Multiple camera fruit localization using a particle filter

Mehta, Siddhartha S.; Ton, Chau; Asundi, Sharanabasaweshwara; Burks, Thomas F.

doi:10.1016/j.compag.2017.08.007

Cited by 26 publications

(12 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kusumam et al used a low-cost RGB-D sensor under real-world conditions to address the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the robotic vehicle [ 13 ]. Mehta et al presented an estimation-based localization approach based on a new sensing procedure that uses multiple (≥2) inexpensive monocular cameras to estimate the unknown position of the fruits [ 14 ]. Rakun et al described a method for apple fruit detection that relied on the combination of the object’s color, texture and 3D properties.…”

Section: Introductionmentioning

confidence: 99%

Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination

Xiong

Liu

Lin

et al. 2018

Sensors

View full text Add to dashboard Cite

Night-time fruit-picking technology is important to picking robots. This paper proposes a method of night-time detection and picking-point positioning for green grape-picking robots to solve the difficult problem of green grape detection and picking in night-time conditions with artificial lighting systems. Taking a representative green grape named Centennial Seedless as the research object, daytime and night-time grape images were captured by a custom-designed visual system. Detection was conducted employing the following steps: (1) The RGB (red, green and blue). Color model was determined for night-time green grape detection through analysis of color features of grape images under daytime natural light and night-time artificial lighting. The R component of the RGB color model was rotated and the image resolution was compressed; (2) The improved Chan–Vese (C–V) level set model and morphological processing method were used to remove the background of the image, leaving out the grape fruit; (3) Based on the character of grape vertical suspension, combining the principle of the minimum circumscribed rectangle of fruit and the Hough straight line detection method, straight-line fitting for the fruit stem was conducted and the picking point was calculated using the stem with an angle of fitting line and vertical line less than 15°. The visual detection experiment results showed that the accuracy of grape fruit detection was 91.67% and the average running time of the proposed algorithm was 0.46 s. The picking-point calculation experiment results showed that the highest accuracy for the picking-point calculation was 92.5%, while the lowest was 80%. The results demonstrate that the proposed method of night-time green grape detection and picking-point calculation can provide technical support to the grape-picking robots.

show abstract

Section: Introductionmentioning

confidence: 99%

Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination

Xiong

Liu

Lin

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Dorj et al (2017) developed a method to detect and segment the citrus fruits using a watershed algorithm after converting the RGB images into an HSV color space and obtained a squared correlation coefficient R 2 value of 0.93. Some works (Stajnko and Cmelik, 2005;Malik et al, 2016;Mehta et al, 2017) adapted the size as a criterion to identify the object boundary. Even though the results are promising, these methods do not work in challenging situations such as occlusion, overlapping, and illumination variations.…”

Section: Fruit Yield Estimation Using ML Techniquesmentioning

confidence: 99%

Intelligent Fruit Yield Estimation for Orchards Using Deep Learning Based Semantic Segmentation Techniques—A Review

et al. 2021

View full text Add to dashboard Cite

Smart farming employs intelligent systems for every domain of agriculture to obtain sustainable economic growth with the available resources using advanced technologies. Deep Learning (DL) is a sophisticated artificial neural network architecture that provides state-of-the-art results in smart farming applications. One of the main tasks in this domain is yield estimation. Manual yield estimation undergoes many hurdles such as labor-intensive, time-consuming, imprecise results, etc. These issues motivate the development of an intelligent fruit yield estimation system that offers more benefits to the farmers in deciding harvesting, marketing, etc. Semantic segmentation combined with DL adds promising results in fruit detection and localization by performing pixel-based prediction. This paper reviews the different literature employing various techniques for fruit yield estimation using DL-based semantic segmentation architectures. It also discusses the challenging issues that occur during intelligent fruit yield estimation such as sampling, collection, annotation and data augmentation, fruit detection, and counting. Results show that the fruit yield estimation employing DL-based semantic segmentation techniques yields better performance than earlier techniques because of human cognition incorporated into the architecture. Future directions like customization of DL architecture for smart-phone applications to predict the yield, development of more comprehensive model encompassing challenging situations like occlusion, overlapping and illumination variation, etc., were also discussed.

show abstract

“…Target tracking and information fusion over multiple frames is rarely implemented in harvesting system designs. In work by Mehta, Ton, Asundi, and Burks (2017) multiple monocular cameras are used with a particle filtering framework to localize fruit in three‐dimensional (3D). Spring‐mass motion models are developed, though only validated in simulation.…”

Section: Related Workmentioning

confidence: 99%

Design and evaluation of a modular robotic plum harvesting system utilizing soft components

Brown

Sukkarieh

2020

Journal of Field Robotics

View full text Add to dashboard Cite

The human labor required for tree crop harvesting is a major cost component in fruit production and is increasing. To address this, many existing research works have sought to demonstrate commercially viable robotic harvesting for tree crops, though successful commercial products resulting from these have been few and far between. Systems developed for specific crops such as sweet peppers or apples have shown promise, but the vast majority of cultivar types remain unaddressed, and developing a specific system for each one is inefficient. In this study, an easily modifiable development platform for robotic fruit harvesting is presented, this can be used to test specific design choices on different fruit and growing conditions. The system is evaluated in a commercial plum orchard, with no crop modifications. Both a hard and soft gripper are trialed, along with three object detector approaches and two picking motions. Some existing techniques are found to be counterproductive for plums, while soft robotics and persistent target tracking significantly improve performance. The best harvest success rate of 42%, was observed when using the soft gripper with complex motion. This is lower than expected based on prior testing with apples and indicates the difficulty in moving to new fruit types. Unique challenges specific to the plum type and growing style are examined in the context of system module design choices.

show abstract

Multiple camera fruit localization using a particle filter

Cited by 26 publications

References 36 publications

Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination

Green Grape Detection and Picking-Point Calculation in a Night-Time Natural Environment Using a Charge-Coupled Device (CCD) Vision Sensor with Artificial Illumination

Intelligent Fruit Yield Estimation for Orchards Using Deep Learning Based Semantic Segmentation Techniques—A Review

Design and evaluation of a modular robotic plum harvesting system utilizing soft components

Contact Info

Product

Resources

About