Machine Learning and Statistical Approaches for Plant Phenotyping

2020 ◽  
pp. 195-220
Author(s):  
Zheng Xu ◽  
Cong Wu
Keyword(s):  
Machine Learning ◽  
Plant Phenotyping ◽  
Statistical Approaches

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

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3208 ◽  
Author(s):  
Liangju Wang ◽  
Yunhong Duan ◽  
Libo Zhang ◽  
Tanzeel U. Rehman ◽  
Dongdong Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Professional Training ◽  
Imaging System ◽  
Estimation Method ◽  
Machine Learning Algorithms ◽  
Plant Phenotyping ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Corn Plants ◽  
Hyperspectral Camera

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.

scholarly journals Applications of Metabolomics in Forensic Toxicology and Forensic Medicine

2021 ◽  
Vol 22 (6) ◽  
pp. 3010
Author(s):  
Michal Szeremeta ◽  
Karolina Pietrowska ◽  
Anna Niemcunowicz-Janica ◽  
Adam Kretowski ◽  
Michal Ciborowski
Keyword(s):  
Machine Learning ◽  
Multivariate Statistics ◽  
High Throughput ◽  
Forensic Medicine ◽  
Forensic Toxicology ◽  
Metabolic Changes ◽  
Untargeted Metabolomics ◽  
Criminal Cases ◽  
Statistical Approaches

Forensic toxicology and forensic medicine are unique among all other medical fields because of their essential legal impact, especially in civil and criminal cases. New high-throughput technologies, borrowed from chemistry and physics, have proven that metabolomics, the youngest of the “omics sciences”, could be one of the most powerful tools for monitoring changes in forensic disciplines. Metabolomics is a particular method that allows for the measurement of metabolic changes in a multicellular system using two different approaches: targeted and untargeted. Targeted studies are focused on a known number of defined metabolites. Untargeted metabolomics aims to capture all metabolites present in a sample. Different statistical approaches (e.g., uni- or multivariate statistics, machine learning) can be applied to extract useful and important information in both cases. This review aims to describe the role of metabolomics in forensic toxicology and in forensic medicine.

Soil erosion potential hotspot zone identification using machine learning and statistical approaches in eastern India

2020 ◽  
Vol 104 (2) ◽  
pp. 1259-1294 ◽  
Author(s):  
Rabin Chakrabortty ◽  
Subodh Chandra Pal ◽  
Mehebub Sahana ◽  
Ayan Mondal ◽  
Jie Dou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Erosion ◽  
Eastern India ◽  
Statistical Approaches

Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis based on resting-state EEG Recordings: A Systematic Review

2020 ◽  
Author(s):  
Katerina D. Tzimourta ◽  
Vasileios Christou ◽  
Alexandros T. Tzallas ◽  
Nikolaos Giannakeas ◽  
Loukas G. Astrakas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Statistical Approaches ◽  
Eeg Recordings ◽  
Disease Analysis

Combining biometric matchers by means of machine learning and statistical approaches

2015 ◽  
Vol 149 ◽  
pp. 526-535 ◽  
Author(s):  
Loris Nanni ◽  
Alessandra Lumini ◽  
Matteo Ferrara ◽  
Raffaele Cappelli
Keyword(s):  
Machine Learning ◽  
Statistical Approaches

scholarly journals Deep Machine Learning provides state-of-the-art performance in image-based plant phenotyping

2016 ◽  
Author(s):  
Michael P. Pound ◽  
Alexandra J. Burgess ◽  
Michael H. Wilson ◽  
Jonathan A. Atkinson ◽  
Marcus Griffiths ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Analysis ◽  
Paradigm Shift ◽  
State Of The Art ◽  
Plant Phenotyping ◽  
Learning Approaches ◽  
Challenging Problem ◽  
Feature Identification ◽  
Art Performance

AbstractDeep learning is an emerging field that promises unparalleled results on many data analysis problems. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping, and demonstrate state-of-the-art results for root and shoot feature identification and localisation. We predict a paradigm shift in image-based phenotyping thanks to deep learning approaches.

scholarly journals TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Snehal Shete ◽  
Srikant Srinivasan ◽  
Timothy A. Gonsalves
Keyword(s):  
Machine Learning ◽  
Plant Traits ◽  
Plant Phenotyping ◽  
Learning Models ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Maize Tassel ◽  
Adversarial Model ◽  
Non Destructive ◽  
Machine Learning Models

Machine learning-based plant phenotyping systems have enabled high-throughput, non-destructive measurements of plant traits. Tasks such as object detection, segmentation, and localization of plant traits in images taken in field conditions need the machine learning models to be developed on training datasets that contain plant traits amidst varying backgrounds and environmental conditions. However, the datasets available for phenotyping are typically limited in variety and mostly consist of lab-based images in controlled conditions. Here, we present a new method called TasselGAN, using a variant of a deep convolutional generative adversarial network, to synthetically generate images of maize tassels against sky backgrounds. Both foreground tassel images and background sky images are generated separately and merged together to form artificial field-based maize tassel data to aid the training of machine learning models, where there is a paucity of field-based data. The effectiveness of the proposed method is demonstrated using quantitative and perceptual qualitative experiments.

Relevance of Machine Learning to Cardiovascular Imaging

2021 ◽  
pp. 78-99
Author(s):  
Sumesh Sasidharan ◽  
M. Yousuf Salmasi ◽  
Selene Pirola ◽  
Omar A. Jarral
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Cognitive Learning ◽  
Cardiovascular Imaging ◽  
Machine Learning Algorithms ◽  
Patient Specific ◽  
Healthcare Data ◽  
Statistical Approaches ◽  
Specific Management

Artificial intelligence (AI) broadly concerns analytical algorithms that iteratively learn from big datasets, allowing computers to find concealed insights. These encompass a range of operations comprising several terms, including machine learning(ML), cognitive learning, deep learning, and reinforcement learning-based methods that can be used to incorporate and comprehend complex biomedical and healthcare data in scenarios where traditional statistical approaches cannot be implemented. For cardiovascular imaging in particular, machine learning guarantees to be a transformative tool that can address many unmet needs for patient-specific management, accurate prediction of disease progression, and the tracking of identifiable biomarkers of disease processes. In this chapter, the authors discuss fundamentals of machine learning algorithms for image analysis in the cardiovascular system by evaluating the need for ML in this field and examining the potential obstacles and challenges of implementation in the context of three common imaging modalities used in cardiovascular medicine.

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