Bayberry segmentation in a complex environment based on a multi-module convolutional neural network

Lei, Huan Yao; Huang, Kai; Jiao, Zeyu; Tang, Yu; Zhong, Zhenyu; Cai, Yi

doi:10.1016/j.asoc.2022.108556

Cited by 19 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…30,31 Because of its excellent performance in prediction, the self-organizing neural network has a strong advantage in estimation of road adhesion coefficient compared with other neural networks. The multi-module neural network 32–34 is composed of several independent sub-network modules, which is suitable for the identification of road adhesion coefficient for multiple wheels. The neural network prediction model of road adhesion coefficient was established for each sub-module, and neural network training can be carried out separately to find the optimal network hidden layer structure and establish the best neural network prediction model.…”

Section: Lm-mmsofnn Algorithmmentioning

confidence: 99%

Section: Lm-mmsofnn Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Road adhesion coefficient estimation by multi-sensors with LM-MMSOFNN algorithm

Guiyang

Feng

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Accurate and efficient road adhesion coefficient estimation is the premise for the proper functioning of vehicle active safety control system. With the increased application of distributed drive vehicles and on-board sensors, a multi-module self-organizing feedforward neural network (LM-MMSOFNN) based on improved Levenberg-Marquardt (LM) learning algorithm is proposed for online road adhesion coefficient estimation. In this method, the vehicle dynamics model and the Dugoff tire model were well established, and the input and output variables of the neural network model were obtained by Principal Component Analysis (PCA) method. To improve the estimation accuracy, Extended Kalman Filter (EKF) and Moving Average (MA) were used to denoise the measured signal. On this basis, a road adhesion coefficient estimation model based on multi-module self-organizing neural network was established. Both sides of road adhesion coefficients are calculated by multi-module self-organizing neural network simultaneously. Through the increase and decrease of self-organizing neurons and the improved LM learning algorithm, the computational complexity and system hardware storage are reduced, and the algorithm exhibits a good adaptability to different roads. Simulation and vehicle experiments show that the proposed method can fully extract multi-sensor information and adapt to the different road characteristics changes under driving condition. As compared with Kmeans method, it has higher estimation accuracy and stronger adaptability to varying speed.

show abstract

Section: Lm-mmsofnn Algorithmmentioning

confidence: 99%

Section: Lm-mmsofnn Algorithmmentioning

confidence: 99%

Road adhesion coefficient estimation by multi-sensors with LM-MMSOFNN algorithm

Guiyang

Feng

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

show abstract

“…To be sure that the trained NNs will learn the main characteristics of the objects to be detected or classified and will be more robust in a natural environment such as the orchard, many researchers have performed data augmentation starting from the original data. For example, 15 different augmentation methods are mentioned in ( Lei et al., 2022 ), such as Gaussian noise, impulse noise, out-of-focus blur, motion blur, zoom blur, elastic transformation, rotation transformation, random erase, random crop, random flip, fog, brighten, contrast, color dithering, and pixelated. To obtain good results on NN training, the classes in the dataset need to be balanced and annotated.…”

Section: Neural Network Used For Orchard Monitoringmentioning

confidence: 99%

“…Using artificial intelligence methods to process the images acquired by autonomous terrestrial or aerial platforms, the conditions for picking fruits that have reached maturity in the optimal period can be improved. This approach leads to increased economic efficiency for orchards ( Lei et al., 2022 ). Fruit estimation is challenging and the number of fruits on a tree cannot be measured exactly due to occlusions ( Zhang X. et al., 2019 ).…”

Section: Applicationsmentioning

confidence: 99%

Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review

Popescu,

Ichim,

Stoican

2023

Front. Plant Sci.

View full text Add to dashboard Cite

Orchard monitoring is a vital direction of scientific research and practical application for increasing fruit production in ecological conditions. Recently, due to the development of technology and the decrease in equipment cost, the use of unmanned aerial vehicles and artificial intelligence algorithms for image acquisition and processing has achieved tremendous progress in orchards monitoring. This paper highlights the new research trends in orchard monitoring, emphasizing neural networks, unmanned aerial vehicles (UAVs), and various concrete applications. For this purpose, papers on complex topics obtained by combining keywords from the field addressed were selected and analyzed. In particular, the review considered papers on the interval 2017-2022 on the use of neural networks (as an important exponent of artificial intelligence in image processing and understanding) and UAVs in orchard monitoring and production evaluation applications. Due to their complexity, the characteristics of UAV trajectories and flights in the orchard area were highlighted. The structure and implementations of the latest neural network systems used in such applications, the databases, the software, and the obtained performances are systematically analyzed. To recommend some suggestions for researchers and end users, the use of the new concepts and their implementations were surveyed in concrete applications, such as a) identification and segmentation of orchards, trees, and crowns; b) detection of tree diseases, harmful insects, and pests; c) evaluation of fruit production, and d) evaluation of development conditions. To show the necessity of this review, in the end, a comparison is made with review articles with a related theme.

show abstract

“…Zhang et al [14] proposed a method for dynamic threshold segmentation of RGB images using the normalized color difference (R-G)/(R+G) method and the OTSU algorithm. However, in actual use, these methods will be affected by changes in lighting conditions which affects the recognition rate [15,16] . In recent years, the neural network has effectively overcome the shortcomings of traditional image recognition technology that are greatly affected by environmental factors [17] .…”

Section: Introductionmentioning

confidence: 99%

Recognition and localization of strawberries from 3D binocular cameras for a strawberry picking robot using coupled YOLO/Mask R-CNN

Hu¹,

Kaizu²,

Zhang³

et al. 2022

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

To solve the problem of high labour costs in the strawberry picking process, the approach of a strawberry picking robot to identify and find strawberries is suggested in this study. First, 1000 images including mature, immature, single, multiple, and occluded strawberries were collected, and a two-stage detection Mask R-CNN instance segmentation network and a one-stage detection YOLOv3 target detection network were used to train a strawberry identification model which classified strawberries into two categories: mature and immature. The accuracy ratings for YOLOv3 and Mask R-CNN were 93.4% and 94.5%, respectively. Second, the ZED stereo camera, triangulation, and a neural network were used to locate the strawberry in three dimensions. YOLOv3 identification accuracy was 3.1 mm, compared to Mask R-CNN of 3.9 mm. The strawberry detection and positioning method proposed in this study may effectively be used to supply the picking robot with a precise location of the ripe strawberry.

show abstract

Bayberry segmentation in a complex environment based on a multi-module convolutional neural network

Cited by 19 publications

References 26 publications

Road adhesion coefficient estimation by multi-sensors with LM-MMSOFNN algorithm

Road adhesion coefficient estimation by multi-sensors with LM-MMSOFNN algorithm

Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review

Recognition and localization of strawberries from 3D binocular cameras for a strawberry picking robot using coupled YOLO/Mask R-CNN

Contact Info

Product

Resources

About