Intelligent Integrated System for Fruit Detection Using Multi-UAV Imaging and Deep Learning [PDF]
SensorsIn the context of Industry 4.0, one of the most significant challenges is enhancing efficiency in sectors like agriculture by using intelligent sensors and advanced computing.
Oleksandr Melnychenko +4 more
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Lightweight Fruit-Detection Algorithm for Edge Computing Applications [PDF]
Frontiers in Plant Science, 2021 In recent years, deep-learning-based fruit-detection technology has exhibited excellent performance in modern horticulture research. However, deploying deep learning algorithms in real-time field applications is still challenging, owing to the relatively
Wenli Zhang +11 more
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deepNIR: Datasets for Generating Synthetic NIR Images and Improved Fruit Detection System Using Deep Learning Techniques [PDF]
Sensors, 2022 This paper presents datasets utilised for synthetic near-infrared (NIR) image generation and bounding-box level fruit detection systems. A high-quality dataset is one of the essential building blocks that can lead to success in model generalisation and ...
Inkyu Sa +3 more
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Fruit Detection and Segmentation for AppleHarvesting Using Visual Sensor in Orchards. [PDF]
Sensors (Basel), 2019 In this research, a fully neural network based visual perception framework for autonomous apple harvesting is proposed. The proposed framework includes a multi-function neural network for fruit recognition and a Pointnet grasp estimation to determine the proper grasp pose to guide the robotic execution.
Kang H, Chen C.
europepmc +4 more sources
KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data. [PDF]
Data Brief, 2019 This article contains data related to the research article entitle 'Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities' [1].
Gené-Mola J +5 more
europepmc +7 more sources
Automatic Parameter Tuning for Adaptive Thresholding in Fruit Detection [PDF]
Sensors, 2019 This paper presents an automatic parameter tuning procedure specially developed for a dynamic adaptive thresholding algorithm for fruit detection. One of the major algorithm strengths is its high detection performances using a small set of training ...
Elie Zemmour, Polina Kurtser, Yael Edan
doaj +7 more sources
YOLOv7-Plum: Advancing Plum Fruit Detection in Natural Environments with Deep Learning. [PDF]
Plants (Basel), 2023 The plum is a kind of delicious and common fruit with high edible value and nutritional value. The accurate and effective detection of plum fruit is the key to fruit number counting and pest and disease early warning.
Tang R, Lei Y, Luo B, Zhang J, Mu J.
europepmc +2 more sources
Lightweight SM-YOLOv5 Tomato Fruit Detection Algorithm for Plant Factory. [PDF]
Sensors (Basel), 2023 Due to their rapid development and wide application in modern agriculture, robots, mobile terminals, and intelligent devices have become vital technologies and fundamental research topics for the development of intelligent and precision agriculture ...
Wang X +6 more
europepmc +2 more sources
A simplified network topology for fruit detection, counting and mobile-phone deployment. [PDF]
PLoS One, 2023 The complex network topology, deployment unfriendliness, computation cost, and large parameters, including the natural changeable environment are challenges faced by fruit detection.
Lawal OM, Zhu S, Cheng K, Liu C.
europepmc +2 more sources
FCOS-LSC: A Novel Model for Green Fruit Detection in a Complex Orchard Environment. [PDF]
Plant Phenomics, 2023 To better address the difficulties in designing green fruit recognition techniques in machine vision systems, a new fruit detection model is proposed.
Zhao R +5 more
europepmc +2 more sources