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27th International Conference on Pattern Recognition, ICPR 2024

作者： Ni, Zixiang Zhai, Weixin College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural Affairs Beijing100083 China

ISBN: (纸本)9783031783883

Rural roads extracted from agricultural machinery trajectories have significant research value. Due to the intricate network of rural roads and the large difference in the density of agricultural machinery trajectories, traditional road extraction methods struggle to perform effectively on rural roads with complex topology and on agricultural machinery trajectories with obscure road features. Therefore, this paper proposes a CGAN-based model named T2R-GAN (Trajectory to Road-GAN) for rural thematic road extraction, which learns the trajectory-to-road feature mapping through continuous adversarial training between the ELAU-Net generator and the PatchGAN discriminator to adapt to trajectories of various densities. ELAU-Net is an efficient network that utilizes an encoder-decoder structure and ELA modules to enhance the capture of obscure road features between sparse trajectories. To enhance model performance and reduce the risk of overfitting, bilateral hinge loss is designed to train our model to enhances the discriminator’s discriminative ability to facilitate the generator to generate more realistic roads improve the generalization of the model. To verify the effectiveness of T2R-GAN in extracting roads from agricultural trajectory data, this paper selects the real agricultural trajectory data from Henan Province, China in June 2021 as the dataset for verification. The experimental results show that the proposed method achieves 79.23% F1score, which is 5.53% higher than the previous state-of-art. The proposed T2R-GAN provides a novel and effective approach for extracting rural thematic roads from agricultural machinery trajectories for the first time. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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FrustumFusionNets: A Three-Dimensional Object Detection Network Based on Tractor Road Scene

arXiv

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arXiv 2025年

作者： Yang, Lili Chang, Mengshuai Guo, Xiao Feng, Yuxin Mei, Yiwen Wu, Caicong College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China

To address the issues of the existing frustum-based methods' underutilization of image information in road three-dimensional object detection as well as the lack of research on agricultural scenes, we constructed an object detection dataset using an 80-line Light Detection And Ranging (LiDAR) and a camera in a complex tractor road scene and proposed a new network called FrustumFusionNets (FFNets). Initially, we utilize the results of image-based two-dimensional object detection to narrow down the search region in the three-dimensional space of the point cloud. Next, we introduce a Gaussian mask to enhance the point cloud information. Then, we extract the features from the frustum point cloud and the crop image using the point cloud feature extraction pipeline and the image feature extraction pipeline, respectively. Finally, we concatenate and fuse the data features from both modalities to achieve three-dimensional object detection. Experiments demonstrate that on the constructed test set of tractor road data, the FrustumFusionNetv2 achieves 82.28% and 95.68% accuracy in the three-dimensional object detection of the two main road objects, cars and people, respectively. This performance is 1.83% and 2.33% better than the original model. It offers a hybrid fusion-based multi-object, high-precision, real-time three-dimensional object detection technique for unmanned agricultural machines in tractor road scenarios. On the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) Benchmark Suite validation set, the FrustumFusionNetv2 also demonstrates significant superiority in detecting road pedestrian objects compared with other frustum-based three-dimensional object detection methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Tractors (truck)

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RFWNet: A Lightweight Remote Sensing Object Detector Integrating Multi-Scale Receptive Fields and Foreground Focus Mechanism

arXiv

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arXiv 2025年

作者： Lei, Yujie Sun, Wenjie Jia, Sen Li, Qingquan Zhang, Jie College of Information and Electrical Engineering China Agricultural University Beijing100083 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Spatial Smart Sensing and Services Shenzhen University Shenzhen518060 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China

Challenges in remote sensing object detection (RSOD), such as high inter-class similarity, imbalanced foreground-background distribution, and the small size of objects in remote sensing images significantly hinder detection accuracy. Moreover, the trade-off between model accuracy and computational complexity poses additional constraints on the application of RSOD algorithms. To address these issues, this study proposes an efficient and lightweight RSOD algorithm integrating multi-scale receptive fields and foreground focus mechanism, named RFWNet. Specifically, we proposed a lightweight backbone network Receptive Field Adaptive Selection Network (RFASNet), leveraging the rich context information of remote sensing images to enhance class separability. Additionally, we developed a Foreground Background Separation Module (FBSM) consisting of a background redundant information filtering module and a foreground information enhancement module to emphasize critical regions within images while filtering redundant background information. Finally, we designed a loss function, the Weighted CIoU-Wasserstein (WCW) loss, which weights the IoU-based loss by using the Normalized Wasserstein Distance to mitigate model sensitivity to small object position deviations. Experimental evaluations on the DOTA V1.0 and NWPU VHR-10 datasets demonstrate that RFWNet achieves advanced performance with 6.0M parameters and can achieves 52 FPS. © 2025, CC BY.

