The spray characteristics of a full-cone pressure swirl nozzle have been investigated in this study. The results were defined by Reynolds number, which focuses on the breakup of liquid film, droplet size, velocity, and liquid volume flux under different Reynolds numbers at the near-field spray. The spray structure was visualized using a high-speed camera, and the characteristics of droplets were measured using a Phase Doppler Anemometer (PDA) in both the radial and axial directions. The tests were carried out at varying spray pressures (0.2 to 1.0 MPa), corresponding to different Reynolds numbers (5369 to 12006). It was found that when the Reynolds number rises, the liquid became more unstable after leaving the nozzle, causing the liquid film to break up faster. According to the measurements of PDA, the coalescence of droplets increased due to the centrifugal effect. What’s more, the velocity of the droplets fluctuates significantly in the radial direction, and the droplets with a smaller particle size had a higher average velocity. From the perspective of liquid distribution, the increase in Reynolds number caused the spray liquid to move in the radial direction gradually. In contrast, the liquid volume distribution changed in the radial direction more obviously than in the axial direction, growing to the maximum along the radial direction and gradually reducing. It can provide a reference for selecting operating parameters for actual agricultural spray operations and the design of electrostatic nozzles through the research on breakup and droplet characteristics. Keywords: full-cone pressure swirl nozzle, droplet size, droplet velocity, liquid volume flux, high-speed camera, PDA DOI: 10.25165/j.ijabe.20231604.7088 Citation: Xue X Y, Xu X F, Lyu S L, Song S R, Ai X, Li N C, et al. Experimental investigation on spray characteristics of agricultural full-cone pressure swirl nozzle. Int J Agric & Biol Eng, 2023; 16(4): 29–40.
{"title":"Experimental investigation on the spray characteristics of agricultural full-cone pressure swirl nozzle","authors":"Xiuyun Xue, Xufeng Xu, Shilei Lyu, Shuran Song, Xin Ai, Nengchao Li, Zhenyu Yang, Zhen Li","doi":"10.25165/j.ijabe.20231604.7088","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231604.7088","url":null,"abstract":"The spray characteristics of a full-cone pressure swirl nozzle have been investigated in this study. The results were defined by Reynolds number, which focuses on the breakup of liquid film, droplet size, velocity, and liquid volume flux under different Reynolds numbers at the near-field spray. The spray structure was visualized using a high-speed camera, and the characteristics of droplets were measured using a Phase Doppler Anemometer (PDA) in both the radial and axial directions. The tests were carried out at varying spray pressures (0.2 to 1.0 MPa), corresponding to different Reynolds numbers (5369 to 12006). It was found that when the Reynolds number rises, the liquid became more unstable after leaving the nozzle, causing the liquid film to break up faster. According to the measurements of PDA, the coalescence of droplets increased due to the centrifugal effect. What’s more, the velocity of the droplets fluctuates significantly in the radial direction, and the droplets with a smaller particle size had a higher average velocity. From the perspective of liquid distribution, the increase in Reynolds number caused the spray liquid to move in the radial direction gradually. In contrast, the liquid volume distribution changed in the radial direction more obviously than in the axial direction, growing to the maximum along the radial direction and gradually reducing. It can provide a reference for selecting operating parameters for actual agricultural spray operations and the design of electrostatic nozzles through the research on breakup and droplet characteristics. Keywords: full-cone pressure swirl nozzle, droplet size, droplet velocity, liquid volume flux, high-speed camera, PDA DOI: 10.25165/j.ijabe.20231604.7088 Citation: Xue X Y, Xu X F, Lyu S L, Song S R, Ai X, Li N C, et al. Experimental investigation on spray characteristics of agricultural full-cone pressure swirl nozzle. Int J Agric & Biol Eng, 2023; 16(4): 29–40.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135659262","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}
