Volumetric estimation using 3D reconstruction method for grading of fruits

Jadhav, Tushar; Singh, Kulbir; Abhyankar, Aditya

doi:10.1007/s11042-018-6271-3

Cited by 33 publications

(12 citation statements)

References 27 publications

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“…For example, Wang et al [10] developed automatic fruit shape measurement based on 3D reconstruction, using a Kinect sensor and an electric turntable. Similarly, Jadhav et al [11] investigated the volumetric 3D reconstruction of different fruits based on voxel mapping using silhouette probabilities and inter-image homographies, with multiple RGB cameras and a checkerboard. Complete reconstruction approaches are generally suitable for ground truth dataset generation or volumetric estimation in laboratory conditions.…”

Section: Related Workmentioning

confidence: 99%

Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

Marangoz¹,

Zaenker²,

Menon³

et al. 2022

Preprint

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Autonomous crop monitoring is a difficult task due to the complex structure of plants. Occlusions from leaves can make it impossible to obtain complete views about all fruits of, e.g., pepper plants. Therefore, accurately estimating the shape and volume of fruits from partial information is crucial to enable further advanced automation tasks such as yield estimation and automated fruit picking. In this paper, we present an approach for mapping fruits on plants and estimating their shape by matching superellipsoids. Our system segments fruits in images and uses their masks to generate point clouds of the fruits. To combine sequences of acquired point clouds, we utilize a real-time 3D mapping framework and build up a fruit map based on truncated signed distance fields. We cluster fruits from this map and use optimized superellipsoids for matching to obtain accurate shape estimates.In our experiments, we show in various simulated scenarios with a robotic arm equipped with an RGB-D camera that our approach can accurately estimate fruit volumes. Additionally, we provide qualitative results of estimated fruit shapes from data recorded in a commercial glasshouse environment.

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Section: Related Workmentioning

confidence: 99%

Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

Marangoz¹,

Zaenker²,

Menon³

et al. 2022

Preprint

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“…3D reconstruction of plant matter is important in solving a number of core tasks including growth measurement and yield estimation such as seen in work by Moonrinta et al [13]. Jadhav et al also use 3D reconstruction to help with the grading of fruit, with emphasis put on the importance of accurate reconstruction of arbitrary shapes [14]. Similarly, 3D reconstruction has been used to map the geometry of plant shoots, another common phenotyping task [15].…”

Section: Plant Phenotypingmentioning

confidence: 99%

GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

et al. 2021

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3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs. In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain, and training is supplemented with different unlabelled datasets from the target real domain. We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment (Blender). Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss, improving performance of 3D reconstruction on real images. Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images. We focus this work on the task of 3D banana reconstruction from a single image, representing a common task in plant phenotyping, but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.

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“…Baigvand et al [11] applied an image processing algorithm to develop the machine vision for grading figs. According to the volume and maturity feature, Jadhav et al [12] proposed a fruit grading system to reconstruct fruit volume. Deplomo et al [13] employed image processing methods to classify the size, color, and texture of onions.…”

Section: Literature Reviewmentioning

confidence: 99%

Pricing and Ordering Strategies for Fresh Food Based on Quality Grading

Xie

Zhou

et al. 2021

Journal of Food Quality

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Fresh food quality grading is the basis of fresh food marketization. On the one hand, it can effectively improve the market efficiency of fresh food and improve the earnings of retailers. On the other hand, it can alleviate the incompleteness of market information and help consumers better identify the quality characteristics of fresh food. To address the issue of quality grading for fresh food, this study constructs a retailer profit model for selling fresh food with two quality grades. Considering the quality level distribution of fresh food and based on the quality selection model, the retailer’s optimal grading, pricing, and ordering of fresh food are studied. Through numerical simulation and sensitivity analysis, some conclusions with managerial implications are drawn. We find that the retailer has the optimal quality grading strategy for fresh food, which is influenced by the minimum quality level and the unit cost of fresh food. Raising the quality standard at the lowest level or reducing the unit cost can help the retailer increase the profits.

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Volumetric estimation using 3D reconstruction method for grading of fruits

Cited by 33 publications

References 27 publications

Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

Fruit Mapping with Shape Completion for Autonomous Crop Monitoring

GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit

Pricing and Ordering Strategies for Fresh Food Based on Quality Grading

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