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":"40 1","pages":""},"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}
Pub Date : 2023-01-01DOI: 10.25165/j.ijabe.20231603.7042
Xiaoyu Li, Yuefeng Du, Enrong Mao, Yan’an Zhang, Lei Liu, Dafang Guo
Kernel broken rate is an important index to evaluate the maize kernel direct harvesting quality. In view of the problem of the high kernel broken rate in the present maize harvester, a new threshing cylinder was designed in this study. This device utilized rasp bar to achieve low damaged maize ears threshing. In order to determine the structure and working parameters of threshing device, the "crop-crop" contact model and the "crop-mechanical" interaction system were established and analyzed based on the discrete element method first, and the mathematical expressions of the related kinematic response of maize kernel under the external force were obtained. Then, the structure parameters of rasp bar were studied through EDEM simulation experiment, and the working parameters were determined through test-bed experiment. Finally, the simulation experiment results and test-bed experiment results were verified through field experiment. The results showed that when the threshing cylinder speed was 356 r/min, the concave clearance was 40 mm, the installation distance of rasp bar was 250 mm with 50Mn steel, and the feeding amount was 8 kg/s, the kernel broken rate was 1.93%, which satisfied the requirements of maize harvest standard. This study proved that the DEM (Discrete Element Method) can be adopted to guide the optimization design of mechanical structure, and it has certain value for the research and development of operation equipment of other agricultural crops. Keywords: DEM, maize threshing, low damage harvesting, kernel broken rate, simulation DOI: 10.25165/j.ijabe.20231603.7042 Citation: Li X Y, Du Y F, Mao E R, Zhang Y A, Liu L, Guo D F. Design and experiment of corn low damage threshing device based on DEM. Int J Agric & Biol Eng, 2023; 16(3): 55–63.
{"title":"Design and experiment of corn low damage threshing device based on DEM","authors":"Xiaoyu Li, Yuefeng Du, Enrong Mao, Yan’an Zhang, Lei Liu, Dafang Guo","doi":"10.25165/j.ijabe.20231603.7042","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7042","url":null,"abstract":"Kernel broken rate is an important index to evaluate the maize kernel direct harvesting quality. In view of the problem of the high kernel broken rate in the present maize harvester, a new threshing cylinder was designed in this study. This device utilized rasp bar to achieve low damaged maize ears threshing. In order to determine the structure and working parameters of threshing device, the \"crop-crop\" contact model and the \"crop-mechanical\" interaction system were established and analyzed based on the discrete element method first, and the mathematical expressions of the related kinematic response of maize kernel under the external force were obtained. Then, the structure parameters of rasp bar were studied through EDEM simulation experiment, and the working parameters were determined through test-bed experiment. Finally, the simulation experiment results and test-bed experiment results were verified through field experiment. The results showed that when the threshing cylinder speed was 356 r/min, the concave clearance was 40 mm, the installation distance of rasp bar was 250 mm with 50Mn steel, and the feeding amount was 8 kg/s, the kernel broken rate was 1.93%, which satisfied the requirements of maize harvest standard. This study proved that the DEM (Discrete Element Method) can be adopted to guide the optimization design of mechanical structure, and it has certain value for the research and development of operation equipment of other agricultural crops. Keywords: DEM, maize threshing, low damage harvesting, kernel broken rate, simulation DOI: 10.25165/j.ijabe.20231603.7042 Citation: Li X Y, Du Y F, Mao E R, Zhang Y A, Liu L, Guo D F. Design and experiment of corn low damage threshing device based on DEM. Int J Agric & Biol Eng, 2023; 16(3): 55–63.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"113 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":"135357313","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":"Effect of soil surface roughness on emergence rate and yield of mechanized direct-seeded rapeseed based on 3D laser scanning","authors":"Hui Chen, Liping Gao, Mengcheng Li, Yitao Liao, Qingxi Liao","doi":"10.25165/j.ijabe.20231603.7276","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7276","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":"135361630","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.20231604.6795
Long Qian, Cheng Chen, Xiaohong Chen, Wenzhi Zeng, Yawen Gao, Kenan Deng
