International Journal of Agricultural and Biological Engineering最新文献

{"title":"Design and test of the bilateral throwing soil-covering device for straw mulching machine in orchards","authors":"Xinhua Zhu, Xianghe Gao, Xudong Li, Shaojie Xu","doi":"10.25165/j.ijabe.20231601.7010","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.7010","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"54 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72426716","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":"Multi-class detection of cherry tomatoes using improved YOLOv4-Tiny","authors":"Fu Zhang, Zijun Chen, Shaukat Ali, Ning Yang, Sanling Fu, Yakun Zhang","doi":"10.25165/j.ijabe.20231602.7744","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.7744","url":null,"abstract":": The rapid and accurate detection of cherry tomatoes is of great significance to realizing automatic picking by robots. However, so far, cherry tomatoes are detected as only one class for picking. Fruits occluded by branches or leaves are detected as pickable objects, which may cause damage to the plant or robot end-effector during picking. This study proposed the Feature Enhancement Network Block (FENB) based on YOLOv4-Tiny to solve the above problem. Firstly, according to the distribution characteristics and picking strategies of cherry tomatoes, cherry tomatoes were divided into four classes in the nighttime, and daytime included not occluded, occluded by branches, occluded by fruits, and occluded by leaves. Secondly, the CSPNet structure with the hybrid attention mechanism was used to design the FENB, which pays more attention to the effective features of different classes of cherry tomatoes while retaining the original features. Finally, the Feature Enhancement Network (FEN) was constructed based on the FENB to enhance the feature extraction ability and improve the detection accuracy of YOLOv4-Tiny. The experimental results show that under the confidence of 0.5, average precision (AP) of non-occluded, branch-occluded, fruit-occluded, and leaf-occluded fruit over the day test images were 95.86%, 92.59%, 89.66%, and 84.99%, respectively, which were 98.43%, 95.62%, 95.50%, and 89.33% on the night test images, respectively. The mean Average Precision (mAP) of four classes over the night test set was higher (94.72%) than that of the day (90.78%), which were both better than YOLOv4 and YOLOv4-Tiny. It cost 32.22 ms to process a 416×416 image on the GPU. The model size was 39.34 MB. Therefore, the proposed model can provide a practical and feasible method for the multi-class detection of cherry tomatoes.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"5 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72589477","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":"Nitrogen use in double cropping soybean with non-fertilized winter oilseed crops","authors":"Ronghao Liu, Stephen Gregg, A. Garcia y Garcia","doi":"10.25165/j.ijabe.20231602.7547","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.7547","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"48 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80167016","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":"Downwash airflow field distribution characteristics and their effect on the spray field distribution of the DJI T30 six-rotor plant protection UAV","authors":"Haiyan Zhang, Sheng Wen, Chunling Chen, Q. Liu, Tongyu Xu, Shengde Chen, Y. Lan","doi":"10.25165/j.ijabe.20231602.8094","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.8094","url":null,"abstract":": Spray characteristics are the fundamental factors that affect droplet transportation downward, deposition, and drift. The downwash airflow field of the Unmanned Aviation Vehicle (UAV) primarily influences droplet deposition and drift by changing the spray characteristics. This study focused mainly on the effect of the downwash airflow field of the UAV and nozzle position on the droplet spatial distribution and velocity distribution, which are two factors of spray characteristics. To study the abovementioned characteristics, computational fluid dynamics based on the lattice Boltzmann method (LBM) was used to simulate the downwash airflow field of the DJI T30 six-rotor plant protection UAV at different rotor rotational speeds (1000-1800 r/min). A particle image velocimetry system (PIV) was utilized to record the spray field with the downwash airflow field at different rotational speeds of rotors (0-1800 r/min) or different nozzle positions (0, 0.20 m, 0.35 m, and 0.50 m from the motor). The simulation and experimental results showed that the rotor downwash airflow field exhibited the ‘ dispersion-shrinkage-redispersion’ development rule. In the initial dispersion stage of rotor airflow, there were obvious high-vorticity and low-vorticity regions in the rotor downwash airflow field. Moreover, the low-vorticity region was primarily concentrated below the motor, and the high-vorticity region was mainly focused in the middle area of the rotors. Additionally, the Y -direction airflow velocity fluctuated at 0.4-1.2 m under the rotor. When the rotor airflow developed to 3.2 m below the rotor, the Y - direction airflow velocity showed a slight decrease. Above 3.2 m from the rotor, the Y -direction airflow velocity started to drastically decrease. Therefore, it is recommended that the DJI T30 plant protection UAV should not exceed 3.2 m in flight height during field spraying operations. The rotor downwash airflow field caused the nozzle atomization angle, droplet concentration, and spray field width to decrease while increasing the vortex scale in the spray field when the rotor system was activated. Moreover, the increase in rotor rotational speed promoted the abovementioned trend. When the nozzle was installed in various radial locations below the rotor, the droplet spatial distribution and velocity distribution were completely different. When the nozzle was installed directly below the motor, the droplet spatial distribution and velocity distribution were relatively symmetrical. When the nozzle was installed at 0.20 m and 0.35 m from the motor, the droplets clearly moved toward the right under the induction of stronger rotor vortices. This resulted in a higher droplet concentration in the right-half spray field. However, the droplet moved toward the left when the nozzle was installed in the rotor tip. For four nozzle positions, when the nozzle was installed at 0 or 0.20 m from the motor, the