presence of the blast resistant pi genes and association analysis was performed with disease tolerance. Finally, a logistic model based on phenotypic traits for prediction of the blast susceptibility of the genotypes is proposed with more than 80% accuracy.

scholarly journals The grades and freshness assessment of eggs based on density detection using machine vision and weighing sensor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Supakorn Harnsoongnoen ◽  
Nuananong Jaroensuk
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Low Cost ◽  
Measuring System ◽  
Coefficient Of Determination ◽  
Water Displacement ◽  
Non Invasive ◽  
Future Potential ◽  
Non Destructive ◽  
Egg Freshness

AbstractThe water displacement and flotation are two of the most accurate and rapid methods for grading and assessing freshness of agricultural products based on density determination. However, these techniques are still not suitable for use in agricultural inspections of products such as eggs that absorb water which can be considered intrusive or destructive and can affect the result of measurements. Here we present a novel proposal for a method of non-destructive, non-invasive, low cost, simple and real—time monitoring of the grading and freshness assessment of eggs based on density detection using machine vision and a weighing sensor. This is the first proposal that divides egg freshness into intervals through density measurements. The machine vision system was developed for the measurement of external physical characteristics (length and breadth) of eggs for evaluating their volume. The weighing system was developed for the measurement of the weight of the egg. Egg weight and volume were used to calculate density for grading and egg freshness assessment. The proposed system could measure the weight, volume and density with an accuracy of 99.88%, 98.26% and 99.02%, respectively. The results showed that the weight and freshness of eggs stored at room temperature decreased with storage time. The relationship between density and percentage of freshness was linear for the all sizes of eggs, the coefficient of determination (R2) of 0.9982, 0.9999, 0.9996, 0.9996 and 0.9994 for classified egg size classified 0, 1, 2, 3 and 4, respectively. This study shows that egg freshness can be determined through density without using water to test for water displacement or egg flotation which has future potential as a measuring system important for the poultry industry.

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