关键词： Information filtering

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Behavior recognition and fuel consumption prediction of tractor sowing operations using smartphone

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International Journal of agricultural and Biological Engineering 2022年第4期15卷 154-162页

作者： Lili Yang Weize Tian Weixin Zhai Xinxin Wang Zhibo Chen Long Wen Yuanyuan Xu Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China

In order to qualitatively recognize the behaviors and investigate the relationship between fuel consumption and machinery driving modes of the tractor in a low-cost approach,this study proposed a method for behavior recognition and fuel consumption prediction of tractor sowing operations using a ***,three driving modes were developed for maize sowing scenarios:manual driving assisted driving and unmanned *** sowing,smartphone software and CAN(Controller Area Network)storage devices collected both positional data and engine operating ***,the tractor trajectory points were divided into kinematic sequences,with six driving cycle indicators built in each series based on the time *** on the semantic information of the kinematic sequences,the three operations of sowing,seeds filling,and turning round were well ***,a model for maize sowing fuel consumption forecast was advanced using the principal component analyses and random forest algorithm,regarding three factors:driving cycles,operating behaviors,and driving *** compared to the traditional K-means algorithm,the results demonstrated that the harmonic mean of the precision and recall(F1 score)of sowing behavior recognition,seeds filling behavior recognition,and turning behavior recognition were enhanced by 2.06%,8.99%,and 21.79%,*** terms of the impacts of driving modes and operating behaviors on fuel consumption,assisted driving mode had the lowest fuel usage for both sowing and turning ***,assisted driving is the most fuel-efficient mode for maize *** the three driving modes,the relative error of the fuel consumption prediction model was 0.11 L/h,with the manual driving mode having the lowest relative error at 0.09 L/*** research method lays the foundation for the optimization of tractor operation behavior,the selection of tractor driving mode,and the fine management of tractor fuel consumption.

关键词： smartphone kinematic sequence operating behavior fuel consumption forecast tractor

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Extraction of straight field roads between farmlands based on agricultural vehicle-mounted LiDAR

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International Journal of agricultural and Biological Engineering 2022年第5期15卷 155-162页

作者： Lili Yang Yuanyuan Xu Yajie Liang Jia Qin Yuanbo Li Xinxin Wang Weixin Zhai Long Wen Zhibo Chen Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural AffairsBeijing 100083China

The application of autonomous agricultural vehicles is gaining popularity as a way to increase production efficiency and lower operational *** achieve high performance,perception tasks(such as obstacle detection,road extraction,and drivable area extraction)are of great *** with structured roads,field roads between farmlands,including unstructured roads and semi-structured roads,are unfavorable for autonomous agricultural vehicle driving due to their bumpiness and unstructured *** study proposed an extraction method for the straight field roads between *** proposed method was based on the point cloud data acquired by LiDAR(Velodyne VLP-16)mounted on a John Deere 12046B-1204 *** proposed method has three aspects:Euclidean Clustering-based extraction,boundary-based extraction,and road point cloud curve segment ***,Euclidean Clustering with K-Dimensional(KD)-Tree data structure was adopted to extract the road curve segments close to the LiDAR composed of road ***,the boundary lines constraint was constructed to extract the distant road curve ***,the local distance ratio was used to modify the extracted road curve *** average extraction accuracy for both semi-structured and unstructured roads exceeded 98%,and the false positive rate(FPR)was less than 0.5%.These experimental findings demonstrated that the proposed road extraction method was precise and *** proposed method of this study can be applied to enhance the perception ability of autonomous agricultural vehicles thereby increasing the efficiency and safety of field road driving.

