AIMC Topic: Blueberry Plants

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Rapid detection and quantitative analysis of thiram in fruits using a shape-adaptable flexible SERS substrate combined with deep learning.

Analytical methods : advancing methods and applications Feb 20, 2025
Ensuring food safety necessitates rapid identification of pesticide residues on fruits. Herein, we developed a shape-adaptable flexible surface-enhanced Raman scattering (SERS) substrate, combined with a deep learning algorithm, to quickly detect and...
Solanum lycopersicum Fruit Silver Blueberry Plants Deep Learning Pesticide Residues Thiram Metal Nanoparticles Dimethylpolysiloxanes Spectrum Analysis, Raman Neural Networks, Computer Reproducibility of Results
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Exploring a universal model for predicting blueberry soluble solids content based on hyperspectral imaging and transfer learning to address spatial heterogeneity challenge.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Feb 18, 2025
Accurate assessment of soluble solid content (SSC) in blueberries is crucial for quality evaluation. However, in real production lines, blueberries are usually in random placement and the biological heterogeneity of blueberry parts can lead to spectr...
Solubility Hyperspectral Imaging Fruit Machine Learning Blueberry Plants
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Fruit wines classification enabled by combing machine learning with comprehensive volatiles profiles of GC-TOF/MS and GC-IMS.

Food research international (Ottawa, Ont.) Feb 5, 2025
Fruit wines, produced through the fermentation of various fruits, are well-documented for their distinct flavor profiles. Intelligent sensory analysis, GC-TOF/MS and GC-IMS were used for the analysis of the volatile profile of eight types of fruit wi...
Blueberry Plants Prunus persica Volatile Organic Compounds Gas Chromatography-Mass Spectrometry Taste Vitis Fermentation Machine Learning Wine Fruit Neural Networks, Computer
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Deep learning supported machine vision system to precisely automate the wild blueberry harvester header.

Scientific reports Jun 23, 2023
An operator of a wild blueberry harvester faces the fatigue of manually adjusting the height of the harvester's head, considering spatial variations in plant height, fruit zone, and field topography affecting fruit yield. For stress-free harvesting o...
Marijuana Abuse Blueberry Plants Fruit Fatigue Deep Learning
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Non-destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning.

Journal of the science of food and agriculture Dec 11, 2020
BACKGROUND: This paper proposes a novel method to improve accuracy and efficiency in detecting the quality of blueberry fruit, taking advantage of deep learning in classification tasks. We first collected 'Tifblue' blueberries at seven different stag...
Anthocyanins Blueberry Plants Deep Learning Chlorophyll Fruit Pigments, Biological Food Analysis
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Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data.

Sensors (Basel, Switzerland) Apr 7, 2018
Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version na...
Algorithms Time Factors Machine Learning Blueberry Plants ROC Curve Neural Networks, Computer
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Predicting Ascospore Release of Monilinia vaccinii-corymbosi of Blueberry with Machine Learning.

Phytopathology Sep 15, 2017
Mummy berry, caused by Monilinia vaccinii-corymbosi, causes economic losses of highbush blueberry in the U.S. Pacific Northwest (PNW). Apothecia develop from mummified berries overwintering on soil surfaces and produce ascospores that infect tissue e...
Ascomycota Blueberry Plants Machine Learning Spores, Fungal Models, Biological
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