Cotton yield is restricted worldwide by flooding and drought that occur across various growth stages. In this study, cotton flooding and drought in Hubei (a major cotton-production province in China) from 1961 to 2019 were analyzed regarding growth stages through a daily index named the standardized antecedent precipitation evapotranspiration index (SAPEI). In addition, the impacts of flooding and drought on cotton climatic yield were quantified using multiple regression models. The results showed that the temporal trends of cotton flooding and drought intensities were generally smooth, except for an obvious downward trend for cotton drought intensity at the flowering and boll-forming stage. Additionally, cotton drought intensity varied more drastically than that of flooding over the years. Cotton-flooding proneness was much greater than cotton-drought proneness at all growth stages, and the most flooding-prone and drought-prone periods were identified as the flowering and boll-forming stage and the budding stage, respectively. In terms of spatial distribution, northeastern Hubei and southwestern Hubei were most prone to flooding and drought, respectively. The SAPEI-based regression model (R2=0.490, p<0.001), obviously outperforming the SPEI-based model (R2=0.278, p<0.05), revealed that both cotton flooding and drought exhibited negatively significant effects on cotton climatic yield and that the yield-reducing effect of cotton flooding was much greater than that of drought. Moreover, when growth stages were further considered using regression analysis, only the flowering and boll-forming stage was detected with a significant yield-reducing effect of cotton flooding. In conclusion, the SAPEI can effectively assist in monitoring cotton flooding and drought; cotton flooding, especially during the flowering and boll-forming stage and that occurring in northeastern Hubei, is the key issue for cotton field water management in Hubei. Keywords: irrigation, drainage, climatic yield, waterlogging DOI: 10.25165/j.ijabe.20231604.6795 Citation: Qian L, Chen C, Chen X H, Zeng W Z, Gao Y W, Deng K N. Cotton flooding and drought analysis regarding growth stages in Hubei, China, using a daily agrometeorological index. Int J Agric & Biol Eng, 2023; 16(4): 174–184.
{"title":"Cotton flooding and drought analysis regarding growth stages in Hubei, China, using a daily agrometeorological index","authors":"Long Qian, Cheng Chen, Xiaohong Chen, Wenzhi Zeng, Yawen Gao, Kenan Deng","doi":"10.25165/j.ijabe.20231604.6795","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231604.6795","url":null,"abstract":"Cotton yield is restricted worldwide by flooding and drought that occur across various growth stages. In this study, cotton flooding and drought in Hubei (a major cotton-production province in China) from 1961 to 2019 were analyzed regarding growth stages through a daily index named the standardized antecedent precipitation evapotranspiration index (SAPEI). In addition, the impacts of flooding and drought on cotton climatic yield were quantified using multiple regression models. The results showed that the temporal trends of cotton flooding and drought intensities were generally smooth, except for an obvious downward trend for cotton drought intensity at the flowering and boll-forming stage. Additionally, cotton drought intensity varied more drastically than that of flooding over the years. Cotton-flooding proneness was much greater than cotton-drought proneness at all growth stages, and the most flooding-prone and drought-prone periods were identified as the flowering and boll-forming stage and the budding stage, respectively. In terms of spatial distribution, northeastern Hubei and southwestern Hubei were most prone to flooding and drought, respectively. The SAPEI-based regression model (R2=0.490, p<0.001), obviously outperforming the SPEI-based model (R2=0.278, p<0.05), revealed that both cotton flooding and drought exhibited negatively significant effects on cotton climatic yield and that the yield-reducing effect of cotton flooding was much greater than that of drought. Moreover, when growth stages were further considered using regression analysis, only the flowering and boll-forming stage was detected with a significant yield-reducing effect of cotton flooding. In conclusion, the SAPEI can effectively assist in monitoring cotton flooding and drought; cotton flooding, especially during the flowering and boll-forming stage and that occurring in northeastern Hubei, is the key issue for cotton field water management in Hubei. Keywords: irrigation, drainage, climatic yield, waterlogging DOI: 10.25165/j.ijabe.20231604.6795 Citation: Qian L, Chen C, Chen X H, Zeng W Z, Gao Y W, Deng K N. Cotton flooding and drought analysis regarding growth stages in Hubei, China, using a daily agrometeorological index. Int J Agric & Biol Eng, 2023; 16(4): 174–184.","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":"135660294","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.20231602.7126
Congju Shen, Lixin Zhang, Shouxing Jia, Yan Zhou, Fang Li, Yameng Dai, Jing Zhang, Wenxiao Ma
: Hydraulic soil insertion device is a key component of orchard gas explosion subsoiling and fertilizing machine to realize rod fixed point soil insertion and gas fertilizer injection into soil. In order to explore the influence of the main working parameters and structural parameters on the depth and cylinder pressure of the hydraulic insertion device during the insertion process, the working parameters were optimized to ensure the insertion quality and efficiency. In this paper, force analysis was performed on the rod insertion process, and key parameter equation of soil insertion resistance was established. LS-DYNA finite element simulation software was applied to analyze the force variation of the rod during the insertion process. Box-Behnken test optimization design method and Design-Expert V8.0.6.1 software were used to carry out parameter optimization test of hydraulic insertion device. A multivariate quadratic polynomial regression equation was established by setting the engine revolution, insertion rod diameter and insertion time as independent variables, and the operation parameters of the hydraulic insertion device were optimized based on the relationship between the independent variables and the response values. The results showed that the regression equation model based on the response values of insertion