Efficient and lightweight grape and picking point synchronous detection model based on key point detection

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 217, P. 108612 - 108612

Published: Jan. 5, 2024

Language: Английский

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Neuromorphic Computing for Smart Agriculture

Agriculture, Journal Year: 2024, Volume and Issue: 14(11), P. 1977 - 1977

Published: Nov. 4, 2024

Neuromorphic computing has received more and attention recently since it can process information interact with the world like human brain. Agriculture is a complex system that includes many processes of planting, breeding, harvesting, processing, storage, logistics, consumption. Smart devices in association artificial intelligence (AI) robots Internet Things (IoT) systems have been used also need to be improved accommodate growth computing. great potential promote development smart agriculture. The aim this paper describe current principles neuromorphic technology, explore examples applications agriculture, consider future route synapses, neurons, neural networks (ANNs). A expected improve agricultural production efficiency ensure food quality safety for nutrition health agriculture future.

Language: Английский

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A review of cross-scale and cross-modal intelligent sensing and detection technology for food quality: mechanism analysis, decoupling strategy and integrated applications

Trends in Food Science & Technology, Journal Year: 2024, Volume and Issue: 151, P. 104646 - 104646

Published: July 20, 2024

Language: Английский

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Improvement of a flexible multimode pressure-strain sensor (FMPSS) for blueberry firmness tactile sensing and tamper-evident packaging

Food Control, Journal Year: 2023, Volume and Issue: 156, P. 110129 - 110129

Published: Sept. 29, 2023

Language: Английский

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Large-Scale High-Altitude UAV-Based Vehicle Detection via Pyramid Dual Pooling Attention Path Aggregation Network

IEEE Transactions on Intelligent Transportation Systems, Journal Year: 2024, Volume and Issue: 25(10), P. 14426 - 14444

Published: May 17, 2024

UAVs can collect vehicle data in high-altitude scenes, playing a significant role intelligent urban management due to their wide of view. Nevertheless, the current datasets for UAV-based detection are acquired at altitude below 150 meters. This contrasts with perspective obtained from potentially leading incongruities distribution. Consequently, it is challenging apply these effectively and there an ongoing obstacle. To resolve this challenge, we developed comprehensive dataset named LH-UAV-Vehicle, specifically collected flight altitudes ranging 250 400 Collecting higher offers broader perspective, but concurrently introduces complexity diversity background, which consequently impacts localization recognition accuracy. In response, proposed pyramid dual pooling attention path aggregation network (PDPA-PAN), innovative framework that improves performance scenes by combining spatial semantic information. Object integration both channel dimensions aimed module (PDPAM), achieved through parallel two distinct mechanisms. Furthermore, have individually (PPAM) (DPAM). The PPAM emphasizes attention, while DPAM prioritizes attention. design aims enhance information suppress background interference more effectively. Extensive experiments conducted on LH-UAV-Vehicle conclusively demonstrate efficacy method. Our code be found https://github.com/yikuizhai/PDPA-PAN.

Language: Английский

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Design and Experimentation of a Machine Vision-Based Cucumber Quality Grader

Foods, Journal Year: 2024, Volume and Issue: 13(4), P. 606 - 606

Published: Feb. 16, 2024

The North China type cucumber, characterized by its dense spines and top flowers, is susceptible to damage during the grading process, affecting market value. Moreover, traditional manual methods are time-consuming labor-intensive. To address these issues, this paper proposes a cucumber quality grader based on machine vision deep learning. In electromechanical aspect, novel fixed tray mechanism designed prevent vulnerable cucumbers process. algorithm, new convolutional neural network introduced named MassNet, capable of predicting mass using only top-view image. After obtaining prediction, achieved. Experimental validation includes assessing performance grader, comparing MassNet with different models in mass, evaluating online integrated algorithm. results indicate that achieves maximum capacity 2.3 t/hr. comparison AlexNet, MobileNet, ResNet, demonstrates superior MAPE 3.9% RMSE 6.7 g. experiments, efficiency reaches 93%.

Language: Английский

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Prototyping and evaluation of a novel machine vision system for real-time, automated quality grading of sweetpotatoes

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 219, P. 108826 - 108826

Published: March 12, 2024

Language: Английский

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Application of nondestructive techniques for peach (Prunus persica) quality inspection: A review

Journal of Food Science, Journal Year: 2024, Volume and Issue: 89(11), P. 6863 - 6887

Published: Oct. 4, 2024

Abstract Peaches are highly valued for their rich nutritional content. Traditional fruit quality accessing methods (i.e., manual squeezing the firmness) both subjective and destructive, which tend to diminish integrity of samples, consequently undermining market value. Compared traditional detection methods, nondestructive technology offers efficient noninvasive solutions rapidly accurately assessing internal external peaches. This can significantly enhance product classification assurance while reducing need extensive human resources minimizing potential physical damage review provided a comprehensive overview techniques peach evaluation, including visible/near‐infrared spectroscopy, machine vision technology, hyperspectral imaging, dielectric optical properties, fluorescence electronic nose/tongue, acoustic vibration methods. It also evaluates effectiveness each technique in quality, maturity, disease The advantages limitations method were summarized. study focuses specifically on peaches encompasses all existing testing providing valuable insights references future studies field analysis using

Language: Английский

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Grasping perception and prediction model of kiwifruit firmness based on flexible sensing claw

Computers and Electronics in Agriculture, Journal Year: 2023, Volume and Issue: 215, P. 108389 - 108389

Published: Nov. 14, 2023

Language: Английский

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Non‐destructive grading technique for mangoes using a flexible impedance sensing system and YOLOv5s_CBAM

Journal of Food Process Engineering, Journal Year: 2024, Volume and Issue: 47(5)

Published: May 1, 2024

Abstract Flexible sensors for food quality control are experiencing rapid development. The purpose of this study is to address the time‐consuming issues associated with traditional fruit grading methods by utilizing a homemade flexible impedance sensing system (FISS). A customized spiral slide was innovatively designed in simulate process on assembly line multimodal features low power consumption and non‐destructive evaluation. FISS integrated components such as camera, slider, 3D printed latex ball, electrodes, back‐end measurement circuits successfully achieved accurate assessment using visual classification model primary based YOLOv5s‐CBAM an feature secondary mangoes coefficient variation method threshold method. With impressive accuracy 97.07% speed up 3 s/piece, classified into seven grades covering overripe, fully ripe, unevenly unripe, underripe, rotten states. By eliminating reliance complex instruments expensive equipment, provides cost‐effective alternative control, significantly reducing operational costs. Practical applications

Language: Английский

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