A Review of Bin Filling Technologies for Apple Harvest and Postharvest Handling

Zhang, Zhao; Pothula, Anand Kumar; Lu, Renfu

doi:10.13031/aea.12827

Cited by 22 publications

(8 citation statements)

References 35 publications

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“…Consumer RGBD cameras have a simple and compact structure and can be used for many local tasks, such as the three-dimensional reconstruction of targets at specific locations. RGBD cameras, e.g., Microsoft Kinect V2 (ToF-based) [22][23][24] and Intel Realsense (AIRS-based) [12,25], have been widely used in harvesting robots with the merits of low cost, high measuring resolution of distance, adaptiveness to ambient light, and quick response. The technique of fruits' localization with RGBD cameras has been widely investigated in the field of harvesting robots.…”

Section: D Fruit Localization and Approaching Direction Estimationmentioning

confidence: 99%

Occluded Apple Fruit Detection and Localization with a Frustum-Based Point-Cloud-Processing Approach for Robotic Harvesting

Feng

Qiu

et al. 2022

Remote Sensing

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Precise localization of occluded fruits is crucial and challenging for robotic harvesting in orchards. Occlusions from leaves, branches, and other fruits make the point cloud acquired from Red Green Blue Depth (RGBD) cameras incomplete. Moreover, an insufficient filling rate and noise on depth images of RGBD cameras usually happen in the shade from occlusions, leading to the distortion and fragmentation of the point cloud. These challenges bring difficulties to position locating and size estimation of fruit for robotic harvesting. In this paper, a novel 3D fruit localization method is proposed based on a deep learning segmentation network and a new frustum-based point-cloud-processing method. A one-stage deep learning segmentation network is presented to locate apple fruits on RGB images. With the outputs of masks and 2D bounding boxes, a 3D viewing frustum was constructed to estimate the depth of the fruit center. By the estimation of centroid coordinates, a position and size estimation approach is proposed for partially occluded fruits to determine the approaching pose for robotic grippers. Experiments in orchards were performed, and the results demonstrated the effectiveness of the proposed method. According to 300 testing samples, with the proposed method, the median error and mean error of fruits’ locations can be reduced by 59% and 43%, compared to the conventional method. Furthermore, the approaching direction vectors can be correctly estimated.

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Section: D Fruit Localization and Approaching Direction Estimationmentioning

confidence: 99%

Occluded Apple Fruit Detection and Localization with a Frustum-Based Point-Cloud-Processing Approach for Robotic Harvesting

Feng

Qiu

et al. 2022

Remote Sensing

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“…Once the fruit is detached and delivered using a robotic or mass harvesting system onto a conveyance system, they have to be conveyed, decelerated at the end of the conveyance, and collected onto a container. There have been various successful efforts in this area, details of which can be found in Zhang et al (2018c) and Karkee et al (2017).…”

