International Journal of Agricultural and Biological Engineering最新文献_第5页

Effects of yellow and green light stress on emergence, feeding and mating of Anomala corpulenta Motschulsky and Holotrichia parallela Motschulsky (Coleoptera: Scarabaeidae) 黄光和绿光胁迫对褐斑金龟和褐斑金龟羽化、取食和交配的影响(鞘翅目:金龟科)

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 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

引用次数: 0

Effects of flight parameters for plant protection UAV on droplets deposition rate based on a 3D simulation approach 基于三维仿真方法的植保无人机飞行参数对液滴沉积速率的影响

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231601.6581

Lifeng Xu, Zhongzhuo Yang, Zusheng Huang, Weilong Ding, G. Buck-Sorlin

引用次数: 2

sugarcane, single-bud sugarcane seed, disc-type seeding device, seeding uniformity 甘蔗，单芽甘蔗种子，圆盘式播种器，播种均匀

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231602.7067

Yulong Feng, Xuchao Yin, Hairong Jin, Wenyu Tong, Xiaofeng Ning

引用次数: 0

In-situ soil texture classification and physical clay content measurement based on multi-source information fusion 基于多源信息融合的原位土壤质地分类与物理粘土含量测量

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231601.6918

Chao Meng, Wei Yang, Xinjian Ren, D. Wang, Minzan Li

引用次数: 0

DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination DR-XGBoost:一种基于双特征提取和递归特征消除的野外道路分割XGBoost模型

2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231603.8187

Yuzhen Xiao, Guozhao Mo, Xiya Xiong, Jiawen Pan, Bingbing Hu, Caicong Wu, Weixin Zhai

{"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}

引用次数: 0

Optimization of a three-row air-suction Brassica chinensis precision metering device based on CFD-DEM coupling simulation 基于CFD-DEM耦合仿真的三排空气吸式芸苔精密计量装置优化

2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 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

{"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}

引用次数: 0

Automatic lameness detection in dairy cows based on machine vision 基于机器视觉的奶牛跛行自动检测

2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231603.8097

Zongwei Jia, Xuhui Yang, Zhi Wang, Ruirui Yu, Ruibin Wang

引用次数: 0

Improvement of gelation properties of myofibrillar proteins from porcine longissimus dorsi muscle through microwave combined with air convection thawing treatment 微波联合空气对流解冻改善猪背最长肌纤维蛋白凝胶化性能

2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231603.7842

Fenxia Han, Mingming Zhu, Yi Xing, Hanjun Ma

引用次数: 0

Kinematic synthesis and simulation of a vegetable pot seedling transplanting mechanism with four exact task poses 具有四种精确任务姿态的蔬菜钵苗移栽机构的运动学综合与仿真

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231602.6739

Liang Sun, Haoming Xu, Yuzhu Zhou, Jiahao Shen, Gaohong Yu, Huafeng Hu, Yuejun Miao

引用次数: 0

Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters 基于人工神经网络的水稻联合收割机维修保养成本估算模型

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231602.5931

A. Numsong, J. Posom, S. Chuan-udom

{"title":"Artificial neural network-based repair and maintenance cost estimation model for rice combine harvesters","authors":"A. Numsong, J. Posom, S. Chuan-udom","doi":"10.25165/j.ijabe.20231602.5931","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.5931","url":null,"abstract":": This research proposes an artificial neural network (ANN)-based repair and maintenance (R&M) cost estimation model for agricultural machinery. The proposed ANN model can achieve high estimation accuracy with small data requirement. In the study, the proposed ANN model is implemented to estimate the R&M costs using a sample of locally-made rice combine harvesters. The model inputs are geographical regions, harvest area, and curve fitting coefficients related to historical cost data; and the ANN output is the estimated R&M cost. Multilayer feed-forward is adopted as the processing algorithm and Levenberg-Marquardt backpropagation learning as the training algorithm. The R&M costs are estimated using the ANN-based model, and results are compared with those of conventional mathematical estimation model. The results reveal that the percentage error between the conventional and ANN-based estimation models is below 1%, indicating the proposed ANN model’s high predictive accuracy. The proposed ANN-based model is useful for setting the service rates of agricultural machinery, given the significance of R&M cost in profitability. The novelty of this research lies in the use of curve-fitting coefficients in the ANN-based estimation model to improve estimation accuracy. Besides, the proposed ANN model could be further developed into web-based applications using a programming language to enable ease of use and greater user accessibility. Moreover, with minor modifications, the ANN estimation model is also applicable to other geographical areas and tractors or combine harvesters of different countries of origin.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"51 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86339653","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}

引用次数: 0