Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural machinery. To improve the accuracy of the field-road segmentation, this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called DR-XGBoost. DR-XGBoost takes only a small amount of agricultural machine trajectory features as input. Firstly, 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 operator. Secondly, 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 training. Thirdly, it trains XGBoost to complete the trajectory segmentation. To evaluate the effectiveness of DR-XGBoost, we conducted a series of experiments on a real trajectory dataset of agricultural machines. The model achieves a 98.2% Macro-F1 score on the dataset, which is 10.9% higher than the previous state-of-art. The 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. Keywords: trajectory segmentation, feature extraction, recursive feature elimination, time window, XGBoost DOI: 10.25165/j.ijabe.20231603.8187 Citation: Xiao Y Z, Mo G Z, Xiong X Y, Pan J W, Hu B B, Wu C C, et al. DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination. Int J Agric & Biol Eng, 2023; 2023; 16(3): 169–179.
{"title":"DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination","authors":"Yuzhen Xiao, Guozhao Mo, Xiya Xiong, Jiawen Pan, Bingbing Hu, Caicong Wu, Weixin Zhai","doi":"10.25165/j.ijabe.20231603.8187","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.8187","url":null,"abstract":"Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural machinery. To improve the accuracy of the field-road segmentation, this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called DR-XGBoost. DR-XGBoost takes only a small amount of agricultural machine trajectory features as input. Firstly, 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 operator. Secondly, 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 training. Thirdly, it trains XGBoost to complete the trajectory segmentation. To evaluate the effectiveness of DR-XGBoost, we conducted a series of experiments on a real trajectory dataset of agricultural machines. The model achieves a 98.2% Macro-F1 score on the dataset, which is 10.9% higher than the previous state-of-art. The 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. Keywords: trajectory segmentation, feature extraction, recursive feature elimination, time window, XGBoost DOI: 10.25165/j.ijabe.20231603.8187 Citation: Xiao Y Z, Mo G Z, Xiong X Y, Pan J W, Hu B B, Wu C C, et al. DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination. Int J Agric & Biol Eng, 2023; 2023; 16(3): 169–179.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135357560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231604.8364
Wenze Hu, Samuel Oliver Wane, Junke Zhu, Dongsheng Li, Qing Zhang, Xiaoting Bie, Yubin Lan
Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. Keywords: deep learning, weed detection, weed classification, image segmentation, Convolutional Neural Network, image processing DOI: 10.25165/j.ijabe.20231604.8364 Citation: Hu W Z, Wane S O, Zhu J K, Li D S, Zhang Q, Bie X T, et al. Review of deep learning-based weed identification in crop fields. Int J Agric & Biol Eng, 2023; 16(4): 1-10.
{"title":"Review of deep learning-based weed identification in crop fields","authors":"Wenze Hu, Samuel Oliver Wane, Junke Zhu, Dongsheng Li, Qing Zhang, Xiaoting Bie, Yubin Lan","doi":"10.25165/j.ijabe.20231604.8364","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231604.8364","url":null,"abstract":"Automatic weed identification and detection are crucial for precision weeding operations. In recent years, deep learning (DL) has gained widespread attention for its potential in crop weed identification. This paper provides a review of the current research status and development trends of weed identification in crop fields based on DL. Through an analysis of relevant literature from both within and outside of China, the author summarizes the development history, research progress, and identification and detection methods of DL-based weed identification technology. Emphasis is placed on data sources and DL models applied to different technical tasks. Additionally, the paper discusses the challenges of time-consuming and laborious dataset preparation, poor generality, unbalanced data categories, and low accuracy of field identification in DL for weed identification. Corresponding solutions are proposed to provide a reference for future research directions in weed identification. Keywords: deep learning, weed detection, weed classification, image segmentation, Convolutional Neural Network, image processing DOI: 10.25165/j.ijabe.20231604.8364 Citation: Hu W Z, Wane S O, Zhu J K, Li D S, Zhang Q, Bie X T, et al. Review of deep learning-based weed identification in crop fields. Int J Agric & Biol Eng, 2023; 16(4): 1-10.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135660077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231603.7842
Fenxia Han, Mingming Zhu, Yi Xing, Hanjun Ma
{"title":"Improvement of gelation properties of myofibrillar proteins from porcine longissimus dorsi muscle through microwave combined with air convection thawing treatment","authors":"Fenxia Han, Mingming Zhu, Yi Xing, Hanjun Ma","doi":"10.25165/j.ijabe.20231603.7842","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7842","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135361584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231601.7309
Luyu Ding, L. E, Yang Lyu, Chunxia Yao, Qifeng Li, Shiwei Huang, Weihong Ma, Ligen Yu, Ronghua Gao
