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 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 Data-Driven Approaches Can Overcome Limitations in Multireference Diagnostics

2020 ◽  
Author(s):  
Chenru Duan ◽  
Fang Liu ◽  
Aditya Nandy ◽  
Heather Kulik
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Density Functional ◽  
Correlation Energy ◽  
Low Cost ◽  
Small Data ◽  
Data Sets ◽  
Strongly Correlated ◽  
Heavy Atoms ◽  
Computational Screening

High-throughput computational screening typically employs methods (i.e., density functional theory or DFT) that can fail to describe challenging molecules, such as those with strongly correlated electronic structure. In such cases, multireference (MR) correlated wavefunction theory (WFT) would be the appropriate choice but remains more challenging to carry out and automate than single-reference (SR) WFT or DFT. Numerous diagnostics have been proposed for identifying when MR character is likely to have an effect on the predictive power of SR calculations, but conflicting conclusions about diagnostic performance have been reached on small data sets. We compute 15 MR diagnostics, ranging from affordable DFT-based to more costly MR-WFT-based diagnostics, on a set of 3,165 equilibrium and distorted small organic molecules containing up to six heavy atoms. Conflicting MR character assignments and low pairwise linear correlations among diagnostics are also observed over this set. We evaluate the ability of existing diagnostics to predict the percent recovery of the correlation energy, %<i>E</i><sub>corr</sub>. None of the DFT-based diagnostics are nearly as predictive of %<i>E</i><sub>corr</sub> as the best WFT-based diagnostics. To overcome the limitation of this cost–accuracy trade-off, we develop machine learning (ML, i.e., kernel ridge regression) models to predict WFT-based diagnostics from a combination of DFT-based diagnostics and a new, size-independent 3D geometric representation. The ML-predicted diagnostics correlate as well with MR effects as their computed (i.e., with WFT) values, significantly improving over the DFT-based diagnostics on which the models were trained. These ML models thus provide a promising approach to improve upon DFT-based diagnostic accuracy while remaining suitably low cost for high-throughput screening.

scholarly journals Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3319
Author(s):  
Stuart A. Bagley ◽  
Jonathan A. Atkinson ◽  
Henry Hunt ◽  
Michael H. Wilson ◽  
Tony P. Pridmore ◽  
...  
Keyword(s):  
High Throughput ◽  
Low Cost ◽  
Time Lapse ◽  
Plant Phenotyping ◽  
Environmental Sensors ◽  
Controlled Conditions ◽  
Sensor Platform ◽  
High Throughput Phenotyping ◽  
Controlled Environments ◽  
Time Lapse Imaging

High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of “affordable phenotyping” solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.

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 Maize-IAS: A Maize Image Analysis Software using Deep Learning for High-throughput Plant Phenotyping

2021 ◽  
Author(s):  
Shuo Zhou ◽  
Xiujuan Chai ◽  
Zixuan Yang ◽  
Hongwu Wang ◽  
Chenxue Yang ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
High Throughput ◽  
Batch Processing ◽  
Food Sources ◽  
Plant Phenotyping ◽  
Plant Science ◽  
Phenotypic Traits ◽  
Analysis Software ◽  
Maize Growth

Abstract Background : Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results : On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-PAS ( Maize Phenotyping Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: I. Projection, II. Color Analysis, III. Internode length, IV. Height, V. Stem Diameter and VI. Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion : The Maize-PAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science. Keywords : Maize phenotyping; Instance segmentation; Computer vision; Deep learning; Convolutional neural network

scholarly journals Broad-scale Applications of the Raspberry Pi: A Review and Guide for Biologists

2021 ◽  
Author(s):  
Jolle Wolter Jolles
Keyword(s):  
Scientific Community ◽  
Large Scale ◽  
Low Cost ◽  
Ease Of Use ◽  
Plant Phenotyping ◽  
Raspberry Pi ◽  
Ecosystem Monitoring ◽  
Diverse Range ◽  
Wide Range ◽  
Macro Scale

The field of biology has seen tremendous technological progress in recent years, fuelled by the exponential growth in processing power and high-level computing, and the rise of global information sharing. Low-cost single-board computers are predicted to be one of the key technological advancements to further revolutionise this field. So far, an overview of current uptake of these devices and a general guide to help researchers integrate them in their work has been missing. In this paper I focus on the most widely used single board computer, the Raspberry Pi. Reviewing its broad applications and uses across the biological domain shows that since its release in 2012 the Raspberry Pi has been increasingly taken up by biologists, both in the lab, the field, and in the classroom, and across a wide range of disciplines. A hugely diverse range of applications already exist that range from simple solutions to dedicated custom-build devices, including nest-box monitoring, wildlife camera trapping, high-throughput behavioural recordings, large-scale plant phenotyping, underwater video surveillance, closed-loop operant learning experiments, and autonomous ecosystem monitoring. Despite the breadth of its implementations, the depth of uptake of the Raspberry Pi by the scientific community is still limited. The broad capabilities of the Raspberry Pi, combined with its low cost, ease of use, and large user community make it a great research tool for almost any project. To help accelerate the uptake of Raspberry Pi’s by the scientific community, I provide detailed guidelines, recommendations, and considerations, and 30+ step-by-step guides on a dedicated accompanying website (raspberrypi-guide.github.io). I hope with this paper to generate more awareness about the Raspberry Pi and thereby fuel the democratisation of science and ultimately help advance our understanding of biology, from the micro- to the macro-scale.

scholarly journals Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

2020 ◽  
Vol 2020 ◽  
pp. 1-22 ◽  
Author(s):  
Yu Jiang ◽  
Changying Li
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
High Throughput ◽  
Plant Stress ◽  
Postharvest Quality ◽  
Plant Phenotyping ◽  
Future Research ◽  
Phenotypic Traits ◽  
Deep Convolutional Neural Networks ◽  
Comprehensive Overview

Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.

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.

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