关键词： road extraction straight field road autonomous agricultural vehicle LiDAR farmland

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Parameter optimization of the field-road trajectory segmentation model based on the chaos sensing slime mould algorithm

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Soft Computing 2024年第19期28卷 11065-11132页

作者： Pan, Jiawen Guo, Zhou Wu, Caicong Zhai, Weixin College of Information and Electrical Engineering China Agricultural University Beijing100083 China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing100083 China

Field-road trajectory segmentation, which aims to automatically divide a trajectory into a sequence of field-road segments, is one of the important tasks of the agricultural machinery trajectory process. This study aims to enhance the performance of the field-road segmentation problem by introducing a novel parameter optimization strategy that relies on metaheuristic algorithms. The utilization of the metaheuristic algorithm offers precise and efficient techniques for determining parameter combinations in the field-road segmentation model. A novel enhanced optimization algorithm called the chaos sensing slime mould algorithm (CSSMA), aimed at determining out the optimal parameter combination from a large constrained solution space is proposed to further improve the segmentation performance of the model. First, to strengthen the exploration capability of the algorithm, a chaotic strategy is used to initialize the search agents. Furthermore, the CSSMA incorporates the beetle antennae search algorithm and the genetic algorithm to improve its exploitation potential. Ultimately, a proposed technique for updating control parameters in a nonlinear and dynamic manner aims to achieve a balance between exploration and exploitation. This strategy involves adaptively adjusting computing operations based on feedback from the optimization process. In order to assess the effectiveness of the CSSMA, the algorithm is compared to other metaheuristic algorithms using 23 standard benchmark functions. Furthermore, the CSSMA is utilized to address the practical implementation of the parameter optimization approach for field-road segmentation. The goal is to achieve the utmost accuracy in segmentation and compare it with the current method. Experimental results demonstrate the superior convergence and speed of CSSMA, along with its benefits in extracting optimal parameter structures. It surpasses prior techniques in handling the complexities of field-road trajectory processing problems in

关键词： Genetic algorithms

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Accurate crop row recognition of maize at the seedling stage using lightweight network

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International Journal of agricultural and Biological Engineering 2024年第1期17卷 189-198,I0001页

作者： Jian Wei Mengfan Zhang Caicong Wu Qin Ma Weitao Wang Chuanfeng Wan College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural AffairsBeijing 100083China

Accurate extraction of crop row is very important for automation of agricultural *** rows are required for accurate machine guidance in agricultural production such as fertilization,plant protection,weeding and *** this study,an efficient crop row detection algorithm called Crop-BiSeNet V2 was proposed,which combined BiSeNet V2 with a spatial convolutional neural *** proposed Crop-BiSeNet V2 detected crop rows in color images without the use of threshold and other pre-information such as number of rows.A data set had 2697 maize crop images was constructed in challenging field trial conditions such as variable light,shadows,presence of weeds,and irregular crop *** proposed system was experimentally determined to overcome the interference of different complex *** it can be applied to crop rows of different numbers,straight lines and *** analyses were performed to check the robustness of the *** this algorithm with the Fully Convolutional Networks(FCN)algorithm,it exhibited superior performance and saved 84.85 *** accuracy rate reached 0.9811,and the detection speed reached 65.54 ms/*** Crop-BiSeNet V2 algorithm proposed in this study show strong generalization performance for seedling crop row *** provides high-reliability technical support for crop row detection research and assists in the study of intelligent field operation machinery navigation.

关键词： computer vision crop row detection precision agriculture semantic segmentation

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Nutrient Deficiency Diagnosis of Plants Based on Transfer Learning and Lightweight Convolutional Neural Networks MobileNetV3-Large 11

Nutrient Deficiency Diagnosis of Plants Based on Transfer Le...

引用

11th International Conference on Computing and Pattern Recognition, ICCPR 2022

作者： Yan, Qian Lin, Xuhong Gong, Wenwen Wu, Caicong Chen, Yifei China Agricultural University China College of Information and Electrical Engineering Key Laboratory of Agricultural Machinery Monitoring and Big Data China

