Application status and challenges of machine vision in plant factory—A review

Tian, Zhiwei; Ma, Wei; Yang, Qichang; Duan, Famin

doi:10.1016/j.inpa.2021.06.003

Cited by 30 publications

(17 citation statements)

References 40 publications

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“…With the development of information technology, mathematical models have been developed to recognize patterns autonomously with well-defined hierarchical structure and learning methods. 97,98 Deep learning also called Deeper Neural Networks is the type of Artificial Neural Network (ANN) broadly used for autonomous learning and feature extraction. Deep learning uses feature learning with complex models; it combines high level features with low level features in automatic feature extraction.…”

Section: Fruit Feature Extraction Using Deep Learning Approachesmentioning

confidence: 99%

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Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Kumar

Mohan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Progressive application of multidisciplinary research and development pushes the evolution of automation in many subsectors of agriculture to increase productivity, economic growth and environmental preservation with the help of robotics and artificial intelligence. Fruit harvesting robots have been developed mainly to provide support in the field for limited labour resources, to enable selective harvesting, to improve the efficiency and to preserve the quality of fruits. Even a small delay in harvesting can cause a maximum impact to the quality of the fruit. Selective fruit harvesting is an integration of different subcomponents. This paper provides a brief analysis of the techniques in selective fruit harvesting for the past 6 years starting from 2017 to 2022, associated principles, limitations and directions for future challenges. The first subcomponent is the vision system, it captures the information about the fruit in a tree canopy to perform efficient 2D and 3D localization. Hence getting accurate information from the vision system is more essential even in a complex agricultural environment. The detection of fruit from the vision system is normally done with two major methods such as traditional image processing and deep learning approaches. The result shows that the traditional methods provide high efficiency but the colour similarity, complex backgrounds and lightning conditions often makes failure in detection. Shortage of standard dataset and high-powered processing devices hinders the development of deep learning algorithms. Also, the usage of large data sets reduces the training speed even in pre-trained networks. For fruit grasping and detachment, the detection of plucking point is more essential to preserve the quality and for further storage. The elaborate description about the framework, limitations in current sensing, fruit and picking point detection algorithms provides guidelines to the researchers in building a fully automated robotic system to increase the processing speed and production rate.

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Section: Fruit Feature Extraction Using Deep Learning Approachesmentioning

confidence: 99%

Section: Fruit Feature Extraction Using Deep Learning Approachesmentioning

confidence: 99%

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Kumar

Mohan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Machine vision (Melvyn et al, 2021) is part of artificial intelligence and utilizes imaging equipment to photograph and record inspection objects, which are processed and analyzed at the terminal (Tian et al, 2021). In practice, it has a huge impact on the detection of product merit and damage to parts.…”

Section: Introductionmentioning

confidence: 99%

Machine visual technology based steel corrosion evaluation and analysis of power transformation lines: A Zhejiang power grid case study

Liu

Zhang

et al. 2023

Front. Energy Res.

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It can be seen that the corrosion failure of transmission and transformation equipment has increasingly restricted the safe operation of the Zhejiang power grid bottleneck problem. If effective anticorrosion measures are not taken promptly, transmission and transformation equipment serving in various sophisticated atmospheric environments will suffer serious corrosion damage in a relatively short period, which endangers the safe usage of transmission and transformation equipment and the security of grid operation. In this article, through the establishment of transmission and transformation steel components corrosion fracture mechanics model, a standard corrosion spectrum grading software based on DeepLabV3+ image segmentation technology is developed to determine the quantitative assessment method of corrosion damage and assess the corrosion status with safety degree of transmission and transformation equipment. According to the assessment results, the operation and maintenance units are guided to adopt differentiated corrosion maintenance and replacement strategies, so as to reduce corrosion safety hazards and reduce safety accidents and economic losses caused by corrosion, which is of great significance for the safe operation of power grids.

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“…Plant phenotyping is a set of methodologies and protocols used to study plant performance, growth, architecture, and composition at different scales of organization, from organs to canopies, and it elucidates the plant trait dynamics in a non-destructive and non-invasive manner [5,14,[19][20][21]. Having great potential for application to commercial PFALs [22], imagebased studies in PFALs have been conducted, including quantifying the projected leaf area (value measured by 2D camera images captured from above the plant canopy or seedling trays) of the plant canopy [15,[23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Variations in the Growth of Cotyledons and Initial True Leaves as Affected by Photosynthetic Photon Flux Density at Individual Seedlings and Nutrients

Amagai

Kozai²,

Maruo³

et al. 2022

Agronomy

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Plant factories with artificial lighting (PFALs), with well-insulated and airtight structures, enable the production of large quantities of high-quality plants year-round while achieving high resource use efficiency. However, despite the controlled environment in PFALs, variations in plant individuals have been found, which affect productivity in PFAL operations. Plant phenotyping plays a crucial role in understanding how the surrounding microenvironment affects variations in plant phenotypes. In the current study, a modular phenotyping system for seedling production was developed, focusing on practicality and scalability in commercial PFALs. Experiments on seedlings, which strongly affect productivity, were conducted to obtain cotyledon unfolding time and the time series projected area of cotyledons and true leaves of individual seedlings of romaine lettuce (Lactuca sativa L. var. longifolia), using RGB images. This was also undertaken to analyze how the surrounding microenvironment of photosynthetic photon flux densities and nutrients affect growth variations for plant cohort research. In agreement with the actual measurements, variations in seedling growth were identified even under similar microenvironments. Furthermore, the results demonstrated larger variations in seedlings with higher relative growth. Aiming for simplified interactions of phenotypes with the microenvironment, management, and genotype, seedling selection and breeding with plant production in PFALs may enable plant uniformity and higher productivity.

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Application status and challenges of machine vision in plant factory—A review

Cited by 30 publications

References 40 publications

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Machine visual technology based steel corrosion evaluation and analysis of power transformation lines: A Zhejiang power grid case study

Variations in the Growth of Cotyledons and Initial True Leaves as Affected by Photosynthetic Photon Flux Density at Individual Seedlings and Nutrients

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