Hierarchical Detection of Gastrodia elata Based on Improved YOLOX

Article Computer Science, Artificial Intelligence

A detection algorithm for cherry fruits based on the improved YOLO-v4 model

Rongli Gai et al.

Summary: Digital agriculture has a significant impact on the value of agricultural output. Robotic picking and cherry fruit detection technologies enable more intelligent and productive farming practices.

NEURAL COMPUTING & APPLICATIONS (2023)

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Article Agronomy

An Automatic Jujube Fruit Detection and Ripeness Inspection Method in the Natural Environment

Defang Xu et al.

Summary: To address the issues of fruit detection, such as ripeness and environmental conditions, a method called YOLO-Jujube was proposed. It incorporated various networks and loss functions to automatically detect jujube fruit and inspect its ripeness, outperforming other similar networks in terms of detection performance.

AGRONOMY-BASEL (2023)

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Article Green & Sustainable Science & Technology

Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing

Poonam Dhiman et al.

Summary: In this study, an efficient model for citrus fruit disease prediction is proposed, which combines deep learning models CNN and LSTM with edge computing. By employing an enhanced feature-extraction mechanism and a feature-fusion subsystem, significant recognition on edge computing devices with retaining citrus fruit disease detection accuracy is achieved. The proposed CNN-LSTM model achieves an accuracy rate of 97.18% with Magnitude-Based Pruning and 98.25% with Magnitude-Based Pruning with Post Quantization, outperforming the existing CNN method.

SUSTAINABILITY (2023)

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Article Agronomy

Lidar-Based 3D Obstacle Detection Using Focal Voxel R-CNN for Farmland Environment

Jia Qin et al.

Summary: With advances in precision agriculture, autonomous agricultural machines can reduce human labor, optimize workflow, and increase productivity. In this study, deep learning was applied to LiDAR-based 3D obstacle detection in the farmland environment. By using deep learning algorithms, a data acquisition platform was established, and an effective 3D obstacle detector was trained. The proposed model significantly improves the detection performance for small objects and increases the average precision in the pedestrian class.

AGRONOMY-BASEL (2023)

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Article Biochemistry & Molecular Biology

Identification of Cancer Driver Genes by Integrating Multiomics Data with Graph Neural Networks

Hongzhi Song et al.

Summary: Cancer is a complex disease caused by genetic and nongenetic alterations. This paper proposes a novel semisupervised deep graph learning framework, GGraphSAGE, to predict cancer driver genes by integrating multiomics data. The experimental results show that GGraphSAGE outperforms other computational methods and provides insights into cancer driver genes specific to different tumor types. This research has significance in advancing precision medicine and predicting drivers for other complex diseases.

METABOLITES (2023)

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Article Health Care Sciences & Services

A Multi-Task Convolutional Neural Network for Semantic Segmentation and Event Detection in Laparoscopic Surgery

Giorgia Marullo et al.

Summary: The study proposes a multi-task deep learning model for real-time blood accumulation detection and tools semantic segmentation in laparoscopic surgery videos. The system utilizes a shared backbone and separate branches to classify blood accumulation events and output segmentation maps. The multi-task approach achieves satisfactory results in surgical video tests, trained only with RGB images. The proposed network achieves an 81.89% Dice score for semantic segmentation and a 90.63% accuracy for event detection.

JOURNAL OF PERSONALIZED MEDICINE (2023)

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Article Agronomy

YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment

Lanhui Fu et al.

Summary: This study proposes and compares two improved YOLOv4 neural network detection models, YOLO-Banana and YOLO-Banana-l4, in a banana orchard. The results show that both models can reduce network weight and shorten detection time compared to YOLOv4. The YOLO-Banana model has the best performance, with high detection accuracy for banana bunches and stalks in natural environment.

AGRONOMY-BASEL (2022)

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Review Computer Science, Interdisciplinary Applications

Medical deep learning-A systematic meta-review

Jan Egger et al.

Summary: Deep learning has made remarkable impact in various scientific disciplines, such as image processing and autonomous driving. It has also shown great potential in the medical domain. However, obtaining a complete overview of the field of 'medical deep learning' is becoming increasingly difficult due to the abundance of patient data and the rapid growth of deep learning research.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2022)

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Article Agronomy

Image classification of root-trimmed garlic using multi-label and multi-class classification with deep convolutional neural network

Pham Thi Quynh Anh et al.

Summary: The study introduced a modified multi-class model and a multi-label model for classifying garlic bulbs after root trimming. The multi-label model showed better overall accuracy in different classes. By adding a background image class, the classification accuracy of both models further improved.