depth and cylinder pressure had a good fitting degree. The engine revolution, rod diameter and insertion time all had significant effects on the increase of insertion depth and decrease of cylinder pressure, with interaction between the engine speed and insertion time with the insertion depth, and interaction between any two factors of engine revolution, rod diameter and insertion time with the cylinder pressure. The influences of the test factors on the insertion depth showed a descending order as engine speed, insertion time, and rod diameter. The influences of the test factors on the cylinder pressure showed a descending order as engine speed, rod diameter, and insertion time. Based on the results of insertion depth and cylinder pressure, the optimal combination of parameters was as follows: engine revolution of 1 450 r/min; rod diameter of 32 mm; and the insertion time of 8 s. Under this optimal combination, the insertion depth of the hydraulic insertion device was 44.43 cm, and the cylinder pressure was 23.09 MPa. The experimental results showed that the optimal combination of parameters could meet the agronomic requirements of fast and deep insertion, thus providing a theoretical support for the improvement and optimization of hydraulic soil insertion device of gas explosion subsoiling and fertilizing machine.
{"title":"Parameter optimization and test of hydraulic soil insertion device of orchard gas explosion subsoiling and fertilizing machine","authors":"Congju Shen, Lixin Zhang, Shouxing Jia, Yan Zhou, Fang Li, Yameng Dai, Jing Zhang, Wenxiao Ma","doi":"10.25165/j.ijabe.20231602.7126","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.7126","url":null,"abstract":": Hydraulic soil insertion device is a key component of orchard gas explosion subsoiling and fertilizing machine to realize rod fixed point soil insertion and gas fertilizer injection into soil. In order to explore the influence of the main working parameters and structural parameters on the depth and cylinder pressure of the hydraulic insertion device during the insertion process, the working parameters were optimized to ensure the insertion quality and efficiency. In this paper, force analysis was performed on the rod insertion process, and key parameter equation of soil insertion resistance was established. LS-DYNA finite element simulation software was applied to analyze the force variation of the rod during the insertion process. Box-Behnken test optimization design method and Design-Expert V8.0.6.1 software were used to carry out parameter optimization test of hydraulic insertion device. A multivariate quadratic polynomial regression equation was established by setting the engine revolution, insertion rod diameter and insertion time as independent variables, and the operation parameters of the hydraulic insertion device were optimized based on the relationship between the independent variables and the response values. The results showed that the regression equation model based on the response values of insertion depth and cylinder pressure had a good fitting degree. The engine revolution, rod diameter and insertion time all had significant effects on the increase of insertion depth and decrease of cylinder pressure, with interaction between the engine speed and insertion time with the insertion depth, and interaction between any two factors of engine revolution, rod diameter and insertion time with the cylinder pressure. The influences of the test factors on the insertion depth showed a descending order as engine speed, insertion time, and rod diameter. The influences of the test factors on the cylinder pressure showed a descending order as engine speed, rod diameter, and insertion time. Based on the results of insertion depth and cylinder pressure, the optimal combination of parameters was as follows: engine revolution of 1 450 r/min; rod diameter of 32 mm; and the insertion time of 8 s. Under this optimal combination, the insertion depth of the hydraulic insertion device was 44.43 cm, and the cylinder pressure was 23.09 MPa. The experimental results showed that the optimal combination of parameters could meet the agronomic requirements of fast and deep insertion, thus providing a theoretical support for the improvement and optimization of hydraulic soil insertion device of gas explosion subsoiling and fertilizing machine.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"60 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80557321","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.20231604.7908
Ruining Zhang, Wei Lu, Xingliang Jian, Hui Luo
The existing steering device in the fruit and vegetable packaging assembly line cannot adjust the attitude of lettuce to a unified attitude, affecting the input and packaging process of the packaging machine. This study proposes an intelligent assembly line sorting method based on the visual positioning and model predictive control of a robotic arm. First, lightweight improvement based on the YOLOv5 is realized, the lettuce stalk in the background of the conveyor belt is promptly identified, the image of the lettuce stalk in the anchor box area is processed, and the edge contour point set is determined to extract the pixel coordinates of the optimal grasp point and mirror inclination angle of the lettuce. For the intelligent assembly line system, a robot arm kinematics model is constructed and the robot kinematics inverse solutions are calculated. Additionally, the lettuce movement speeds are dynamically measured by the vision system. A combination of the model prediction control, dynamic tracking, and