A Study on Long-Close Distance Coordination Control Strategy for Litchi Picking

Wang, Hongjun; Lin, Yiyan; Xu, Xiujin; Chen, Zhaoyi; Wu, Zihao; Tang, Yunchao

doi:10.3390/agronomy12071520

Cited by 36 publications

(12 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The basic operations of morphological image processing include expansion, corrosion, open operation, and close operation. In Figure 6A is the original ( Figures 6B–F ) are the comparison of the effects of several image morphological processing methods, such as binarization processing, expansion processing, corrosion processing, open operation, and closed operation ( Wang et al, 2022 ).…”

Section: Forest Fire Risk Image Recognitionmentioning

confidence: 99%

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

et al. 2022

View full text Add to dashboard Cite

Forests are indispensable links in the ecological chain and important ecosystems in nature. The destruction of forests seriously influences the ecological environment of the Earth. Forest protection plays an important role in human sustainable development, and the most important aspect of forest protection is preventing forest fires. Fire affects the structure and dynamics of forests and also climate and geochemical cycles. Using various technologies to monitor the occurrence of forest fires, quickly finding the source of forest fires, and conducting early intervention are of great significance to reducing the damage caused by forest fires. An improved forest fire risk identification algorithm is established based on a deep learning algorithm to accurately identify forest fire risk in a complex natural environment. First, image enhancement and morphological preprocessing are performed on a forest fire risk image. Second, the suspected forest fire area is segmented. The color segmentation results are compared using the HAF and MCC methods, and the suspected forest fire area features are extracted. Finally, the forest fire risk image recognition processing is conducted. A forest fire risk dataset is constructed to compare different classification methods to predict the occurrence of forest fire risk to improve the backpropagation (BP) neural network forest fire identification algorithm. An improved machine learning algorithm is used to evaluate the classification accuracy. The results reveal that the algorithm changes the learning rate between 0.1 and 0.8, consistent with the cross-index verification of the 10x sampling algorithm. In the combined improved BP neural network and support vector machine (SVM) classifier, forest fire risk is recognized based on feature extraction and the BP network. In total, 1,450 images are used as the training set. The experimental results reveal that in image preprocessing, image enhancement technology using the frequency and spatial domain methods can enhance the useful information of the image and improve its clarity. In the image segmentation stage, MCC is used to evaluate the segmentationresults. The accuracy of this algorithm is high compared with other algorithms, up to 92.73%. Therefore, the improved forest fire risk identification algorithm can accurately identify forest fire risk in the natural environment and contribute to forest protection.

show abstract

Section: Forest Fire Risk Image Recognitionmentioning

confidence: 99%

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Through RL, the network parameters of DL are tuned to continuously optimize their own behavior strategies; its framework is shown in Figure 2. This method has become a new research hotspot in the field of artificial intelligence and has been applied in fields such as robot control [30][31][32], autonomous driving [33], and machine vision [34][35][36][37][38][39][40][41]. In this paper, the vehicle autonomous driving decision problem is modeled with a partially observable Markov decision process (POMDP) [42], and the autonomous driving strategy optimization problem is solved by identifying the optimal driving strategy of the POMDP.…”

Section: Modeling Of the Automatic Driving Strategy Optimization Problemmentioning

confidence: 99%

APF-DPPO: An Automatic Driving Policy Learning Method Based on the Artificial Potential Field Method to Optimize the Reward Function

Lin

Zhang

et al. 2022

Machines

Self Cite

View full text Add to dashboard Cite

To address the difficulty of obtaining the optimal driving strategy under the condition of a complex environment and changeable tasks of vehicle autonomous driving, this paper proposes an end-to-end autonomous driving strategy learning method based on deep reinforcement learning. The ideas of target attraction and obstacle rejection of the artificial potential field method are introduced into the distributed proximal policy optimization algorithm, and the APF-DPPO learning model is established. To solve the range repulsion problem of the artificial potential field method, which affects the optimal driving strategy, this paper proposes a directional penalty function method that combines collision penalty and yaw penalty to convert the range penalty of obstacles into a single directional penalty, and establishes the vehicle motion collision model. Finally, the APF-DPPO learning model is selected to train the driving strategy for the virtual vehicle, and the transfer learning method is selected to verify the comparison experiment. The simulation results show that the completion rate of the virtual vehicle in the obstacle environment that generates penalty feedback is as high as 96.3%, which is 3.8% higher than the completion rate in the environment that does not generate penalty feedback. Under different reward functions, the method in this paper obtains the highest cumulative reward value within 500 s, which improves 69 points compared with the reward function method based on the artificial potential field method, and has higher adaptability and robustness in different environments. The experimental results show that this method can effectively improve the efficiency of autonomous driving strategy learning and control the virtual vehicle for autonomous driving behavior decisions, and provide reliable theoretical and technical support for real vehicles in autonomous driving decision-making.

show abstract

“…The above research focused on the segmentation of branches and trunks in the image and did not explore the location of pruning points. Wang et al (2022) used the Mask R-CNN instance segmentation network to segment branches and bifurcate stems in a scene and extract the fruit-bearing branch positions based on mask relationships. The location of the litchi cutting point was realized by introducing depth reference points and fruit stalk positioning lines.…”

Section: Introductionmentioning

confidence: 99%

Skeleton extraction and pruning point identification of jujube tree for dormant pruning using space colonization algorithm

Xia²,

Zhang³

et al. 2023

Front. Plant Sci.

View full text Add to dashboard Cite

The dormant pruning of jujube is a labor-intensive and time-consuming activity in the production and management of jujube orchards, which mainly depends on manual operation. Automatic pruning using robots could be a better way to solve the shortage of skilled labor and improve efficiency. In order to realize automatic pruning of jujube trees, a method of pruning point identification based on skeleton information is presented. This study used an RGB-D camera to collect multi-view information on jujube trees and built a complete point cloud information model of jujube trees. The space colonization algorithm acts on the global point cloud to generate the skeleton of jujube trees. The iterative relationship between skeleton points was represented by constructing a directed graph. The proposed skeleton analysis algorithm marked the skeleton as the trunk, the primary branches, and the lateral branches and identified the pruning points under the guidance of pruning rules. Finally, the visual model of the pruned jujube tree was established through the skeleton information. The results showed that the registration errors of individual jujube trees were less than 0.91 cm, and the average registration error was 0.66 cm, which provided a favorable database for skeleton extraction. The skeleton structure extracted by the space colonization algorithm had a high degree of coincidence with jujube trees, and the identified pruning points were all located on the primary branches of jujube trees. The study provides a method to identify the pruning points of jujube trees and successfully verifies the validity of the pruning points, which can provide a reference for the location of the pruning points and visual research basis for automatic pruning.

show abstract

A Study on Long-Close Distance Coordination Control Strategy for Litchi Picking

Cited by 36 publications

References 43 publications

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

Forest fire monitoring via uncrewed aerial vehicle image processing based on a modified machine learning algorithm

APF-DPPO: An Automatic Driving Policy Learning Method Based on the Artificial Potential Field Method to Optimize the Reward Function

Skeleton extraction and pruning point identification of jujube tree for dormant pruning using space colonization algorithm

Contact Info

Product

Resources

About