YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment

Fu, Lanhui; Yang, Zhou; Wu, Fengyun; Zou, Xiangjun; Lin, Jiaquan; Cao, Yongjun; Duan, Jieli

doi:10.3390/agronomy12020391

Cited by 45 publications

(27 citation statements)

References 41 publications

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“…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

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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.

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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

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“…For example, the YOLO (You Only Look Once) algorithm is a popular computer vision algorithm that has been used in several challenges in agriculture. YOLO has previously been used to detect flowers for robotic pollination (Li et al, 2022), fruit load and maturation (Cuong et al, 2022;Fu et al, 2022;Mirhaji et al, 2021), and weed detection (Parico and Ahamed, 2020). Therefore, this study aims to implement and explore different YOLO algorithms to detect coffee fruits on tree branches and classify the fruits according to the different maturation stages.…”

Section: Introductionmentioning

confidence: 99%

Detection of coffee fruits on tree branches using computer vision

Bazame

Molin

Althoff

et al. 2023

Sci. agric. (Piracicaba, Braz.)

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Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

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“…Fruit-picking robots are a class of agricultural machines that combine the advantages of the accuracy, efficiency, and characteristics of diverse sensors. They primarily perform automatic operations for crops in natural environments [1][2][3][4][5][6]. Among them, the deployment of machine vision systems and corresponding recognition algorithms allows them to efficiently complete many harvesting operations.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Lin

et al. 2022

Agronomy

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For the automated robotic picking of bunch-type fruit, the strategy is to roughly determine the location of the bunches, plan the picking route from a remote location, and then locate the picking point precisely at a more appropriate, closer location. The latter can reduce the amount of information to be processed and obtain more precise and detailed features, thus improving the accuracy of the vision system. In this study, a long–close distance coordination control strategy for a litchi picking robot was proposed based on an Intel Realsense D435i camera combined with a point cloud map collected by the camera. The YOLOv5 object detection network and DBSCAN point cloud clustering method were used to determine the location of bunch fruits at a long distance to then deduce the sequence of picking. After reaching the close-distance position, the Mask RCNN instance segmentation method was used to segment the more distinctive bifurcate stems in the field of view. By processing segmentation masks, a dual reference model of “Point + Line” was proposed, which guided picking by the robotic arm. Compared with existing studies, this strategy took into account the advantages and disadvantages of depth cameras. By experimenting with the complete process, the density-clustering approach in long distance was able to classify different bunches at a closer distance, while a success rate of 88.46% was achieved during fruit-bearing branch locating. This was an exploratory work that provided a theoretical and technical reference for future research on fruit-picking robots.

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YOLO-Banana: A Lightweight Neural Network for Rapid Detection of Banana Bunches and Stalks in the Natural Environment

Cited by 45 publications

References 41 publications

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

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

Detection of coffee fruits on tree branches using computer vision

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

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