rapid sorting of the lettuce by the robot claw is realized. The results show that the average detection time of a single frame image in the visual positioning part is 0.014 s, which is reduced by 50%; the accuracy and recall are 98% and 95%, respectively. The detection time is significantly reduced by ensuring accuracy. Within the current speed range of the packaging assembly line conveyor belt, the manipulator can grasp lettuce at different speeds stably and fast; the average axial error, average radial error, and adjusted average inclination angle error are 0.71 cm, 1.02 cm, and 3.79°, respectively, verifying the high efficiency and stability of the model. The proposed method of this study enables application in the intelligent sorting operation of industrial assembly lines Keywords: YOLOv5, deep learning, image recognition, model predictive control, intelligent assembly line DOI: 10.25165/j.ijabe.20231604.7908 Citation: Zhang R N, Lu W, Jian X L, Luo H. Intelligent sorting method for assembly line based on visual positioning and model predictive control of robotic arm. Int J Agric & Biol Eng, 2023; 16(4): 207-214.
{"title":"Intelligent sorting method for assembly line based on visual positioning and model predictive control of robotic arm","authors":"Ruining Zhang, Wei Lu, Xingliang Jian, Hui Luo","doi":"10.25165/j.ijabe.20231604.7908","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231604.7908","url":null,"abstract":"The existing steering device in the fruit and vegetable packaging assembly line cannot adjust the attitude of lettuce to a unified attitude, affecting the input and packaging process of the packaging machine. This study proposes an intelligent assembly line sorting method based on the visual positioning and model predictive control of a robotic arm. First, lightweight improvement based on the YOLOv5 is realized, the lettuce stalk in the background of the conveyor belt is promptly identified, the image of the lettuce stalk in the anchor box area is processed, and the edge contour point set is determined to extract the pixel coordinates of the optimal grasp point and mirror inclination angle of the lettuce. For the intelligent assembly line system, a robot arm kinematics model is constructed and the robot kinematics inverse solutions are calculated. Additionally, the lettuce movement speeds are dynamically measured by the vision system. A combination of the model prediction control, dynamic tracking, and rapid sorting of the lettuce by the robot claw is realized. The results show that the average detection time of a single frame image in the visual positioning part is 0.014 s, which is reduced by 50%; the accuracy and recall are 98% and 95%, respectively. The detection time is significantly reduced by ensuring accuracy. Within the current speed range of the packaging assembly line conveyor belt, the manipulator can grasp lettuce at different speeds stably and fast; the average axial error, average radial error, and adjusted average inclination angle error are 0.71 cm, 1.02 cm, and 3.79°, respectively, verifying the high efficiency and stability of the model. The proposed method of this study enables application in the intelligent sorting operation of industrial assembly lines Keywords: YOLOv5, deep learning, image recognition, model predictive control, intelligent assembly line DOI: 10.25165/j.ijabe.20231604.7908 Citation: Zhang R N, Lu W, Jian X L, Luo H. Intelligent sorting method for assembly line based on visual positioning and model predictive control of robotic arm. Int J Agric & Biol Eng, 2023; 16(4): 207-214.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"9 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":"135660291","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.20231601.7898
Na Li, Tongyu Xu, Nan Hao
{"title":"Phase states of moisture content in different maize kernel types","authors":"Na Li, Tongyu Xu, Nan Hao","doi":"10.25165/j.ijabe.20231601.7898","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.7898","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"100 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76075990","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.20231603.4503
Honglei Wei, Xiangzhi Kong, Xianyi Zhai, Qiang Tong, Guibing Pang
One of the essential techniques for using underwater robots to fish sea cucumbers is that the robots must track sea cucumbers using computer vision technology. Tracking underwater targets is a challenging task due to suspension, water absorption, and light scattering. This study proposed a simple but effective algorithm for sea cucumber tracking based on Kernelized Correlation Filters (KCF) framework. This method tracked the head and tail of the sea cucumber respectively and calculated the scale change according to the distance between the head and tail. The KCF method was improved on three strategies. First of all, the target was searched at the predicted position to improve accuracy. Secondly, an adaptive learning rate updating method based on the detection score of each frame was proposed. Finally, the adaptive size of the histogram of the oriented gradient (HOG) feature was used to balance the accuracy and efficiency. Experimental results showed that the algorithm had good tracking performance. Keywords: visual tracking, correlation filters, kernelized correlation filters, sea cucumber, scale estimation, underwater DOI: 10.25165/j.ijabe.20231603.4503 Citation: Wei H L, Kong X Z, Zhai X Y, Tong Q, Pang G B. Visual tracking for underwater sea cucumber via correlation filters. Int J Agric & Biol Eng, 2023; 16(3): 16(3): 247–253.
