An improved YOLOv5s model using feature concatenation with attention mechanism for real-time fruit detection and counting [PDF]
Frontiers in Plant Science, 2023 An improved YOLOv5s model was proposed and validated on a new fruit dataset to solve the real-time detection task in a complex environment. With the incorporation of feature concatenation and an attention mechanism into the original YOLOv5s network, the ...
Olarewaju Mubashiru Lawal +2 more
doaj +2 more sources
EasyDAM_V2: Efficient Data Labeling Method for Multishape, Cross-Species Fruit Detection. [PDF]
Plant Phenomics, 2022 In modern smart orchards, fruit detection models based on deep learning require expensive dataset labeling work to support the construction of detection models, resulting in high model application costs.
Zhang W, Chen K, Zheng C, Liu Y, Guo W.
europepmc +2 more sources
Real Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT. [PDF]
Sensors (Basel), 2021 This study aimed to produce a robust real-time pear fruit counter for mobile applications using only RGB data, the variants of the state-of-the-art object detection model YOLOv4, and the multiple object-tracking algorithm Deep SORT.
Parico AIB, Ahamed T.
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Agronomy, 2023 Continuing progress in machine learning (ML) has led to significant advancements in agricultural tasks. Due to its strong ability to extract high-dimensional features from fruit images, deep learning (DL) is widely used in fruit detection and automatic ...
Feng Xiao +3 more
doaj +2 more sources
Fruit Detection and Pose Estimation for Grape Cluster-Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks. [PDF]
Front Robot AI, 2021 Reliable and robust fruit-detection algorithms in nonstructural environments are essential for the efficient use of harvesting robots. The pose of fruits is crucial to guide robots to approach target fruits for collision-free picking. To achieve accurate
Yin W +5 more
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DeepFruits: A Fruit Detection System Using Deep Neural Networks. [PDF]
Sensors (Basel), 2016 This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a ...
Sa I +5 more
europepmc +2 more sources
Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning. [PDF]
Front Plant Sci, 2020 Accurately detecting and counting fruits during plant growth using imaging and computer vision is of importance not only from the point of view of reducing labor intensive manual measurements of phenotypic information, but also because it is a critical ...
Afonso M +7 more
europepmc +2 more sources
An Embedded Real-Time Red Peach Detection System Based on an OV7670 Camera, ARM Cortex-M4 Processor and 3D Look-Up Tables [PDF]
Sensors, 2012 This work proposes the development of an embedded real-time fruit detection system for future automatic fruit harvesting. The proposed embedded system is based on an ARM Cortex-M4 (STM32F407VGT6) processor and an Omnivision OV7670 color camera.
Marcel Tresanchez +5 more
doaj +5 more sources
YOLOMuskmelon: Quest for Fruit Detection Speed and Accuracy Using Deep Learning
IEEE Access, 2021 Fruit detection plays a vital role in harvesting robot platforms. However, complicated environment attributes such as illumination variation, occlusion, have made fruit detection a challenging task.
Olarewaju M. Lawal
doaj +2 more sources
A Review of Non-destructive Detection for Fruit Quality [PDF]
, 2010 An overview of non-destructive detection in quality of post-harvest fruit was presented in this paper, and the research and application were discussed. This paper elaborated the fruit quality detection methods which were based on one of the following properties: optical properties, sonic vibration, machine vision technique, nuclear magnetic resonance ...
Gao Hai-sheng, Fengmei Zhu, Jinxing Cai
openalex +3 more sources