droplet average velocity was much higher. However, the droplet","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"218 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80175732","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":"Investigation of distribution uniformity of distributor for biogas slurry application based on CFD analysis","authors":"Jingjing Fu, Yongsheng Chen, Binxing Xu, Biao Ma, Pengjun Wang, A. Wu, Mingjiang Chen","doi":"10.25165/j.ijabe.20231601.7460","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.7460","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"16 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86522500","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":"Airflow distribution law of multi-branch pipe of pneumatic rice direct seeder based on dimensional analysis","authors":"W. Qin, Zaimang Wang, Minghua Zhang, Siyu He, Xuguo Wang, Youcong Jiang, Zishun Huang, Ying Zang","doi":"10.25165/j.ijabe.20231601.7663","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.7663","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"20 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85325910","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":"Method for the automatic recognition of cropland headland images based on deep learning","authors":"Yujie Qiao, Hui Liu, Zhijun Meng, Jingping Chen, Luyao Ma","doi":"10.25165/j.ijabe.20231602.6195","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.6195","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"77 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83921014","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":"Evaluation of the nonmarket value of livestock and poultry feces returning to farmland utilization using CVM in Heilongjiang, China","authors":"Jiajie Shang, Yongtao Xie, Lifeng Guo, Jinxia Fan, Hongxin Liu","doi":"10.25165/j.ijabe.20231602.7588","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.7588","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"15 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78184659","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":"Skeleton extraction and pose estimation of piglets using ZS-DLC-PAF","authors":"Chengqi Liu, Haijian Ye, Shuhan Lu, Zhan Tang, Zhao Bai, Lei Diao, Longhe Wang, Lin Li","doi":"10.25165/j.ijabe.20231603.6930","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.6930","url":null,"abstract":"The accurate identification of various postures in the daily life of piglets that are directly reflected by their skeleton morphology is necessary to study the behavioral characteristics of pigs. Accordingly, this study proposed a novel approach for the skeleton extraction and pose estimation of piglets. First, an improved Zhang-Suen (ZS) thinning algorithm based on morphology was used to establish the chain code mechanism of the burr and the redundant information deletion templates to achieve a single-pixel width extraction of pig skeletons. Then, body nodes were extracted on the basis of the improved DeepLabCut (DLC) algorithm, and a part affinity field (PAF) was added to realize the connection of body nodes, and consequently, construct a database of pig behavior and postures. Finally, a support vector machine was used for pose matching to recognize the main behavior of piglets. In this study, 14 000 images of piglets with different types of behavior were used in posture recognition experiments. Results showed that the improved algorithm based on ZS-DLC-PAF achieved the best thinning rate compared with those of distance transformation, medial axis transformation, morphology refinement, and the traditional ZS algorithm. The node tracking accuracy reached 85.08%, and the pressure test could accurately detect up to 35 nodes of 5 pigs. The average accuracy of posture matching was 89.60%. This study not only realized the single-pixel extraction of piglets’ skeletons but also the connection among the different behavior body nodes of individual sows and multiple piglets. Furthermore, this study established a database of pig posture behavior, which provides a reference for studying animal behavior identification and classification and anomaly detection. Keywords: piglets, skeleton extraction, pose estimation, Zhang-Suen, DeepLabCut, Part affinity field DOI: 10.25165/j.ijabe.20231603.6930 Citation: Liu C Q, Ye H J, Lu S H, Tang Z, Bai Z, Diao L, et al. Skeleton extraction and pose estimation of piglets using ZS-DLC-PAF. Int J Agric & Biol Eng, 2023; 16(3): 180–193.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"15 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":"135357309","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":"Vision-based measuring method for individual cow feed intake using depth images and a Siamese network","authors":"Xinjie Wang, Baisheng Dai, Xiaoli Wei, Weizheng Shen, Yonggen Zhang, Benhai Xiong","doi":"10.25165/j.ijabe.20231603.7985","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7985","url":null,"abstract":"Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows, which can also evaluate the utilization rate of pasture feed. To achieve an automatic and non-contact measurement of feed intake, this paper proposes a method for measuring the feed intake of cows based on computer vision technology with a Siamese network and depth images. An automated data acquisition system was first designed to collect depth images of feed piles and constructed a dataset with 24 150 samples. A deep learning model based on the Siamese network was then constructed to implement non-contact measurement of feed intake for dairy cows by training with collected data. The experimental results show that the mean absolute error (MAE) and the root mean square error (RMSE) of this method are 0.100 kg and 0.128 kg in the range of 0-8.2 kg respectively, which outperformed existing works. This work provides a new idea and technology for the intelligent measuring of dairy cow feed intake. Keywords: computer vision, Siamese network, cow feed intake, depth image, precision livestock farming DOI: 10.25165/j.ijabe.20231603.7985 Citation: Wang X J, Dai B S, Wei X L, Shen W Z, Zhang Y G, Xiong B H. Vision-based measuring method for individual cow feed intake using depth images and a Siamese network. Int J Agric & Biol Eng, 2023; 16(3): 233–239.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"15 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":"135358864","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}