Real-Time Detection of Mango Based on Improved YOLOv4

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 Agriculture, Multidisciplinary

Pineapple (Ananas comosus) fruit detection and localization in natural environment based on binocular stereo vision and improved YOLOv3 model

Tian-Hu Liu et al.

Summary: This paper proposes a method for detecting and localizing pineapples in natural environments using binocular stereo vision and an improved YOLOv3 model. Experimental results show that the improved model outperforms other models in terms of detection accuracy and localization precision.

PRECISION AGRICULTURE (2023)

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Article Agriculture, Multidisciplinary

Real-time defects detection for apple sorting using NIR cameras with pruning-based YOLOV4 network

Shuxiang Fan et al.

Summary: This study developed an inspection module using NIR cameras and diffuse illumination chamber for online detection of defective apples in a two-lane fruit sorting machine. A real-time apple defect inspection method based on YOLO V4 deep learning algorithm was proposed, which utilized channel and layer pruning to simplify the network and improve detection speed. The pruning-based YOLO V4 network using NIR images showed reduced model size and inference time, increased mean average precision, and was suitable for detecting various apple cultivars.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

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Article Engineering, Mechanical

Lightweight object detection network model suitable for indoor mobile robots

Lin Jiang et al.

Summary: This study proposes a lightweight object detection network model called S-SSD, which improves the SSD network model to meet the real-time performance requirement in indoor mobile robot object detection and recognition tasks. The results show that the S-SSD network model outperforms other lightweight network models in terms of detection accuracy and detection rate, and can simultaneously meet the requirements of accuracy and real-time performance in indoor object detection and recognition tasks of mobile robots.

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2022)

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Article Agriculture, Multidisciplinary

A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern orchard

Fangfang Gao et al.

Summary: This study develops an automated video processing method to improve the counting accuracy of apple fruits in orchard environment. By tracking the trunk instead of the fruits, a higher accuracy and speed can be achieved. The method combines YOLOv4-tiny network and CSR-DCF algorithm for training and utilizes frame displacement to predict fruit locations. Results show a high counting accuracy in orchard videos.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

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Article Multidisciplinary Sciences

Online recognition and yield estimation of tomato in plant factory based on YOLOv3

Xinfa Wang et al.

Summary: A recognition method based on an improved yolov3 deep learning model was proposed for intelligent online yield estimation of tomatoes in a plant factory with artificial lighting. The experimental results showed that the improved model achieved higher accuracy and better identification effects for dense and shaded fruits.

SCIENTIFIC REPORTS (2022)

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Article Agriculture, Multidisciplinary

Multiple disease detection method for greenhouse-cultivated strawberry based on multiscale feature fusion Faster R_CNN

Shengyi Zhao et al.

Summary: Diseases have a significant impact on strawberry quality and yield, and deep learning has become an important approach for disease detection. A new architecture is proposed to accurately extract disease features from natural environments, and experimental results show its effectiveness in strawberry cultivation.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

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Article Agriculture, Multidisciplinary

DSE-YOLO: Detail semantics enhancement YOLO for multi-stage strawberry detection

Yan Wang et al.

Summary: This paper proposes a method called DSE-YOLO for detecting multi-stage strawberries. The method utilizes a Detail-Semantics Enhancement module and specific loss functions to improve accuracy. Experimental results demonstrate the effectiveness of the proposed method and its potential for application in automatic picking and monitoring systems.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2022)

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Article Agriculture, Multidisciplinary

Fast and accurate detection of kiwifruit in orchard using improved YOLOv3-tiny model

Longsheng Fu et al.

Summary: Automatic detection of kiwifruit in the orchard is challenging due to varying illumination and color similarity with the complex background. A deep YOLOv3-tiny model (DY3TNet) was developed for efficient and accurate detection, achieving high precision and fast processing speed, especially in images captured with flash.

PRECISION AGRICULTURE (2021)

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Article Agriculture, Multidisciplinary

Fast detection and location of longan fruits using UAV images

Denghui Li et al.

Summary: This paper proposes a deep learning-based scheme to allow unmanned aerial vehicles (UAVs) to pick longan fruit, improving target detection and location speed and accuracy through the combination of UAVs, RGB-D cameras, and CNNs.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Article Agricultural Engineering

Detection of maturity stages of coconuts in complex background using Faster R-CNN model

Subramanian Parvathi et al.

Summary: An improved Faster R-CNN model was proposed for detecting maturity stages of coconuts, achieving better detection performance compared to other object detectors through training with collected and augmented coconut images.

BIOSYSTEMS ENGINEERING (2021)

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Article Agriculture, Multidisciplinary

A deep learning approach for anthracnose infected trees classification in walnut orchards

A. Anagnostis et al.

Summary: This study presents a novel approach for detecting disease-infected leaves on trees using deep learning, specifically focusing on walnut trees infected with anthracnose. By training an object detector to recognize infected leaves, the system showed potential for real-time application in commercial orchards.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Article Chemistry, Analytical

Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse

Sandro Augusto Magalhaes et al.

Summary: The study provides a visual dataset of green and reddish tomatoes, contributing to the development of robots for greenhouse harvesting. Results show that SSD MobileNet v2 performs the best in detecting green and reddish tomatoes grown in greenhouses.

SENSORS (2021)

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Article Agriculture, Multidisciplinary