Comparing RGB-D Sensors for Close Range Outdoor Agricultural Phenotyping

Vit, Adar; Shani, Guy

doi:10.3390/s18124413

Cited by 84 publications

(66 citation statements)

References 40 publications

(50 reference statements)

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“…A final, common class of peripherals aboard UAS in agricultural-related applications are depth sensors. The RGB-D sensors provide a relatively cheap and effective way of generating depth data by capturing an extra value in each RGB pixel-a value indicating distance from the sensor to that point in the image [90]. It is true that standard RGB imaging can predict depth values; however, Wang and Li demonstrated how RGB-D cameras are able to provide greater accuracy in volume estimation than standard RGB imaging in 2014 [91].…”

Section: Depth Sensorsmentioning

confidence: 99%

“…Another popular type of depth sensor uses LiDAR technology. In contrast to RGB-D sensors which primarily rely on light reflection intensities [90], LiDAR sensors use laser pulses to map distances [75]. Common reasons to incorporate depth sensors into a UAS for imaging purposes include: altitude monitoring during spraying [92]; phenotyping [90]; and 3D modeling [93,94].…”

Section: Depth Sensorsmentioning

confidence: 99%

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Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

2019

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Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.

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Section: Depth Sensorsmentioning

confidence: 99%

Section: Depth Sensorsmentioning

confidence: 99%

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

2019

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“…This demands the robotic vision system to accurately and robustly extract the geometry and semantic information from the working scene in the orchard environment [5]. Recently, with the advancements of the depth camera technologies, the harvesting robotic vision system is able to model and present the working scene in the three-dimensional form [6]. However, it is still challenging to robustly and accurately perform semantic processing of the visual data in the orchard environment, such as detection and segmentation of the fruit and branch, due to various factors such as illumination variance, occlusion, and variations of object appearance.…”

Section: Introductionmentioning

confidence: 99%

Fruit Detection and Segmentation for Apple Harvesting Using Visual Sensor in Orchards

Kang

Chen

2019

Sensors

106

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Autonomous harvesting shows a promising prospect in the future development of the agriculture industry, while the vision system is one of the most challenging components in the autonomous harvesting technologies. This work proposes a multi-function network to perform the real-time detection and semantic segmentation of apples and branches in orchard environments by using the visual sensor. The developed detection and segmentation network utilises the atrous spatial pyramid pooling and the gate feature pyramid network to enhance feature extraction ability of the network. To improve the real-time computation performance of the network model, a lightweight backbone network based on the residual network architecture is developed. From the experimental results, the detection and segmentation network with ResNet-101 backbone outperformed on the detection and segmentation tasks, achieving an F1 score of 0.832 on the detection of apples and 87.6% and 77.2% on the semantic segmentation of apples and branches, respectively. The network model with lightweight backbone showed the best computation efficiency in the results. It achieved an F1 score of 0.827 on the detection of apples and 86.5% and 75.7% on the segmentation of apples and branches, respectively. The weights size and computation time of the network model with lightweight backbone were 12.8 M and 32 ms, respectively. The experimental results show that the detection and segmentation network can effectively perform the real-time detection and segmentation of apples and branches in orchards.

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“…It integrates the same IMU as the ZR300, but uses the vision sensors of the D435, such that we can take advantage of D435’s excellent range (0.2–10 m+), combined with the time-synchronized accelerometer and gyroscope measurements. Another advantage of the Intel RealSense sensor series is, that they also work outdoors in sunny conditions, due to their stereo-infrared camera approach [24,27,28], but also project an infrared pattern to be able to detect features-less surfaces.…”

Section: Introductionmentioning

confidence: 99%

RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots

Shan

Schwertfeger

2019

Sensors

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Using camera sensors for ground robot Simultaneous Localization and Mapping (SLAM) has many benefits over laser-based approaches, such as the low cost and higher robustness. RGBD sensors promise the best of both worlds: dense data from cameras with depth information. This paper proposes to fuse RGBD and IMU data for a visual SLAM system, called VINS-RGBD, that is built upon the open source VINS-Mono software. The paper analyses the VINS approach and highlights the observability problems. Then, we extend the VINS-Mono system to make use of the depth data during the initialization process as well as during the VIO (Visual Inertial Odometry) phase. Furthermore, we integrate a mapping system based on subsampled depth data and octree filtering to achieve real-time mapping, including loop closing. We provide the software as well as datasets for evaluation. Our extensive experiments are performed with hand-held, wheeled and tracked robots in different environments. We show that ORB-SLAM2 fails for our application and see that our VINS-RGBD approach is superior to VINS-Mono.

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Comparing RGB-D Sensors for Close Range Outdoor Agricultural Phenotyping

Cited by 84 publications

References 40 publications

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Fruit Detection and Segmentation for Apple Harvesting Using Visual Sensor in Orchards

RGBD-Inertial Trajectory Estimation and Mapping for Ground Robots

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