A review of three-dimensional vision techniques in food and agriculture applications

Xiang, Lirong; Wang, Dongyi

doi:10.1016/j.atech.2023.100259

Cited by 8 publications

(4 citation statements)

References 78 publications

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“…It is essential to place the results within the broader context of current research. A recent study by Xiang and Wang [ 24 ] provided a comparative analysis of depth imaging techniques in food and agriculture applications assessing depth imaging approaches, use cases, advantages, disadvantages, and price ranges. Besides high-depth reconstruction performance, the dual line laser approach presents a cost-effective implementation and a robust calibration process.…”

Section: Discussionmentioning

confidence: 99%

Active Dual Line-Laser Scanning for Depth Imaging of Piled Agricultural Commodities for Itemized Processing Lines

Ali,

Wang,

Tao

2024

Sensors

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The accurate depth imaging of piled products provides essential perception for the automated selection of individual objects that require itemized food processing, such as fish, crabs, or fruit. Traditional depth imaging techniques, such as Time-of-Flight and stereoscopy, lack the necessary depth resolution for imaging small items, such as food commodities. Although structured light methods such as laser triangulation have high depth resolution, they depend on conveyor motion for depth scanning. This manuscript introduces an active dual line-laser scanning system for depth imaging static piled items, such as a pile of crabs on a table, eliminating the need for conveyor motion to generate high-resolution 3D images. This advancement benefits robotic perception for loading individual items from a pile for itemized food processing. Leveraging a unique geometrical configuration and laser redundancy, the dual-laser strategy overcomes occlusions while reconstructing a large field of view (FOV) from a long working distance. We achieved a depth reconstruction MSE of 0.3 mm and an STD of 0.5 mm on a symmetrical pyramid stage. The proposed system demonstrates that laser scanners can produce depth maps of complex items, such as piled Chesapeake Blue Crab and White Button mushrooms. This technology enables 3D perception for automated processing lines and offers broad applicability for quality inspection, sorting, and handling of piled products.

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Section: Discussionmentioning

confidence: 99%

Active Dual Line-Laser Scanning for Depth Imaging of Piled Agricultural Commodities for Itemized Processing Lines

Ali,

Wang,

Tao

2024

Sensors

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“…3-D modeling in computer vision is fundamentally about stereo correspondence and 3-D reconstruction [69], [70]. Stereo correspondence generates a 3-D model from multiple images of the same object or scene by finding matching pixels across these images and mapping their 2-D positions to 3-D. Techniques such as epipolar geometry, sparse correspondence, and dense correspondence are commonly used [71].…”

Section: ) 3-d Modelingmentioning

confidence: 99%

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

Akbar,

Kamarulzaman,

Muzahid

et al. 2024

IEEE Access

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With the escalating global challenges of food security and resource sustainability, innovative solutions like deep learning and computer vision are transforming agricultural practices by enabling datadriven decision-making. This paper provides a focused review of recent advancements in deep learningenabled computer vision techniques tailored specifically for greenhouse environments. First, deep learning and computer vision fundamentals are briefly introduced. Over 100 studies from 2020 to date are then comprehensively reviewed in which these technologies were applied within greenhouses for growth monitoring, disease detection, yield estimation, and other tasks. The techniques, datasets, models, and overall performance results reported in the literature are analyzed. Tables and figures showcase real-world implementations and results synthesized from current research. Key challenges are also outlined related to aspects like model adaptability, lack of sufficient labeled greenhouse data, computational constraints, the need for multi-modal sensor fusion, and other areas needing further investigation. Future trends and prospects are discussed to provide guidance for researchers exploring computer vision in the niche greenhouse domain. By condensing prior work and elucidating the state-of-the-art, this timely review aims to promote continued progress in smart greenhouse agriculture. The focused analysis, specifically on greenhouse environments, fills a gap compared to previous agricultural surveys. Overall, this paper highlights the immense potential of computer vision and deep learning in driving the emergence of data-driven, smart greenhouse farming worldwide.

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“…Machine vision technology mainly focuses on 3D point cloud and 2D planar imaging [10,11]. A 3D point cloud obtains the coordinates of targets through high-precision equipment, such as depth cameras and lidar, which have the advantage of being free from perspective distortions [12][13][14]. Currently, studies on the measurement of the phenotypic parameters of lettuces mainly focus on the volume [15], height, diameter, and leaf area [16] of a single lettuce, and there are few studies on those of multiple lettuces.…”

Section: Introductionmentioning

confidence: 99%

A Visual Method of Hydroponic Lettuces Height and Leaves Expansion Size Measurement for Intelligent Harvesting

et al. 2023

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Harvesting is an important procedure for hydroponic lettuces in plant factories. At present, hydroponic lettuces are mainly harvested manually, and the key difficulty in mechanical harvesting is reducing the occurrence of leaf injury. Measuring the size of hydroponic lettuces using the image processing method and intelligently adjusting the operating parameters of the harvesting device are the foundation of high-quality harvesting for lettuces. The overlapped leaves of adjacent hydroponic lettuces cause difficulties in measuring lettuce size, especially the leaves expansion size. Therefore, we proposed an image processing method for measuring lettuce height and leaves expansion size according to the upper contour feature of lettuces and an image included three lettuces. Firstly, the upper contours of the lettuces were extracted and segmented via image preprocessing. Secondly, lettuce height was measured according to the maximum ordinate of the contour. Lastly, the lettuce’s upper contour was fitted to a function to measure the leaves expansion size. The measurement results showed that the maximal relative error of the lettuce height measurements was 5.58%, and the average was 2.14%. The effect of the quadratic function in fitting the upper contour was the best compared with the cubic function and sine function. The maximal relative error of the leaves expansion size measurements was 8.59%, and the average was 4.03%. According to the results of the lettuce height and leaves expansion size measurements, the grabbing parameters of each lettuce were intelligently adjusted to verify the harvesting effect. The harvesting success rates of lettuces was above 90%, and the injured leaves areas of the left, middle, and right lettuces in each image were 192.6 mm2, 228.1 mm2, and 205.6 mm2, respectively. This paper provides a reference for the design and improvement of intelligent harvesters for hydroponic lettuces.

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A review of three-dimensional vision techniques in food and agriculture applications

Cited by 8 publications

References 78 publications

Active Dual Line-Laser Scanning for Depth Imaging of Piled Agricultural Commodities for Itemized Processing Lines

Active Dual Line-Laser Scanning for Depth Imaging of Piled Agricultural Commodities for Itemized Processing Lines

A Comprehensive Review on Deep Learning Assisted Computer Vision Techniques for Smart Greenhouse Agriculture

A Visual Method of Hydroponic Lettuces Height and Leaves Expansion Size Measurement for Intelligent Harvesting

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