{"title":"Estimating the air exchange rates in naturally ventilated cattle houses using Bayesian-optimized GBDT","authors":"Luyu Ding, L. E, Yang Lyu, Chunxia Yao, Qifeng Li, Shiwei Huang, Weihong Ma, Ligen Yu, Ronghua Gao","doi":"10.25165/j.ijabe.20231601.7309","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.7309","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87227606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231601.7574
Haoyu Yang, J. Wu, Chenyao Guo, Hang Li, Zhe Wu
{"title":"Effects of geotextile envelope and perforations on the performance of corrugated drain pipes","authors":"Haoyu Yang, J. Wu, Chenyao Guo, Hang Li, Zhe Wu","doi":"10.25165/j.ijabe.20231601.7574","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.7574","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85249241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231601.7810
Yan Li, G. Che, Lin Wan, Qilin Zhang, Tianqi Qu, F. Zhao
{"title":"Characteristics and mathematical models of the thin-layer drying of paddy rice with low-pressure superheated steam","authors":"Yan Li, G. Che, Lin Wan, Qilin Zhang, Tianqi Qu, F. Zhao","doi":"10.25165/j.ijabe.20231601.7810","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.7810","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84603902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231602.7423
Zhifang Zhu, Guohuan Wu, Bingliang Ye, Yongchang Zhang
: In the previous research, the seedling pick-up mechanism of the planetary gear train with incomplete eccentric circular gear and non-circular gears for vegetable plug seedlings still has two shortcomings. One is that not enough seedling pick-up depth leads to a low success ratio of seedling pick-up at high rotation speeds, the other is that the smaller seedling pushing angle results in poor seedling pushing effect. Therefore, the reverse design of the seedling pick-up mechanism based on its motion trajectory was carried out. The local trajectory of seedling pick-up and seedling pushing sections was adjusted to obtain the theoretical motion trajectory of the seedling pick-up mechanism. The cubic non-uniform B-spline curve was used to fit the adjusted trajectory. A novel seedling pick-up mechanism of the planetary gear train with non-circular gears was proposed, including three combined non-circular gears, four non-circular gears, one planetary carrier, and two seedling pick-up arms. The reverse design model of the mechanism was established. The analysis and design software of the mechanism was developed to obtain the mechanism parameters meeting design requirements. The virtual prototype of the mechanism was established and its physical prototype was manufactured. Through the virtual motion simulation and high-speed photographic kinematics bench tests of the mechanism, the kinematic model and results of reverse design of the mechanism were verified, with the kinematic performances of the mechanism prototype studied. The seedling pick-up tests of the mechanism were conducted in the laboratory. The success ratios of seedling pick-up were 94.2%, 95.6% and 90.2% while the seedling pick-up efficiencies of the mechanism were 60, 80 and 100 plants per minute per row, respectively. Besides, the seedling pushing effect was improved mush because of the greater seedling pushing angle. The seedling pick-up mechanism through revise design is of high value to be applied in the practical vegetable plug seedling transplanters
{"title":"Reverse design and tests of vegetable plug seedling pick-up mechanism of planetary gear train with non-circular gears","authors":"Zhifang Zhu, Guohuan Wu, Bingliang Ye, Yongchang Zhang","doi":"10.25165/j.ijabe.20231602.7423","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.7423","url":null,"abstract":": In the previous research, the seedling pick-up mechanism of the planetary gear train with incomplete eccentric circular gear and non-circular gears for vegetable plug seedlings still has two shortcomings. One is that not enough seedling pick-up depth leads to a low success ratio of seedling pick-up at high rotation speeds, the other is that the smaller seedling pushing angle results in poor seedling pushing effect. Therefore, the reverse design of the seedling pick-up mechanism based on its motion trajectory was carried out. The local trajectory of seedling pick-up and seedling pushing sections was adjusted to obtain the theoretical motion trajectory of the seedling pick-up mechanism. The cubic non-uniform B-spline curve was used to fit the adjusted trajectory. A novel seedling pick-up mechanism of the planetary gear train with non-circular gears was proposed, including three combined non-circular gears, four non-circular gears, one planetary carrier, and two seedling pick-up arms. The reverse design model of the mechanism was established. The analysis and design software of the mechanism was developed to obtain the mechanism parameters meeting design requirements. The virtual prototype of the mechanism was established and its physical prototype was manufactured. Through the virtual motion simulation and high-speed photographic kinematics bench tests of the mechanism, the kinematic model and results of reverse design of the mechanism were verified, with the kinematic performances of the mechanism prototype studied. The seedling pick-up tests of the mechanism were conducted in the laboratory. The success ratios of seedling pick-up were 94.2%, 95.6% and 90.2% while the seedling pick-up efficiencies of the mechanism were 60, 80 and 100 plants per minute per row, respectively. Besides, the seedling pushing effect was improved mush because of the greater seedling pushing angle. The seedling pick-up mechanism through revise design is of high value to be applied in the practical vegetable plug seedling transplanters","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77065587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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