Microalgae harvesting remains a challenging step in microalgae industrialization, thereby provoking the necessity to explore sustainable and economically feasible approaches. This research investigated the use of magnetic flocculated nanoparticles in the harvesting of the common microalgae Chlorella pyrenoidosa and Scenedesmus obliquus. The results showed that magnetic flocculated nanoparticles efficiently adsorbed negatively charged microalgae cells, and a magnetic field could adsorb the magnetic flocculated nanoparticles, thereby harvesting the microalgae cells. Harvesting efficiency was remarkably increased at the optimum magnetic field strength of 0.5 T with the magnetic flocculated nanoparticles at 0.738 g/L, and microalgae broth at pH 9.0, whereas the recovery rates of both C. pyrenoidosa and S. obliquus were around 97% and the sedimentation speed of both was above 2.63 cm/min. This study exemplified the magnetic flocculated nanoparticles-based approach to effectively harvest the microalgae cells. Keywords: magnetic flocculated nanoparticles, Chlorella pyrenoidosa, Scenedesmus obliquus, recovery rate, sedimentation speed DOI: 10.25165/j.ijabe.20231604.7809 Citation: Liu S F, Fu S P, Wen Z J, Wang X, Jiang T J, Li H Y. Efficient harvesting of green microalgae cells by magnetic flocculated Fe3O4 nanoparticles combined with chitosan. Int J Agric & Biol Eng, 2023; 16(4): 215-221
{"title":"Efficient harvesting of green microalgae cells by magnetic flocculated Fe3O4 nanoparticles combined with chitosan","authors":"Sifen Liu, Suping Fu, Zhongjie Wen, Xiang Wang, Tianjiu Jiang, Hongye Li","doi":"10.25165/j.ijabe.20231604.7809","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231604.7809","url":null,"abstract":"Microalgae harvesting remains a challenging step in microalgae industrialization, thereby provoking the necessity to explore sustainable and economically feasible approaches. This research investigated the use of magnetic flocculated nanoparticles in the harvesting of the common microalgae Chlorella pyrenoidosa and Scenedesmus obliquus. The results showed that magnetic flocculated nanoparticles efficiently adsorbed negatively charged microalgae cells, and a magnetic field could adsorb the magnetic flocculated nanoparticles, thereby harvesting the microalgae cells. Harvesting efficiency was remarkably increased at the optimum magnetic field strength of 0.5 T with the magnetic flocculated nanoparticles at 0.738 g/L, and microalgae broth at pH 9.0, whereas the recovery rates of both C. pyrenoidosa and S. obliquus were around 97% and the sedimentation speed of both was above 2.63 cm/min. This study exemplified the magnetic flocculated nanoparticles-based approach to effectively harvest the microalgae cells. Keywords: magnetic flocculated nanoparticles, Chlorella pyrenoidosa, Scenedesmus obliquus, recovery rate, sedimentation speed DOI: 10.25165/j.ijabe.20231604.7809 Citation: Liu S F, Fu S P, Wen Z J, Wang X, Jiang T J, Li H Y. Efficient harvesting of green microalgae cells by magnetic flocculated Fe3O4 nanoparticles combined with chitosan. Int J Agric & Biol Eng, 2023; 16(4): 215-221","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135660295","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":"Experimental and numerical study on the shrinkage-deformation of carrot slices during hot air drying","authors":"Dalong Jiang, Congcong Li, Zifan Lin, Yun-tian Wu, Hongjuan Pei, M. Zielińska, Hongwei Xiao","doi":"10.25165/j.ijabe.20231601.6736","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.6736","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81870603","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}
: Unmanned driving of agricultural machinery has garnered significant attention in recent years, especially with the development of precision farming and sensor technologies. To achieve high performance and low cost, perception tasks are of great importance. In this study, a low-cost and high-safety method was proposed for field road recognition in unmanned agricultural machinery. The approach of this study utilized point clouds, with low-resolution lidar point clouds as inputs, generating high-resolution point clouds and Bird's Eye View (BEV) images that were encoded with several basic statistics. Using a BEV representation, road detection was reduced to a single-scale problem that could be addressed with an improved U-Net++ neural network. Three enhancements were proposed for U-Net++: 1) replacing the convolutional kernel in the original U-Net++ with an Asymmetric Convolution Block (ACBlock); 2) adding a multi-branch Asymmetric Dilated Convolutional Block (MADC) in the highest semantic information layer; 3) adding an Attention Gate (AG) model to the long-skip-connection in the decoding stage. The results of experiments of this study showed that our algorithm achieved a Mean Intersection Over Union of 96.54% on the 16-channel point clouds, which was 7.35 percentage points higher than U-Net++. Furthermore, the average processing time of the model was about 70 ms, meeting the time requirements of unmanned driving in agricultural machinery. The proposed method of this study can be applied to enhance the perception ability of unmanned agricultural machinery thereby increasing the safety of field road driving.
