Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231601.7639
Yue-li Jiang, Qiuying Huang, Guoshu Wei, Zhongjun Gong, Tong Li, J. Miao, Ruijie Lu, Shiqiong Mei, Xueqin Wang, Y. Duan, Yu-Qing Wu, Chuantao Lu
{"title":"Effects of yellow and green light stress on emergence, feeding and mating of Anomala corpulenta Motschulsky and Holotrichia parallela Motschulsky (Coleoptera: Scarabaeidae)","authors":"Yue-li Jiang, Qiuying Huang, Guoshu Wei, Zhongjun Gong, Tong Li, J. Miao, Ruijie Lu, Shiqiong Mei, Xueqin Wang, Y. Duan, Yu-Qing Wu, Chuantao Lu","doi":"10.25165/j.ijabe.20231601.7639","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.7639","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"23 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86935676","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.20231601.6581
Lifeng Xu, Zhongzhuo Yang, Zusheng Huang, Weilong Ding, G. Buck-Sorlin
{"title":"Effects of flight parameters for plant protection UAV on droplets deposition rate based on a 3D simulation approach","authors":"Lifeng Xu, Zhongzhuo Yang, Zusheng Huang, Weilong Ding, G. Buck-Sorlin","doi":"10.25165/j.ijabe.20231601.6581","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.6581","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"46 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80581357","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.20231601.6918
Chao Meng, Wei Yang, Xinjian Ren, D. Wang, Minzan Li
{"title":"In-situ soil texture classification and physical clay content measurement based on multi-source information fusion","authors":"Chao Meng, Wei Yang, Xinjian Ren, D. Wang, Minzan Li","doi":"10.25165/j.ijabe.20231601.6918","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.6918","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"125 1 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74853254","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}
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":"132 1","pages":"0"},"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.20231603.7812
Xinping Sun, Hua Li, Xindan Qi, Dinghao Feng, Jianqi Zhou, Yongjian Wang, Samuel Mbugua Nyambura, Xiaoyu Zhang, Xi Chen
This study aimed to optimize a three-row air-suction Brassica chinensis precision metering device to improve the low seeding performance. ANSYS 17.0 Software was used to analyze the effect of different numbers of suction holes and different suction hole structures on the airflow field. It was found that a suction hole number of 60 was beneficial to the flow field stability and a conical hole structure was beneficial to the adsorption of seeds. Box-Behnken design experiments were carried out with negative pressure, rotational speed, and hole diameter as the experimental factors. The optimal parameter combination was achieved when the negative pressure was 3.96 kPa, the rotational speed of the seeding plate was 1.49 rad/s and the hole diameter was 1.10 mm. The qualification rate of inner, middle, and outer rings were 87.580%, 90.548%, and 90.117%, respectively, and the miss seeding rate of inner, middle, and outer rings were 10.915%, 7.139%, and 5.920%, respectively. Keywords: Brassica chinensis, metering device, airflow field, Box-Behnken design DOI: 10.25165/j.ijabe.20231603.7812 Citation: Sun X P, Li H, Qi X D, Feng D H, Zhou J Q, Wang Y J, et al. Optimization of a three-row air-suction Brassica chinensis precision metering device based on CFD-DEM coupling simulation. Int J Agric & Biol Eng, 2023; 16(3): 130–142.
{"title":"Optimization of a three-row air-suction Brassica chinensis precision metering device based on CFD-DEM coupling simulation","authors":"Xinping Sun, Hua Li, Xindan Qi, Dinghao Feng, Jianqi Zhou, Yongjian Wang, Samuel Mbugua Nyambura, Xiaoyu Zhang, Xi Chen","doi":"10.25165/j.ijabe.20231603.7812","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7812","url":null,"abstract":"This study aimed to optimize a three-row air-suction Brassica chinensis precision metering device to improve the low seeding performance. ANSYS 17.0 Software was used to analyze the effect of different numbers of suction holes and different suction hole structures on the airflow field. It was found that a suction hole number of 60 was beneficial to the flow field stability and a conical hole structure was beneficial to the adsorption of seeds. Box-Behnken design experiments were carried out with negative pressure, rotational speed, and hole diameter as the experimental factors. The optimal parameter combination was achieved when the negative pressure was 3.96 kPa, the rotational speed of the seeding plate was 1.49 rad/s and the hole diameter was 1.10 mm. The qualification rate of inner, middle, and outer rings were 87.580%, 90.548%, and 90.117%, respectively, and the miss seeding rate of inner, middle, and outer rings were 10.915%, 7.139%, and 5.920%, respectively. Keywords: Brassica chinensis, metering device, airflow field, Box-Behnken design DOI: 10.25165/j.ijabe.20231603.7812 Citation: Sun X P, Li H, Qi X D, Feng D H, Zhou J Q, Wang Y J, et al. Optimization of a three-row air-suction Brassica chinensis precision metering device based on CFD-DEM coupling simulation. Int J Agric & Biol Eng, 2023; 16(3): 130–142.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135358842","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":"265 1","pages":"0"},"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.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":"46 1","pages":"0"},"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}
{"title":"Kinematic synthesis and simulation of a vegetable pot seedling transplanting mechanism with four exact task poses","authors":"Liang Sun, Haoming Xu, Yuzhu Zhou, Jiahao Shen, Gaohong Yu, Huafeng Hu, Yuejun Miao","doi":"10.25165/j.ijabe.20231602.6739","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.6739","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"35 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88468151","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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