AIMC Topic: Malus

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DBA-ViNet: an effective deep learning framework for fruit disease detection and classification using explainable AI.

BMC plant biology Jul 28, 2025
OBJECTIVE: The primary aim of this research is to develop an effective and robust model for identifying and classifying diseases in general fruits, particularly apples, guavas, mangoes, pomegranates, and oranges, utilizing computer vision techniques.
Fruit Plant Diseases Malus Neural Networks, Computer Deep Learning
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A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases.

Scientific reports Jul 16, 2025
Precise classification and detection of apple diseases are essential for efficient crop management and maximizing yield. This paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple lea...
Convolutional Neural Networks Plant Diseases Plant Leaves Neural Networks, Computer Malus
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Enhancing the dataset of CycleGAN-M and YOLOv8s-KEF for identifying apple leaf diseases.

PloS one May 30, 2025
Accurate diagnosis of apple diseases is vital for tree health, yield improvement, and minimizing economic losses. This study introduces a deep learning-based model to tackle issues like limited datasets, small sample sizes, and low recognition accura...
Plant Leaves Plant Diseases Malus Algorithms Deep Learning
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A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production.

Sensors (Basel, Switzerland) Apr 12, 2025
The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study review...
Neural Networks, Computer Deep Learning Fruit Malus
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RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention.

PloS one Mar 3, 2025
The widespread cultivation of apples highlights the importance of efficient and accurate apple detection algorithms in robotic picking technology. The accuracy of current apple picking detection algorithms is still limited when the distribution is dens...
Neural Networks, Computer Algorithms Robotics Malus Detection Algorithms
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Deep learning combined Monte Carlo simulation reveal the fundamental light propagation in apple puree: Monitoring the quality changes from different cultivar, storage period and heating duration.

Food research international (Ottawa, Ont.) Feb 21, 2025
This work explored the light propagation of purees from a large variability of apple cultivar, storage period and heating duration based on their optical absorption (μ) and reduced scattering (μ') properties at 900-1650 nm, in order to better monitor...
Food Storage Monte Carlo Method Malus Fruit Rheology Deep Learning Neural Networks, Computer Viscosity Light Hot Temperature
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Exploring the impact of lenticels on the detection of soluble solids content in apples and pears using hyperspectral imaging and one-dimensional convolutional neural networks.

Food research international (Ottawa, Ont.) Feb 7, 2025
In this work, the effect of lenticels on the predictive performance of apple and pear soluble solids content (SSC) models developed based on hyperspectral imaging (HSI) at 380-1010 nm was investigated for the first time. Variations in the spectral pr...
Hyperspectral Imaging Calibration Discriminant Analysis Fruit Malus Pyrus Algorithms Convolutional Neural Networks Principal Component Analysis Least-Squares Analysis Neural Networks, Computer
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Gripping Success Metric for Robotic Fruit Harvesting.

Sensors (Basel, Switzerland) Dec 31, 2024
Recently, computer vision methods have been widely applied to agricultural tasks, such as robotic harvesting. In particular, fruit harvesting robots often rely on object detection or segmentation to identify and localize target fruits. During the mod...
Fruit Algorithms Malus Agriculture Robotics
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Comparative performance of artificial neural networks and support vector Machines in detecting adulteration of apple juice concentrate using spectroscopy and time domain NMR.

Food research international (Ottawa, Ont.) Dec 30, 2024
The detection of adulteration in apple juice concentrate is critical for ensuring product authenticity and consumer safety. This study evaluates the effectiveness of artificial neural networks (ANN) and support vector machines (SVM) in analyzing spec...
Neural Networks, Computer Magnetic Resonance Spectroscopy Fruit and Vegetable Juices Spectroscopy, Near-Infrared Malus Support Vector Machine Food Contamination Food Analysis
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Incremental RPN: Hierarchical Region Proposal Network for Apple Leaf Disease Detection in Natural Environments.

IEEE/ACM transactions on computational biology and bioinformatics Dec 10, 2024
Apple leaf diseases can seriously affect apple production and quality, and accurately detecting them can improve the efficiency of disease monitoring. Owing to the complex natural growth environment, apple leaf lesions may be easily confused with bac...
Plant Leaves Image Processing, Computer-Assisted Neural Networks, Computer Plant Diseases Computational Biology Algorithms Malus
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