ISBN: (纸本)9781450397056

Nutrient Deficiency Diagnosis of Plants is an important application in precision agriculture. At present, nutrient deficiency diagnosis of plants mainly depends on manual identification, which makes it difficult to ensure efficiency and accuracy. Therefore, based on deep learning and focusing on the problems of difficult convergence and poor real-time performance of the existing deep convolution neural network in the detection of plant nutrient deficiency, this study proposes a lightweight model - UMNet (Nutrient-MobileNetV3-Network) for plant nutrient deficiency detection. This model enhances the collected rice leaf images to expand the dataset, then migrates the knowledge learned by the MobilenetV3-Large network on the ImageNet dataset to UMNet, redesigns a new full connection layer, and uses a new activation function. The experimental results show that: (1) Transfer learning solves the problem of insufficient training data. Compared with learning without transfer learning, the accuracy increases by 7.22% ∼9.63%, which greatly improves the convergence speed and recognition accuracy of the model. (2) Compared with complex convolutional neural networks(CNN), such as InceptionV3, InceptionResnetV2 and VGG16, the lightweight network UMNet has lower storage requirements and shorter training time. At the same time, it can still ensure high accuracy, and the recognition accuracy is better than other lightweight networks with the same complexity: ShuffleNetV2, EfficientNetB0 and Xception. The identification accuracy of the plant nutrient deficiency detection model UMNet constructed in this paper can reach 97.80%, and the training time of a single epoch is about 46.4s. It only takes 1.45s to predict the nutrient deficiency of a single object, which realizes the intelligent detection in the field of plant nutrient deficiency, and it will promote academic exploration of deep learning in plant phenotype research. © 2022 ACM.

关键词： Image recognition

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Recognition of field roads based on improved U-Net++Network

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International Journal of agricultural and Biological Engineering 2023年第2期16卷 171-178页

作者： Lili Yang Yuanbo Li Mengshuai Chang Yuanyuan Xu Bingbing Hu Xinxin Wang Caicong Wu College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Kunlun Beidou Intelligence Technologies Co.Ltd. Beijing 102200China

Unmanned driving of agricultural machinery has garnered significant attention in recent years,especially with the development of precision farming and sensor *** achieve high performance and low cost,perception tasks are of great *** this study,a low-cost and high-safety method was proposed for field road recognition in unmanned agricultural *** approach of this study utilized point clouds,with low-resolution Lidar point clouds as inputs,generating high-resolution point clouds and Bird's Eye View(BEV)images that were encoded with several basic *** a BEV representation,road detection was reduced to a single-scale problem that could be addressed with an improved U-Net++neural *** enhancements were proposed for U-Net++:1)replacing the convolutional kernel in the original U-Net++ with an Asymmetric Convolution Block(ACBlock);2)adding a multi-branch Asymmetric Dilated Convolutional Block(MADC)in the highest semantic information layer;3)adding an Attention Gate(AG)model to the long-skip-connection in the decoding *** results of experiments of this study showed that our algorithm achieved a Mean Intersection Over Union of 96.54% on the 16-channel point clouds,which was 7.35 percentage points higher than U-Net++.Furthermore,the average processing time of the model was about 70 ms,meeting the time requirements of unmanned driving in agricultural *** proposed method of this study can be applied to enhance the perception ability of unmanned agricultural machinery thereby increasing the safety of field road driving.

关键词： image segmentation unmanned agricultural machinery field roads point cloud super-resolution point cloud bird’s eye view

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DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination

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International Journal of agricultural and Biological Engineering 2023年第3期16卷 169-179页

作者： Yuzhen Xiao Guozhao Mo Xiya Xiong Jiawen Pan Bingbing Hu Caicong Wu Weixin Zhai College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Kunlun Beidou Intelligence Technologies Co. Ltd.Beijing 102200China

Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural *** improve the accuracy of the field-road segmentation,this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called ***-XGBoost takes only a small amount of agricultural machine trajectory features as ***,the model adopted the dual feature extraction method we designed to rapidly expand the number of features and then adequately extract local trajectory features by the time window and feature extraction ***,the model applies the recursive feature elimination algorithm to eliminate redundant features from the perspective of the model segmentation effect and thus reduce the computational consumption of model ***,it trains XGBoost to complete the trajectory *** evaluate the effectiveness of DR-XGBoost,we conducted a series of experiments on a real trajectory dataset of agricultural *** model achieves a 98.2%Macro-F1 score on the dataset,which is 10.9%higher than the previous *** proposal of DR-XGBoost fills the knowledge gap of trajectory feature extraction for agricultural machinery and provides a reasonable and effective feature selection scheme for the field-road segmentation problem.

关键词： trajectory segmentation feature extraction recursive feature elimination time window XGBoost

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