Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

Lyu, Hong-Kun; Park, Chi-Ho; Han, Dong-Hee; Kwak, Seong Woo; Choi, Byeongdae

doi:10.3390/sym10090355

Cited by 24 publications

(15 citation statements)

References 16 publications

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“…In addition, practical technology development for various industrial applications, including agricultural applications, is ongoing. Deep learning and machine learning technologies are being actively researched for application in the agricultural field, and a representative technical field is image-based object recognition technology [5][6][7][8]. Here, the target objects include all things of industrial interest, e.g., trees, people, cars, roads, buildings, various obstacles, objects, numbers, and letters.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed method is expected to be used as an element technology for the development of object recognition technology applicable to small smart farms, e.g., orchard environments. In our previous work, the coordinates of the lowest part of the image binarized with the ML function were extracted, and the free space centerline between the two rows of apple trees was finally estimated using Naive Bayesian classification [7]. However, in this study, we used the same binarized image as in the previous study, but we intend to detect the location of the tree with high accuracy by proposing the ML characteristic variable based on the binary pixel quantification.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards

2020

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Deep learning and machine learning (ML) technologies have been implemented in various applications, and various agriculture technologies are being developed based on image-based object recognition technology. We propose an orchard environment free space recognition technology suitable for developing small-scale agricultural unmanned ground vehicle (UGV) autonomous mobile equipment using a low-cost lightweight processor. We designed an algorithm to minimize the amount of input data to be processed by the ML algorithm through low-resolution grayscale images and image binarization. In addition, we propose an ML feature extraction method based on binary pixel quantification that can be applied to an ML classifier to detect free space for autonomous movement of UGVs from binary images. Here, the ML feature is extracted by detecting the local-lowest points in segments of a binarized image and by defining 33 variables, including local-lowest points, to detect the bottom of a tree trunk. We trained six ML models to select a suitable ML model for trunk bottom detection among various ML models, and we analyzed and compared the performance of the trained models. The ensemble model demonstrated the best performance, and a test was performed using this ML model to detect apple tree trunks from 100 new images. Experimental results indicate that it is possible to recognize free space in an apple orchard environment by learning using approximately 100 low-resolution grayscale images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards

2020

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“…The machine vision system was based on the use of a multispectral camera to capture real-time images and process the images to obtain trajectories for autonomous navigation. Lyu [4] used naïve Bayesian classification to detect the boundary between the trunks and the ground of an orchard and proposed an algorithm to determine the centerline of the orchard road for automatic driving of the orchard's autonomous navigation vehicle. In order to enable agricultural robots to extract effective navigation information in a complex, open, non-structural farmland environment, and solve the instability of navigation information extraction algorithm caused by light changes, An [5] proposed to use the color constancy theory to solve lighting problems in machine vision navigation.…”

Section: Introductionmentioning

confidence: 99%

A Deep Residual U-Type Network for Semantic Segmentation of Orchard Environments

et al. 2020

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Recognition of the orchard environment is a prerequisite for realizing the autonomous operation of intelligent horticultural tractors. Due to the complexity of the environment and the algorithm’s dependence on ambient light, the traditional recognition algorithm based on machine vision is limited and has low accuracy. However, the deep residual U-type network is more effective in this situation. In an orchard, the deep residual U-type network can perform semantic segmentation on trees, drivable roads, debris, etc. The basic structure of the network adopts a U-type network, and residual learning is added in the coding layer and bottleneck layer. Firstly, the residual module is used to improve the network depth, enhance the fusion of semantic information at different levels, and improve the feature expression capability and recognition accuracy. Secondly, the decoding layer uses up-sampling for feature mapping, which is convenient and fast. Thirdly, the semantic information of the coding layer is integrated by skip connection, which reduces the network parameters and accelerates the training. Finally, a network was built through the Pytorch Deep Learning Framework, which was implemented to train the data set and compare the network with the fully convolutional neural network, the U-type network, and the Front-end+Large network. The results show that the deep residual U-type network has the highest recognition accuracy, with an average of 85.95%, making it more suitable for environment recognition in orchards.

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“…About the last step, a variety of navigation line detection methods have been proposed in recent years. Generally, these methods are classified into several categories according to their detection principles, such as HT (Hough transform), LR (linear regression), SA (speckle analysis), SV (stereo vision), and HF (horizontal fringes) [16][17][18][19][20]. It is worth noting that the crop row is approximated as a straight line in the above methods.…”

Section: Introductionmentioning

confidence: 99%

Research on Vision-Based Navigation for Plant Protection UAV under the Near Color Background

et al. 2019

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GPS (Global Positioning System) navigation in agriculture is facing many challenges, such as weak signals in orchards and the high cost for small plots of farmland. With the reduction of camera cost and the emergence of excellent visual algorithms, visual navigation can solve the above problems. Visual navigation is a navigation technology that uses cameras to sense environmental information as the basis of an aircraft flight. It is mainly divided into five parts: Image acquisition, landmark recognition, route planning, flight control, and obstacle avoidance. Here, landmarks are plant canopy, buildings, mountains, and rivers, with unique geographical characteristics in a place. During visual navigation, landmark location and route tracking are key links. When there are significant color-differences (for example, the differences among red, green, and blue) between a landmark and the background, the landmark can be recognized based on classical visual algorithms. However, in the case of non-significant color-differences (for example, the differences between dark green and vivid green) between a landmark and the background, there are no robust and high-precision methods for landmark identification. In view of the above problem, visual navigation in a maize field is studied. First, the block recognition method based on fine-tuned Inception-V3 is developed; then, the maize canopy landmark is recognized based on the above method; finally, local navigation lines are extracted from the landmarks based on the maize canopy grayscale gradient law. The results show that the accuracy is 0.9501. When the block number is 256, the block recognition method achieves the best segmentation. The average segmentation quality is 0.87, and time is 0.251 s. This study suggests that stable visual semantic navigation can be achieved under the near color background. It will be an important reference for the navigation of plant protection UAV (Unmanned Aerial Vehicle).

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Orchard Free Space and Center Line Estimation Using Naive Bayesian Classifier for Unmanned Ground Self-Driving Vehicle

Cited by 24 publications

References 16 publications

Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards

Machine Learning Feature Extraction Based on Binary Pixel Quantification Using Low-Resolution Images for Application of Unmanned Ground Vehicles in Apple Orchards

A Deep Residual U-Type Network for Semantic Segmentation of Orchard Environments

Research on Vision-Based Navigation for Plant Protection UAV under the Near Color Background

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