Mechanized technologies for scaffolding cultivation in the kiwifruit industry: A review

Mu, Longtao; Liu, Haozhou; Cui, Yongjie; Fu, Longsheng; Gejima, Yoshinori

doi:10.1016/j.inpa.2018.07.005

Cited by 14 publications

(7 citation statements)

References 11 publications

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“…Wires run on the top of cross arms connecting them in the middle and on both edges of the cross arms. The upper stems of the kiwifruit are tied to the top wires to keep the egg-sized kiwifruits hanging downward, which makes them visible and accessible for manual picking [5]. This canopy structure also provides relatively simpler and structured workspace for mechanized or automated field operations such as robotic picking [3], [6], compared to other fruit trees such as apples [7].…”

Section: Introductionmentioning

confidence: 99%

Improved Kiwifruit Detection Using Pre-Trained VGG16 With RGB and NIR Information Fusion

Liu

et al. 2020

IEEE Access

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140

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This study presents a novel method to apply the RGB-D (Red Green Blue-Depth) sensors and fuse aligned RGB and NIR images with deep convolutional neural networks (CNN) for fruit detection. It aims to build a more accurate, faster, and more reliable fruit detection system, which is a vital element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). A common Faster R-CNN network VGG16 was adopted through transfer learning, for the task of kiwifruit detection using imagery obtained from two modalities: RGB (red, green, blue) and Near-Infrared (NIR) images. Kinect v2 was used to take a bottom view of the kiwifruit canopy's NIR and RGB images. The NIR (1 channel) and RGB images (3 channels) were aligned and arranged side by side into a 6-channel image. The input layer of the VGG16 was modified to receive the 6-channel image. Two different fusion methods were used to extract features: Image-Fusion (fusion of the RGB and NIR images on input layer) and Feature-Fusion (fusion of feature maps of two VGG16 networks where the RGB and NIR images were input respectively). The improved networks were trained end-to-end using back-propagation and stochastic gradient descent techniques and compared to original VGG16 networks with RGB and NIR image input only. Results showed that the average precision (APs) of the original VGG16 with RGB and NIR image input only were 88.4% and 89.2% respectively, the 6-channel VGG16 using the Feature-Fusion method reached 90.5%, while that using the Image-Fusion method reached the highest AP of 90.7% and the fastest detection speed of 0.134 s/image. The results indicated that the proposed kiwifruit detection approach shows a potential for better fruit detection.

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Section: Introductionmentioning

confidence: 99%

Improved Kiwifruit Detection Using Pre-Trained VGG16 With RGB and NIR Information Fusion

Liu

et al. 2020

IEEE Access

Self Cite

140

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“…At the beginning demonstrating stage, numerous farmers are exploring incorporating smart technology and methods that enhance the effectiveness of agricultural processes ( Verdouw et al, 2021 ). Agricultural mechanisation can save labour time and other resources, enhance labour efficiency, and lower production costs ( Mu et al, 2018 ). In addition, the proposed technique can be implemented in the existing national standard for agricultural machinery maintenance as an efficient method to ensure PdM with DT in accordance with the standard.…”

Section: Discussionmentioning

confidence: 99%

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

Abd Wahab,

Hasikin,

Wee Lai

et al. 2024

PeerJ Computer Science

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Background Maintaining machines effectively continues to be a challenge for industrial organisations, which frequently employ reactive or premeditated methods. Recent research has begun to shift its attention towards the application of Predictive Maintenance (PdM) and Digital Twins (DT) principles in order to improve maintenance processes. PdM technologies have the capacity to significantly improve profitability, safety, and sustainability in various industries. Significantly, precise equipment estimation, enabled by robust supervised learning techniques, is critical to the efficacy of PdM in conjunction with DT development. This study underscores the application of PdM and DT, exploring its transformative potential across domains demanding real-time monitoring. Specifically, it delves into emerging fields in healthcare, utilities (smart water management), and agriculture (smart farm), aligning with the latest research frontiers in these areas. Methodology Employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) criteria, this study highlights diverse modeling techniques shaping asset lifetime evaluation within the PdM context from 34 scholarly articles. Results The study revealed four important findings: various PdM and DT modelling techniques, their diverse approaches, predictive outcomes, and implementation of maintenance management. These findings align with the ongoing exploration of emerging applications in healthcare, utilities (smart water management), and agriculture (smart farm). In addition, it sheds light on the critical functions of PdM and DT, emphasising their extraordinary ability to drive revolutionary change in dynamic industrial challenges. The results highlight these methodologies’ flexibility and application across many industries, providing vital insights into their potential to revolutionise asset management and maintenance practice for real-time monitoring. Conclusions Therefore, this systematic review provides a current and essential resource for academics, practitioners, and policymakers to refine PdM strategies and expand the applicability of DT in diverse industrial sectors.

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“…Table 2 summarises the information found in the literature on handheld devices developed for pollination. Most of the handheld devices developed for artificial pollination use dry or wet application methods [21][22][23][24][25][26][27][28][29][30]. Portable devices use tools, such as air pressure, sprayers, and equivalent tools to spread the pollen.…”

Section: Advancing Artificial Pollination With Digital Farming Techno...mentioning

confidence: 99%

Robotic Pollinating Tools for Actinidia Crops

Pinheiro,

Santos,

Valente

et al. 2023

The 3rd International Electronic Conference on Agronomy

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Mechanized technologies for scaffolding cultivation in the kiwifruit industry: A review

Cited by 14 publications

References 11 publications

Improved Kiwifruit Detection Using Pre-Trained VGG16 With RGB and NIR Information Fusion

Improved Kiwifruit Detection Using Pre-Trained VGG16 With RGB and NIR Information Fusion

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

Robotic Pollinating Tools for Actinidia Crops

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