AIMC Topic: Malus

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OptiNet-B3: a lightweight explainable deep learning model for multiclass classification of fruit and leaf diseases.

Scientific reports
Early and accurate detection of diseases is very important for the health of crops and ensuring sustainable agricultural productivity. This paper proposes OptiNet-B3, a novel approach and an efficient deep model for the multiclass classification of f...

An enhanced deep learning-based framework for diagnosing apple leaf diseases.

Scientific reports
Timely and correct identification of diseases in the apple leaf is also important in protecting crop production and sustaining agriculture. This paper introduces E-YOLOv8, a lightweight improved version of YOLOv8, that can be implemented in real-time...

The apple detection method based on multimodal features.

PloS one
Accurate detection of apples and other fruits in complex environments remains a formidable challenge due to the intricate interplay of varying lighting conditions, occlusions, and background clutter. Traditional detection methods, which primarily rel...

Predicting the Seasonal Dynamics of Fruit Fly Anastrepha fraterculus Populations in Apple Orchards Using Artificial Neural Networks.

Neotropical entomology
The fruit fly Anastrepha fraterculus (Wiedemann) (Diptera: Tephritidae) is one of the main pests in apple orchards. Artificial neural networks (ANNs) are tools with good ability to predict phenomena such as the seasonal dynamics of pest populations. ...

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

BMC plant biology
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.

A fine tuned EfficientNet-B0 convolutional neural network for accurate and efficient classification of apple leaf diseases.

Scientific reports
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...

Advancing freshness classification of freshly squeezed fruit juice via integrated multivariate analysis and machine learning approaches.

Food chemistry
In this study, multivariate analysis (MVA) and machine learning (ML), combined with UHPLC-HRMS, were used to evaluate the freshness of apples for juice production based on the analysis of freshly squeezed apple juice. A total of eight stages of apple...

Enhancing the dataset of CycleGAN-M and YOLOv8s-KEF for identifying apple leaf diseases.

PloS one
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...

A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production.

Sensors (Basel, Switzerland)
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...

RE-YOLO: An apple picking detection algorithm fusing receptive-field attention convolution and efficient multi-scale attention.

PloS one
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...