{"title":"Visual tracking for underwater sea cucumber via correlation filters","authors":"Honglei Wei, Xiangzhi Kong, Xianyi Zhai, Qiang Tong, Guibing Pang","doi":"10.25165/j.ijabe.20231603.4503","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.4503","url":null,"abstract":"One of the essential techniques for using underwater robots to fish sea cucumbers is that the robots must track sea cucumbers using computer vision technology. Tracking underwater targets is a challenging task due to suspension, water absorption, and light scattering. This study proposed a simple but effective algorithm for sea cucumber tracking based on Kernelized Correlation Filters (KCF) framework. This method tracked the head and tail of the sea cucumber respectively and calculated the scale change according to the distance between the head and tail. The KCF method was improved on three strategies. First of all, the target was searched at the predicted position to improve accuracy. Secondly, an adaptive learning rate updating method based on the detection score of each frame was proposed. Finally, the adaptive size of the histogram of the oriented gradient (HOG) feature was used to balance the accuracy and efficiency. Experimental results showed that the algorithm had good tracking performance. Keywords: visual tracking, correlation filters, kernelized correlation filters, sea cucumber, scale estimation, underwater DOI: 10.25165/j.ijabe.20231603.4503 Citation: Wei H L, Kong X Z, Zhai X Y, Tong Q, Pang G B. Visual tracking for underwater sea cucumber via correlation filters. Int J Agric & Biol Eng, 2023; 16(3): 16(3): 247–253.","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":"135357307","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.20231603.7268
Xizhi Lyu, Weimin Xing, Yuguo Han, Zhigong Peng, Baozhong Zhang, Muhammad Roman
Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation. However, the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors. This study carried out research on the correlations among spectral parameters of the canopy of winter wheat, crop growth process, and soil water content, and finally constructed the soil water content prediction model with the growth days parameter. The results showed that the plant water content of winter wheat tended to decrease during the whole growth period. The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer. At different growth stages, even if the soil water content was the same, the plant water content and characteristic spectral reflectance were also different. Therefore, the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy. It is found that the determination coefficient (R2) of the models built during the whole growth period was greatly increased, ranging from 0.54 to 0.60. Then, the model built by OSAVI (Optimized Soil Adjusted Vegetation Index) and Rg/Rr, two of the highest precision characteristic spectral parameters, were selected for model validation. The correlation between OSAVI and soil water content, Rg/Rr, and soil water content were still significant (p<0.05). The R2, MAE, and RMSE validation models were 0.53 and 0.58, 3.19 and 2.97, 4.76 and 4.41, respectively, which was accurate enough to be applied in a large-area field. Furthermore, the upper and lower irrigation limit of OSAVI and Rg/Rr were put forward. The research results could guide the agricultural production of winter wheat in northern China. Keywords: Winter wheat, Canopy spectra, Growth process, Soil water content, Irrigation threshold, Soil moisture model prediction DOI: 10.25165/j.ijabe.20231603.7268 Citation: Lyu X Z, Xing W M, Han Y G, Peng Z G, Zhang B Z, Roman M. Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat. Int J Agric & Biol Eng, 2023; 16(3): 160–168.
