{"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}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231602.6195
Yujie Qiao, Hui Liu, Zhijun Meng, Jingping Chen, Luyao Ma
{"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}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231602.7588
Jiajie Shang, Yongtao Xie, Lifeng Guo, Jinxia Fan, Hongxin Liu
{"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}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231603.6930
Chengqi Liu, Haijian Ye, Shuhan Lu, Zhan Tang, Zhao Bai, Lei Diao, Longhe Wang, Lin Li
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.
{"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}
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.
{"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}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231603.7613
Yuanyuan Shao, Hongdong Zhang, Guantao Xuan, Tao Zhang, Xianlu Guan, Fuhui Wang
{"title":"Simulation and experiment of a transplanting mechanism for sweet potato seedlings with ‘boat-bottom’ transplanting trajectory","authors":"Yuanyuan Shao, Hongdong Zhang, Guantao Xuan, Tao Zhang, Xianlu Guan, Fuhui Wang","doi":"10.25165/j.ijabe.20231603.7613","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7613","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"223 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":"135361579","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.20231603.6293
Qizhi Yang, Lei Shi, Aiping Shi, Mingsheng He, Xiaoqi Zhao, Li Zhang, Min Addy
{"title":"Determination of key soil characteristic parameters using angle of repose and direct shear stress test","authors":"Qizhi Yang, Lei Shi, Aiping Shi, Mingsheng He, Xiaoqi Zhao, Li Zhang, Min Addy","doi":"10.25165/j.ijabe.20231603.6293","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.6293","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"12 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":"135361599","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.20231603.6863
Attique ur Rehman, Abdul Razzaq, Adnan Altaf, Salman Qadri, Aamir Hussain, Ali Nawaz Khan, None Tausif-ur-Rehman, Zaid Sarfraz
{"title":"Indoor smart farming by inducing artificial climate for high value-added crops in optimal duration","authors":"Attique ur Rehman, Abdul Razzaq, Adnan Altaf, Salman Qadri, Aamir Hussain, Ali Nawaz Khan, None Tausif-ur-Rehman, Zaid Sarfraz","doi":"10.25165/j.ijabe.20231603.6863","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.6863","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"10 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":"135361621","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}
The efficient and effective application of fertilizers to crops is a major challenge. Conventionally, constant rate or equal dose of fertilizer is applied to each plant. Constant rate fertilizer application across entire field can result in over or under incorporation of nutrients. Fertilizer application is influenced by soil parameters as well as geographical variation in the field. The nutrient management depends on selection of nutrient, application rate and placement of nutrient at the optimal distance from the crop and soil depth. Variable rate technology (VRT) is an input application technology that allows for the application of inputs at a certain rate, time, and place based on soil properties and spatial variation in the field or plants. There are two approaches for implementing VRT, one is sensor based and another is map based. The sensor based approach; with suitable sensors, measures the soil and crop characteristics on-the-go calculating the amount of nutrients required per unit area/plant and micro controlling unit which uses suitable algorithms for controlling the flow of fertilizer with required amount of nutrient. In map based approach; Grid sampling and soil analysis are used to create a prescription map. According to the soil and crop conditions, the microcontroller regulates the desired application rate. The sensor-based VRT system includes a fertilizer tank, sensors, GPS, microcontroller, actuators, and other components, whereas the map-based system does not require an on-the-go sensor. Both approaches of VRT for fertilizer application in orchards and field crops are reviewed in this paper. The use of this advance technology surely increases the fertilizer use efficiency; improve crop yield and profitability with reduced environment impacts. Keywords: nutrient sensor, prescription map, spatial variation, VRT Citation: Pawase P P, Nalawade S M, Bhanage G B, Walunj A A, Kadam P B, Durgude A G, Patil M R. Variable rate fertilizer application technology for nutrient management: A review. Int J Agric & Biol Eng, 2023; 16(4): 11-19.
对作物进行高效和有效的施肥是一项重大挑战。按惯例，每株植物施用等量或等量肥料。在整个田地中等量施肥会导致养分吸收过多或不足。施肥受土壤参数和田间地理变化的影响。养分管理取决于养分的选择、施肥量以及与作物的最佳距离和土壤深度。可变速率技术(VRT)是一种输入应用技术，它允许根据田地或植物的土壤性质和空间变化，以一定的速率、时间和地点施用输入。VRT的实现有两种方法，一种是基于传感器的，另一种是基于地图的。基于传感器的方法;通过合适的传感器，测量土壤和作物特性，计算每单位面积/植物所需的养分量，微控制单元使用合适的算法来控制所需养分量的肥料流动。在基于地图的方法;网格采样和土壤分析用于创建处方图。根据土壤和作物条件，单片机调节所需的施用量。基于传感器的VRT系统包括肥料罐、传感器、GPS、微控制器、执行器和其他组件，而基于地图的系统不需要移动传感器。本文综述了VRT在果园和大田作物施肥中的两种方法。这一先进技术的应用无疑提高了肥料的利用效率;在减少环境影响的同时提高作物产量和盈利能力。引用本文:Pawase P P, Nalawade S M, Bhanage G B, Walunj A A, Kadam P B, dulude A G, Patil M r。农业与生物工程学报，2023;16(4): 11-19。
{"title":"Variable rate fertilizer application technology for nutrient management: A review","authors":"Pranav Pramod Pawase, Sachin Madhukar Nalawade, Girishkumar Balasaheb Bhanage, Avdhoot Ashok Walunj, Pravin Bhaskar Kadam, Anil G Durgude, Mahesh R Patil","doi":"10.25165/j.ijabe.20231604.7671","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231604.7671","url":null,"abstract":"The efficient and effective application of fertilizers to crops is a major challenge. Conventionally, constant rate or equal dose of fertilizer is applied to each plant. Constant rate fertilizer application across entire field can result in over or under incorporation of nutrients. Fertilizer application is influenced by soil parameters as well as geographical variation in the field. The nutrient management depends on selection of nutrient, application rate and placement of nutrient at the optimal distance from the crop and soil depth. Variable rate technology (VRT) is an input application technology that allows for the application of inputs at a certain rate, time, and place based on soil properties and spatial variation in the field or plants. There are two approaches for implementing VRT, one is sensor based and another is map based. The sensor based approach; with suitable sensors, measures the soil and crop characteristics on-the-go calculating the amount of nutrients required per unit area/plant and micro controlling unit which uses suitable algorithms for controlling the flow of fertilizer with required amount of nutrient. In map based approach; Grid sampling and soil analysis are used to create a prescription map. According to the soil and crop conditions, the microcontroller regulates the desired application rate. The sensor-based VRT system includes a fertilizer tank, sensors, GPS, microcontroller, actuators, and other components, whereas the map-based system does not require an on-the-go sensor. Both approaches of VRT for fertilizer application in orchards and field crops are reviewed in this paper. The use of this advance technology surely increases the fertilizer use efficiency; improve crop yield and profitability with reduced environment impacts. Keywords: nutrient sensor, prescription map, spatial variation, VRT Citation: Pawase P P, Nalawade S M, Bhanage G B, Walunj A A, Kadam P B, Durgude A G, Patil M R. Variable rate fertilizer application technology for nutrient management: A review. Int J Agric & Biol Eng, 2023; 16(4): 11-19.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"20 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":"135659253","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：