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Plant Leaves

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Support Vector Machine and Artificial Neural Network Models for the Classification of Grapevine Varieties Using a Portable NIR Spectrophotometer.

PloS one
The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work p...

Identification of novel Xanthomonas euvesicatoria type III effector proteins by a machine-learning approach.

Molecular plant pathology
The Gram-negative bacterium Xanthomonas euvesicatoria (Xcv) is the causal agent of bacterial spot disease in pepper and tomato. Xcv pathogenicity depends on a type III secretion (T3S) system that delivers effector proteins into host cells to suppress...

Enhancing spatial resolution of (18)F positron imaging with the Timepix detector by classification of primary fired pixels using support vector machine.

Physics in medicine and biology
Position-sensitive positron cameras using silicon pixel detectors have been applied for some preclinical and intraoperative clinical applications. However, the spatial resolution of a positron camera is limited by positron multiple scattering in the ...

Estimation of mesophyll conductance in Ginkgo biloba from the PSII redox state using a machine learning approach.

Tree physiology
Mesophyll conductance (gm) has been proved to be one of the important factors limiting photosynthesis and thus affects the estimation of plant productivity and terrestrial carbon balance. However, beyond the leaf scale, gm is usually assumed to be in...

Early and late blight disease identification in tomato plants using a neural network-based model to augmenting agricultural productivity.

Science progress
Computer-advanced technologies have a significant impact across various fields. It is widely recognized that diseases have a detrimental effect on crop productivity and can significantly impact the economy, particularly in agricultural countries. Tom...

Machine learning for image-based multi-omics analysis of leaf veins.

Journal of experimental botany
Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form ...

Integrating a crop growth model and radiative transfer model to improve estimation of crop traits based on deep learning.

Journal of experimental botany
A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. To address this problem, this research investigated a conceptual framework by integrating a crop g...

Different stages of disease detection in squash plant based on machine learning.

Journal of biosciences
To increase agriculture production, accurate and fast detection of plant disease is required. Expert advice is needed to detect disease in plants, nutrition deficiencies or any other abnormalities caused by extreme weather conditions. But this proces...

Machine Learning for Image Analysis: Leaf Disease Segmentation.

Methods in molecular biology (Clifton, N.J.)
Plant phenomics field has seen a great increase in scalability in the last decade mainly due to technological advances in remote sensors and phenotyping platforms. These are capable of screening thousands of plants many times throughout the day, gene...

Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms.

Plant physiology
Measuring leaf area index (LAI) is essential for evaluating crop growth and estimating yield, thereby facilitating high-throughput phenotyping of maize (Zea mays). LAI estimation models use multi-source data from unmanned aerial vehicles (UAVs), but ...