Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN

Zhang, Keke; Wu, Qiufeng; Chen, Yiping

doi:10.1016/j.compag.2021.106064

Cited by 106 publications

(44 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [16], a synthetic image dataset of soybean leaf disease synthetic images is developed to first address the problem of an insufficient dataset. The study designs a fusion of multiple Faster R-CNN (MF3 R-CNN) functions.…”

Section: Scientific Publicationsmentioning

confidence: 99%

“…Yan et al [15] RGB video YOLOv5s, YOLOv5s, YOLOv3, YOLOv4 and EfficientDet-D0 Zhang et al [16] RGB image Faster R-CNN Parvathi et al [17] RGB image Faster R-CNN, SSD, YOLOv3, R-FCN Lawal et al [18] RGB images YOLOv3 modified: YOLO-Tomato-A, YOLO-Tomato-B and YOLO-Tomato-C Itakura et al [19] RGB video YOLOv2 Perez-Borrero et.al. [20] RGB video Mask R-CNN Apolo Apolo et al [21] RGB images Faster R-CNN Santos et al [22] RGB images Mask R-CNN, YOLOv2 and YOLOv3…”

Section: Article Data Type Modelmentioning

confidence: 99%

See 1 more Smart Citation

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

et al. 2021

View full text Add to dashboard Cite

Raspberries are fruit of great importance for human beings. Their products are segmented by quality. However, estimating raspberry quality is a manual process carried out at the reception of the fruit processing plant, and is thus exposed to factors that could distort the measurement. The agriculture industry has increased the use of deep learning (DL) in computer vision systems. Non-destructive and computer vision equipment and methods are proposed to solve the problem of estimating the quality of raspberries in a tray. To solve the issue of estimating the quality of raspberries in a picking tray, prototype equipment is developed to determine the quality of raspberry trays using computer vision techniques and convolutional neural networks from images captured in the visible RGB spectrum. The Faster R–CNN object-detection algorithm is used, and different pretrained CNN networks are evaluated as a backbone to develop the software for the developed equipment. To avoid imbalance in the dataset, an individual object-detection model is trained and optimized for each detection class. Finally, both hardware and software are effectively integrated. A conceptual test is performed in a real industrial scenario, thus achieving an automatic evaluation of the quality of the raspberry tray, in this way eliminating the intervention of the human expert and eliminating errors involved in visual analysis. Excellent results were obtained in the conceptual test performed, reaching in some cases precision of 100%, reducing the evaluation time per raspberry tray image to 30 s on average, allowing the evaluation of a larger and representative sample of the raspberry batch arriving at the processing plant.

show abstract

Section: Scientific Publicationsmentioning

confidence: 99%

Section: Article Data Type Modelmentioning

confidence: 99%

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

et al. 2021

View full text Add to dashboard Cite

show abstract

“…For the purposes of network training, a synthetic dataset is procedurally generated in Blender, in order to mitigate the cost of labeling a large training dataset [11], [12]. Increasingly popular synthetic datasets like [13], have recently found applications in agriculture for various crops and cultures [14], [15], [16], including a synthetic dataset for the C. annuum semantic segmentation tasks [17]. Transfer learning for the network first conducted on the synthetic dataset is followed by additional fine tuning on a small dataset of real, manually labeled images.…”

Section: A Fruit Detectionmentioning

confidence: 99%

Render-in-the-loop aerial robotics simulator: Case Study on Yield Estimation in Indoor Agriculture

Ivanović¹,

Polić²,

Tabak³

et al. 2022

Preprint

View full text Add to dashboard Cite

Inspired by recent promising results in sim-toreal transfer in deep learning we built a realistic simulation environment combining a Robot Operating System (ROS)compatible physics simulator (Gazebo) with Cycles, the realistic production rendering engine from Blender. The proposed simulator pipeline allows us to simulate near-realistic RGB-D images. To showcase the capabilities of the simulator pipeline we propose a case study that focuses on indoor robotic farming. We developed a solution for sweet pepper yield estimation task. Our approach to yield estimation starts with aerial robotics control and trajectory planning, combined with deep learningbased pepper detection, and a clustering approach for counting fruit. The results of this case study show that we can combine real time dynamic simulation with near realistic rendering capabilities to simulate complex robotic systems.

show abstract

“…In addition, the 147 highway slopes were coded as 0 or 1, where stable slopes were coded as 0 and unstable slopes were coded as 1. [40]. The following took the first structure as an example to illustrate the calculation process.…”

Section: Spatial Prediction Models Of Hsds In Boshan Districtmentioning

confidence: 99%

Spatial prediction of highway slope disasters based on convolution neural networks

Yin

Wang²,

Zhao³

2022

Nat Hazards

View full text Add to dashboard Cite

In order to clarify the spatial differentiations of highway slope disasters (HSDs) in Boshan District, spatial prediction was carried out based on ECG-CNN with the support of GIS. Spatial prediction factors of HSDs were selected, the stabilities of the 147 highway slopes in Boshan District were determined. The spatial prediction model of HSDs was established by ECG-CNN, and the spatial susceptibility map of HSDs in Boshan District was drawn. Influences of the prediction factor combinations and the drill sample & verification sample combinations on the prediction success rates were verified. The results showed that low susceptible areas, medium susceptible areas and high susceptible areas account for 56.92%, 28.46% and 14.62% of the total areas of Boshan District respectively. Some sections of Binlai Expressway, G205, G309, S210 and S307 pass through high susceptible areas. The surface cutting depth has a small impact on the prediction success rate, while the elevation and gradient have great impacts on the prediction success rate. When the drill samples are small, network drill's maturity has a great impact on the prediction success rate, while when there are many drill samples, the model's logical structure itself has a large impact on the prediction success rate.

show abstract

Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN

Cited by 106 publications

References 18 publications

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

Disease and Defect Detection System for Raspberries Based on Convolutional Neural Networks

Render-in-the-loop aerial robotics simulator: Case Study on Yield Estimation in Indoor Agriculture

Spatial prediction of highway slope disasters based on convolution neural networks

Contact Info

Product

Resources

About