{"title":"Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat","authors":"Xizhi Lyu, Weimin Xing, Yuguo Han, Zhigong Peng, Baozhong Zhang, Muhammad Roman","doi":"10.25165/j.ijabe.20231603.7268","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7268","url":null,"abstract":"Large area of soil moisture status diagnosis based on plant canopy spectral data remains one of the hot spots of agricultural irrigation. However, the existing soil water prediction model constructed by the spectral parameters without considering the plant growth process will inevitably increase the prediction errors. This study carried out research on the correlations among spectral parameters of the canopy of winter wheat, crop growth process, and soil water content, and finally constructed the soil water content prediction model with the growth days parameter. The results showed that the plant water content of winter wheat tended to decrease during the whole growth period. The plant water content had the best correlations with the soil water content of the 0-50 cm soil layer. At different growth stages, even if the soil water content was the same, the plant water content and characteristic spectral reflectance were also different. Therefore, the crop growing days parameter was added to the model established by the relationships between characteristic spectral parameters and soil water content to increase the prediction accuracy. It is found that the determination coefficient (R2) of the models built during the whole growth period was greatly increased, ranging from 0.54 to 0.60. Then, the model built by OSAVI (Optimized Soil Adjusted Vegetation Index) and Rg/Rr, two of the highest precision characteristic spectral parameters, were selected for model validation. The correlation between OSAVI and soil water content, Rg/Rr, and soil water content were still significant (p<0.05). The R2, MAE, and RMSE validation models were 0.53 and 0.58, 3.19 and 2.97, 4.76 and 4.41, respectively, which was accurate enough to be applied in a large-area field. Furthermore, the upper and lower irrigation limit of OSAVI and Rg/Rr were put forward. The research results could guide the agricultural production of winter wheat in northern China. Keywords: Winter wheat, Canopy spectra, Growth process, Soil water content, Irrigation threshold, Soil moisture model prediction DOI: 10.25165/j.ijabe.20231603.7268 Citation: Lyu X Z, Xing W M, Han Y G, Peng Z G, Zhang B Z, Roman M. Establishment of soil moisture model based on hyperspectral data and growth parameters of winter wheat. Int J Agric & Biol Eng, 2023; 16(3): 160–168.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"142 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":"135357316","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.20231603.7799
Ang Gao, Aijun Geng, Yuepeng Song, Longlong Ren, Yue Zhang, Xiang Han
In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control, this study proposed a maize disease detection method based on improved MobileNet V3-small, using a UAV to collect maize disease images and establish a maize disease dataset in a complex context, and explored the effects of data expansion and migration learning on model recognition accuracy, recall rate, and F1-score instructive evaluative indexes, and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model. The structured compression of MobileNet V3-small bneck layer retains only 6 layers, the expansion multiplier of each layer was redesigned, 32-fold fast downsampling was used in the first layer, and the location of the SE module was optimized. The improved model had an average accuracy of 79.52% in the test set, a recall of 77.91%, an F1-score of 78.62%, a model size of 2.36 MB, and a single image detection speed of 9.02 ms. The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices. This study provides technical support for realizing the intelligent detection of maize leaf diseases. Keywords: maize leaf disease, image recognition, model compression, MobileNetV3-small DOI: 10.25165/j.ijabe.20231603.7799 Citation: Gao A, Geng A J, Song Y P, Ren L L, Zhang Y, Han X. Detection of maize leaf diseases using improved MobileNet V3-small. Int J Agric & Biol Eng, 2023; 16(3): 225–232.
{"title":"Detection of maize leaf diseases using improved MobileNet V3-small","authors":"Ang Gao, Aijun Geng, Yuepeng Song, Longlong Ren, Yue Zhang, Xiang Han","doi":"10.25165/j.ijabe.20231603.7799","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7799","url":null,"abstract":"In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control, this study proposed a maize disease detection method based on improved MobileNet V3-small, using a UAV to collect maize disease images and establish a maize disease dataset in a complex context, and explored the effects of data expansion and migration learning on model recognition accuracy, recall rate, and F1-score instructive evaluative indexes, and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model. The structured compression of MobileNet V3-small bneck layer retains only 6 layers, the expansion multiplier of each layer was redesigned, 32-fold fast downsampling was used in the first layer, and the location of the SE module was optimized. The improved model had an average accuracy of 79.52% in the test set, a recall of 77.91%, an F1-score of 78.62%, a model size of 2.36 MB, and a single image detection speed of 9.02 ms. The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices. This study provides technical support for realizing the intelligent detection of maize leaf diseases. Keywords: maize leaf disease, image recognition, model compression, MobileNetV3-small DOI: 10.25165/j.ijabe.20231603.7799 Citation: Gao A, Geng A J, Song Y P, Ren L L, Zhang Y, Han X. Detection of maize leaf diseases using improved MobileNet V3-small. Int J Agric & Biol Eng, 2023; 16(3): 225–232.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"21 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":"135358861","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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