Seedling-lump integrated non-destructive monitoring for automatic transplanting with Intel RealSense depth camera

Syed, Tabinda Naz; Liu, Jizhan; Zhou, Xin; Zhao, Shengyi; Yan, Yuan; Mohamed, Sami Hassan Ahmed; Lakhiar, Imran Ali

doi:10.1016/j.aiia.2019.09.001

Cited by 21 publications

(15 citation statements)

References 27 publications

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“…Modern information technologies adopted for non-destructive monitoring, including satellite remote sensing, near-earth hyperspectral remote sensing, and image analysis, can actively improve crop production management and yield prediction, which is also a vital link to modern agriculture [18]. Digital image analysis uses computers to process graphics or images to PLOS ONE obtain valuable information.…”

Section: Digital Image Processing Technologymentioning

confidence: 99%

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages

et al. 2021

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The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with different agricultural parameters is constructed through different densities and nitrogen fertilizer application rates based on digital image processing technology, and an estimation NC (Nitrogen Content) model is established. The population is classified through the K-Means Clustering algorithm using the feature extraction method, and the Chinese cabbage population quality BPNN (Backpropagation Neural Network) model is constructed. The nonlinear mapping relationship between different agricultural parameters and population quality, and the contribution rate of each indicator, are studied. The nitrogen utilization of Chinese cabbage is monitored effectively. Results demonstrate that the proposed NC estimation model has correlation coefficients above 0.70 in different growth stages. This model can accurately estimate the NC of the Chinese cabbage population. The results of the Chinese cabbage population quality BPNN model show that the population planting density based on the seedling number is reasonable. The constructed population quality evaluation model has a high R2 value and a comparatively low RMSE (Root Mean Square Error) value for the quality evaluation of Chinese cabbage in different periods, showing that it applies to evaluate the population quality of Chinese cabbage in different growth stages. The constructed nitrogen utilization model and quality evaluation model can monitor the nutrient utilization of crops in different growth stages, ascertain the agricultural characteristics of other yield groups in different growth stages, and clarify the performance of agricultural parameters in different growth stages. The above results can provide some ideas for crop growth intelligent detection.

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Section: Digital Image Processing Technologymentioning

confidence: 99%

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages

et al. 2021

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“…According to the analysis of the grabbing process, it is found that the major reason for the result is that the irregularity of objects in life is more prominent, and the change of small grasping angle easily leads to large change of bending angle, which means more attention should be paid to the bending condition for further study [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

An Integrated Multi-Sensor Network for Adaptive Grasping of Fragile Fruits: Design and Feasibility Tests

Xie

Zhang

Zhou

et al. 2020

Sensors

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Secure grasping of fragile fruits and other agricultural products without potential slip and damage is still a challenge due to the size and shape varying, bruise susceptible, as well as hardness changing during fruit and vegetable maturation. In the robotic grasping process, the mechanical damage mainly depends upon the aggressiveness of the gripper and the sensitivity of the product to the damage. In this study, a flexible gripper integrated with multi-sensor network is designed and tested. The network proposed includes three kinds of sensors that enable the gripper to grasp various products with the sense of touch and visual perception. Particular attention has been attached to the sensors applied between the fingers, and this makes sensing and grasping capabilities improved. To create an accurate grasping system, a grasping algorithm and the force control model are proposed for any bending state based on Cosserat theory. The boundary detection is included in the grasping algorithm, detecting the shape edge by some certain point calculation. The created grasping system guarantees mechanical compliance by evaluating and adjusting the finger status including force, angle, and direction. Multi-group tests have been done on grasping several objects of different sizes and materials in daily life. The relationship between force, bending, and surface material is also analyzed and compared under different conditions. The numerical comparisons related to the measurement error are analyzed based on their standard deviations. Experimental results indicate that this flexible manipulator with proposed system and strategy has better grasping ability for fragile fruits with its good flexibility and dexterity.

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“…The final 1000way layer is a soft-max layer that outputs class probabilities. VGG-16 was trained and tested on ImageNet database to classify the 1.3 million images to the 1000 classes with accuracy of 92.7% [13].…”

Section: Segmentation Methodsmentioning

confidence: 99%

“…The second generation RealSense cameras and its stereo vision to calculate depth [9] were introduced in January 2018. Despite the fact that Intel RealSense D400 series devices appeared on the market only in recent years, they began to be used in many areas, for example, in security systems [10], robotics [11], medicine [12], and agriculture [13]. At the same time, there are no works that report on the RealSense D400 using for recognizing hand gestures that can be integrated into effective HCI systems.…”

Section: Introductionmentioning

confidence: 99%

Deep learning based static hand gesture recognition

Satybaldina

Kalymova

2021

IJEECS

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and image segmentation algorithms computer vision methods from the OpenCV and Intel Real Sense libraries are used. The subsystem for features extracting and gestures classification is based on the modified VGG-16 by using the TensorFlow&Keras deep learning framework. Performance of the static gestures recognition system is evaluated using maching learning metrics. Experimental results show that the proposed model, trained on a database of 2000 images, provides high recognition accuracy both at the training and testing stages.

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Seedling-lump integrated non-destructive monitoring for automatic transplanting with Intel RealSense depth camera

Cited by 21 publications

References 27 publications

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages

Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages

An Integrated Multi-Sensor Network for Adaptive Grasping of Fragile Fruits: Design and Feasibility Tests

Deep learning based static hand gesture recognition

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