Section: -13mentioning

confidence: 99%

The use of agricultural robots in orchard management

Zhang¹,

Karkee²,

Tabb³

2019

Burleigh Dodds Series in Agricultural Science

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The use of robotic or automated machines in orchard operations is associated primarily with insufficient labor availability and rapidly increasing labor costs in tree fruit production and is critical for improving yield of high-quality fruit with minimal dependence on seasonal human labor. Primarily, mechanized or robotic orchard management operations (after establishment of the trees) include pruning, thinning, spraying and harvesting.Pruning is an operation to grow fruit trees into a desired shape for improving sunlight penetration into the canopies, supporting more effective (e.g. more human-and machinefriendly) orchard operations, and regulating location and amount of fruiting sites that can lead to improved yield and quality of fruit. Pruning is physically cutting and removing parts of a tree, such as cutting branches back and/or removing small limbs entirely following some guidelines. This operation requires workers have a specific level of knowledge and skill to perform the job well. Pruning is performed both in the winter (also called "dormant pruning") and in the summer. Currently, research and development of robotic pruning is focused primarily on dormant pruning, therefore, we will focus on dormant pruning for the remainder of this chapter.Fruit trees' bloom often includes many more flowers than are required for producing desired yield and quality of a commercial crop. Crop thinning is a practice to manage/control crop load by removing a portion of the bloom and/or young fruit on the tree either selectively or non-selectively to improve the size and quality of the remaining fruit. Thinning is commonly performed during and shortly after the bloom period to reduce the number of set fruit using either chemical thinners or physical means. However, thinning can occur later in the season to remove poor quality fruit or those growing too close together to ensure good quality and size of the remaining ones using only physical means. Currently research and development of robotic thinning technologies is more focused on blossom thinning, and therefore, we will focus on this aspect of thinning in this chapter.Pest control is an essential operation in orchard management to protect the yield and quality of fruit from various types of pests. One solution to achieve effective pest control is to spray recommended pesticides at the correct time and correct rate to the target area. Due to the confined environment in tree fruit orchards, spraying pesticides is inherently hazardous for the operators driving a sprayer. Robotic sprayers are probably the best solution to ensure operator safety and efficiency in pest control.Fruit harvesting for fresh market consumption requires a large seasonal semi-skilled workforce which has created a critical labor-related risk of not having enough workers to perform this time-sensitive task. In addition, harvesting labor is already the most significant

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“…Net present value (NPV), calculated by subtracting the present value of cost from the present value of benefits, is a widely used measurement of the profitability of an investment. Determining the machine NPV helps apple growers with decision making on purchasing harvest platforms [37,[62][63][64][65][66] . Apple growers anticipate the purchase of a harvest platform to lead to a positive NPV (profit) instead of a negative NPV (loss) throughout the machine life span.…”

Section: Net Present Value Analysismentioning

confidence: 99%

Models for economic evaluation of multi-purpose apple harvest platform and software development

Zhang

Wang

et al. 2019

International Journal of Agricultural and Biological Engineering

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An increasing number of U.S. apple growers are now interested in using harvest platforms to increase harvest productivity, expand labor pool, and alleviate tough working conditions. To maximize benefits, functions of thinning, pruning & training, and infield sorting have been or are to be incorporated into harvest platforms. Though growers are most concerned with economic benefits, few cost-benefit studies had been conducted on different platforms. In the meantime, economic analysis procedure is complex and each analysis is for one specific machine (not for general purposes). No software has been developed as a general and ready-to-use tool for growers and researchers for the platform economic analysis. In this study, platforms, both available on the marketplace and developed in lab as pilot trials, were reviewed. Costs and benefits models were then established, based on which multipurpose apple harvest platform economic evaluation software (iMPAHP) was developed (capable of evaluating a wide variety of apple harvest platforms). A case study (machine cost of $100 000, accommodating 6 workers, processing apple incidence of 10% with 90% sort-out rate, and harvest, thinning, and pruning & training productivity increase by 40%, 50%, and 60%, respectively) based on iMPAHP demonstrated that infield sorting, harvest, thinning, and pruning and training accounted for 48.4%, 23.9%, 14.3%, and 13.4% of the total benefits, respectively. In the case that the platform was in all-four-purpose-application, the net present value (NPV) analysis of a 10-year investment showed a positive return of $60 547. However, without infield sorting function, the NPV resulted in a negative value, indicating a loss for the machine investment. Though incorporating the modular infield sorting system certainly increased the overall machine investment by $30 000, the benefits outweighed the costs.

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A Review of Bin Filling Technologies for Apple Harvest and Postharvest Handling

Cited by 22 publications

References 35 publications

Occluded Apple Fruit Detection and Localization with a Frustum-Based Point-Cloud-Processing Approach for Robotic Harvesting

Occluded Apple Fruit Detection and Localization with a Frustum-Based Point-Cloud-Processing Approach for Robotic Harvesting

The use of agricultural robots in orchard management

Models for economic evaluation of multi-purpose apple harvest platform and software development

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