POSTHARVEST BIOLOGY AND TECHNOLOGY (2022)

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Article Agronomy

Xiaomila Green Pepper Target Detection Method under Complex Environment Based on Improved YOLOv5s

Fenghua Wang et al.

Summary: This paper proposes an improved YOLOv5s-CFL model for real-time detection of fruit targets in the Xiaomila green pepper picking robot. The model enhances detection accuracy and efficiency through modifications in the convolutional layer, the addition of a Coordinate Attention layer, and replacement of the Path Aggregation Network with the Bidirectional Feature Pyramid Network. Experimental results show that the improved model outperforms other models in detecting Xiaomila green peppers, and it has lightweight and real-time capabilities.

AGRONOMY-BASEL (2022)

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Article Computer Science, Artificial Intelligence

Focal and efficient IOU loss for accurate bounding box regression

Yi-Fan Zhang et al.

Summary: This paper focuses on the crucial step of bounding box regression in object detection and proposes two improvements, named EIOU loss and Focal-EIOU loss, to address the issues with previous loss functions. Experimental results demonstrate notable superiority in convergence speed and localization accuracy compared to other methods.

NEUROCOMPUTING (2022)

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Article Agronomy

A Real-Time Detection Algorithm for Sweet Cherry Fruit Maturity Based on YOLOX in the Natural Environment

Zhiyong Li et al.

Summary: The paper proposes an improved YOLOX target detection algorithm for accurately detecting the ripeness categories of sweet cherries in complex environments. By constructing the SweetCherry dataset and embedding the Convolutional Block Attention Module (CBAM) in the YOLOX model, the algorithm improves the model's attention to different channels and spaces, and replaces the original bounding box loss function with Efficient IoU (EIoU) loss for more accurate regression of prediction boxes. Experimental results show that the proposed method outperforms traditional algorithms and the YOLOX model in terms of various metrics, and can handle complex backgrounds and provide data and references for similar target detection problems.

AGRONOMY-BASEL (2022)

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Article Agronomy

A Real-Time Sorting Robot System for Panax Notoginseng Taproots Equipped with an Improved Deeplabv3+ Model

Fujie Zhang et al.

Summary: A real-time sorting robot system for classifying Panax notoginseng taproots based on the improved DeepLabv3+ model was developed. The system achieved high segmentation accuracy and detection speed, meeting the requirements of Panax notoginseng processing enterprises.

AGRICULTURE-BASEL (2022)

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Article Agriculture, Multidisciplinary

Fruit detection and load estimation of an orange orchard using the YOLO models through simple approaches in different imaging and illumination conditions

Hamzeh Mirhaji et al.

Summary: Fruit load estimation is crucial for Precision Agriculture. The study applied YOLO detection models to detect and count ripe oranges in an orchard, achieving high performance and accuracy through transfer learning and RGB image training. The YOLO-V4 model showed promising results in detecting and estimating orange yield, with high precision and performance.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Article Agricultural Engineering

Distinguishing seedling volunteer corn from soybean through greenhouse color, color-infrared, and fused images using machine and deep learning

P. Flores et al.

Summary: This study collected color RGB and color-infrared images of volunteer corn (VC) and soybean seedlings in a greenhouse, and found that support vector machine (SVM) and GoogLeNet performed best on the fused images. GoogLeNet was selected for its high accuracy, reasonable computation time, and ease of application.

INDUSTRIAL CROPS AND PRODUCTS (2021)

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Article Chemistry, Analytical

Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion

Yinlong Zhu et al.

Summary: This study establishes a classification model for Panax notoginseng taproots based on image feature fusion, validating the importance of color, texture, and fusion features for classification through various models. Multiple dimensionality reduction methods are used to establish the main root classification model, providing a basis for developing online classification methods for different grades of Panax notoginseng in actual production.

SENSORS (2021)

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Review Agriculture, Multidisciplinary

Automation in Agriculture by Machine and Deep Learning Techniques: A Review of Recent Developments

Muhammad Hammad Saleem et al.

Summary: In recent years, there has been a growing interest in the application of automation in agriculture using artificial intelligence techniques and robotic systems. Significant improvements in agricultural tasks have been observed with the advancements in machine learning and deep learning concepts. Studies show that deep learning outperforms traditional machine learning algorithms in areas such as plant disease detection, crop recognition, and land cover classification.

PRECISION AGRICULTURE (2021)

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Article Food Science & Technology

Online defect detection and automatic grading of carrots using computer vision combined with deep learning methods

Limiao Deng et al.

Summary: This study developed an automatic carrot grading system based on computer vision and deep learning, utilizing a lightweight model and multiple techniques to detect and grade surface defects of carrots, achieving high accuracy and performance.

LWT-FOOD SCIENCE AND TECHNOLOGY (2021)

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Article Computer Science, Artificial Intelligence