AIMC Topic: Fruit

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A rapid, non-destructive, and accurate method for identifying citrus granulation using Raman spectroscopy and machine learning.

Journal of food science Dec 10, 2024
Citrus fruits are widely consumed for their nutritional value and taste; however, juice sac granulation during fruit storage poses a significant challenge to the citrus industry. This study used Raman spectroscopy coupled with machine learning algori...
Fruit and Vegetable Juices Citrus Spectrum Analysis, Raman Principal Component Analysis Discriminant Analysis Fruit Least-Squares Analysis Machine Learning Algorithms
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Artificial intelligence-driven quantification of antibiotic-resistant Bacteria in food by color-encoded multiplex hydrogel digital LAMP.

Food chemistry Dec 4, 2024
Antibiotic-resistant bacteria pose considerable risks to global health, particularly through transmission in the food chain. Herein, we developed the artificial intelligence-driven quantification of antibiotic-resistant bacteria in food using a color...
Artificial Intelligence Anti-Bacterial Agents Escherichia coli Food Contamination Methicillin-Resistant Staphylococcus aureus Drug Resistance, Bacterial Molecular Diagnostic Techniques Hydrogels Vegetables Food Microbiology Color Nucleic Acid Amplification Techniques Fruit
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Development of a visuo-tactile sensor for non-destructive peach firmness and contact force measurement suitable for robotic arm applications.

Food chemistry Nov 29, 2024
Precise measurement of firmness was crucial for determining optimal harvesting times, implementing rational storage strategies and minimizing avoidable waste. Current technologies for assessing peach firmness struggled to balance high precision and n...
Prunus persica Fruit Touch Robotics
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Evaluation and process monitoring of jujube hot air drying using hyperspectral imaging technology and deep learning for quality parameters.

Food chemistry Nov 12, 2024
Timely and effective detection of quality attributes during drying control is essential for enhancing the quality of fruit processing. Consequently, this study aims to employ hyperspectral imaging technology for the non-destructive monitoring of solu...
Food Handling Hot Temperature Quality Control Ziziphus Desiccation Deep Learning Fruit Hyperspectral Imaging
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GMOPNet: A GAN-MLP two-stage network for optical properties measurement of kiwifruit and peaches with spatial frequency domain imaging.

Food chemistry Nov 8, 2024
Spatial frequency domain imaging (SFDI) is an imaging technique using spatially modulated illumination for measurement of optical properties. Conventional SFDI methods require capturing at least six images, making it time-consuming. This study presen...
Neural Networks, Computer Fruit Optical Imaging Actinidia Prunus persica
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TOMMicroNet: Convolutional Neural Networks for Smartphone-Based Microscopic Detection of Tomato Biotic and Abiotic Plant Health Issues.

Phytopathology Oct 30, 2024
The image-based detection and classification of plant diseases has become increasingly important to the development of precision agriculture. We consider the case of tomato, a high-value crop supporting the livelihoods of many farmers around the worl...
Microscopy Plant Diseases Image Processing, Computer-Assisted Smartphone Solanum lycopersicum Fruit Plant Leaves Neural Networks, Computer
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Predicting physicochemical properties of papayas (Carica papaya L.) using a convolutional neural networks model approach.

Journal of food science Oct 16, 2024
The current state of quality assessment methods for agricultural produce, particularly fruits, heavily relies on manual inspection techniques, which could be subjective, time-consuming, and prone to human errors. Consequently, there have been emergin...
Carica Hydrogen-Ion Concentration Fruit Image Processing, Computer-Assisted Seeds Neural Networks, Computer
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Fusion of visible and fluorescence imaging through deep neural network for color value prediction of pelletized red peppers.

Journal of food science Oct 11, 2024
A non-destructive method for determining the color value of pelletized red peppers is crucial for pepper processing factories. This study aimed to investigate the potentiality of visible and fluorescence images for the determination of color value of...
Neural Networks, Computer Capsicum Image Processing, Computer-Assisted Fluorescence Fruit Color Optical Imaging
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Rheological modelling of apple puree based on machine learning combined Monte Carlo simulation: Insight into the fundamental light- particle structure interaction processes.

Food chemistry Oct 10, 2024
In this work, apple purees from different particle concentration and verifying in size were reconstituted to investigate their impacts on rheological behaviors, optical properties and light interaction at 900-1650 nm. The optical scattering of differ...
Particle Size Light Viscosity Machine Learning Fruit Monte Carlo Method Rheology Malus
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Integrating deep learning and data fusion for enhanced oranges soluble solids content prediction using machine vision and Vis/NIR spectroscopy.

Food chemistry Oct 1, 2024
The visible/near infrared (Vis/NIR) spectrum will become distorted due to variations in sample color, thereby reducing the prediction accuracy of fruit composition. In this study, we aimed to develop a deep learning model with color correction capabi...
Deep Learning Color Fruit Neural Networks, Computer Spectroscopy, Near-Infrared
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