A Novel Benchmark RGBD Dataset for Dormant Apple Trees and Its Application to Automatic Pruning

Akbar, Shayan A.; Chattopadhyay, Somrita; Elfiky, Noha M.; Kak, Avinash C.

doi:10.1109/cvprw.2016.50

Cited by 14 publications

(13 citation statements)

References 29 publications

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“…During the recent years, deep learning based methods have made remarkable progress in many fileds [24], such as Internet of Things [25,26], Signal processing [27,28], UAV [29], wireless communications [30], and especially in the field of agriculture [31][32][33][34][35]. These include fruit classification [36][37][38], yield estimation and counting [39,40].…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

Zhang¹,

Ren²,

Tao³

et al. 2021

KSII TIIS

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Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.

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Section: Deep Learning Based Methodsmentioning

confidence: 99%

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

Zhang¹,

Ren²,

Tao³

et al. 2021

KSII TIIS

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“…In Akbar et al (2016a), one Kinect depth image was used to estimate branch diameters. A dataset containing Kinect data from apple trees was described in Akbar et al (2016b). Medeiros et al (2017) used a laser detection and ranging sensor, or lidar, for estimating tree shape for a pruning application.…”

Section: Robotic Pruningmentioning

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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“…The results showed that the algorithm removed 85% of long branches, and 69% of overlapping branches. A few other studies have also been conducted on tree modeling using different vision sensing for automatic pruning, such as Kinect 2 [68], RGBD [71], depth image [72], and time-of-flight data [67]. Tabb and her collaborator focused on developing a 3D reconstruction of fruit trees ( Figure 6, reproduced from [69]) for automatic pruning with identifying the branch parameters such as length, diameter, angle, etc.…”

Section: Branch Detection/reconstruction For Automated Pruningmentioning

confidence: 99%

Sensing and Automation in Pruning of Apple Trees: A Review

Schupp

2018

Agronomy

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Pruning is one of the most important tree fruit production activities, which is highly dependent on human labor. Skilled labor is in short supply, and the increasing cost of labor is becoming a big issue for the tree fruit industry. Meanwhile, worker safety is another issue in the manual pruning. Growers are motivated to seek mechanical or robotic solutions for reducing the amount of hand labor required for pruning. Identifying tree branches/canopies with sensors as well as automated operating pruning activity are the important components in the automated pruning system. This paper reviews the research and development of sensing and automated systems for branch pruning in apple production. Tree training systems, pruning strategies, 3D structure reconstruction of tree branches, and practice mechanisms or robotics are some of the developments that need to be addressed for an effective tree branch pruning system. Our study summarizes the potential opportunities for automatic pruning with machine-friendly modern tree architectures, previous studies on sensor development, and efforts to develop and deploy mechanical/robotic systems for automated branch pruning. We also describe two examples of qualified pruning strategies that could potentially simplify the automated pruning decision and pruning end-effector design. Finally, the limitations of current pruning technologies and other challenges for automated branch pruning are described, and possible solutions are discussed.

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A Novel Benchmark RGBD Dataset for Dormant Apple Trees and Its Application to Automatic Pruning

Cited by 14 publications

References 29 publications

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

The use of agricultural robots in orchard management

Sensing and Automation in Pruning of Apple Trees: A Review

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