近年来，随着精准农业和传感器技术的发展，农业机械的无人驾驶受到了广泛关注。为了实现高性能和低成本，感知任务非常重要。本研究提出了一种低成本、高安全性的无人农机现场道路识别方法。本研究方法利用点云，以低分辨率激光雷达点云为输入，生成高分辨率点云和用几种基本统计编码的鸟瞰(BEV)图像。使用BEV表示，道路检测被简化为一个单尺度问题，可以通过改进的U-Net++神经网络来解决。针对U-Net++提出了三个改进方案:1)用非对称卷积块(ACBlock)取代原始U-Net++中的卷积核;2)在最高语义信息层增加多分支非对称扩展卷积块(MADC);3)在解码阶段为长跳接增加注意门(Attention Gate, AG)模型。本研究的实验结果表明，我们的算法在16通道点云上实现了96.54%的average Intersection Over Union，比U-Net++提高了7.35个百分点。模型的平均处理时间约为70 ms，满足农业机械无人驾驶的时间要求。本文提出的方法可用于增强无人农机的感知能力，从而提高田间道路行驶的安全性。
{"title":"Recognition of field roads based on improved U-Net++ Network","authors":"Lili Yang, Yuanbo Li, Mengshuai Chang, Yuanyuan Xu, Bingbing Hu, Xinxin Wang, Caicong Wu","doi":"10.25165/j.ijabe.20231602.7941","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.7941","url":null,"abstract":": Unmanned driving of agricultural machinery has garnered significant attention in recent years, especially with the development of precision farming and sensor technologies. To achieve high performance and low cost, perception tasks are of great importance. In this study, a low-cost and high-safety method was proposed for field road recognition in unmanned agricultural machinery. The approach of this study utilized point clouds, with low-resolution lidar point clouds as inputs, generating high-resolution point clouds and Bird's Eye View (BEV) images that were encoded with several basic statistics. Using a BEV representation, road detection was reduced to a single-scale problem that could be addressed with an improved U-Net++ neural network. Three enhancements were proposed for U-Net++: 1) replacing the convolutional kernel in the original U-Net++ with an Asymmetric Convolution Block (ACBlock); 2) adding a multi-branch Asymmetric Dilated Convolutional Block (MADC) in the highest semantic information layer; 3) adding an Attention Gate (AG) model to the long-skip-connection in the decoding stage. The results of experiments of this study showed that our algorithm achieved a Mean Intersection Over Union of 96.54% on the 16-channel point clouds, which was 7.35 percentage points higher than U-Net++. Furthermore, the average processing time of the model was about 70 ms, meeting the time requirements of unmanned driving in agricultural machinery. The proposed method of this study can be applied to enhance the perception ability of unmanned agricultural machinery thereby increasing the safety of field road driving.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80869022","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":"Design of a fixed-pipe cold aerosol spraying system for chemical application in greenhouse","authors":"Shilin Wang, Daipeng Lu, Xue Li, Xiaohui Lei, Yuxin Tang, Xiaolan Lyu","doi":"10.25165/j.ijabe.20231601.6573","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.6573","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89302096","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.20231602.7020
Xin Wang, Yu Yang, Xin Zhao, Min Huang, Qibing Zhu
: Crop coverage (CC) is an important parameter to represent crop growth characteristics, and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions. In this study, a novel CNN-LSTM model that combined the advantages of convolutional neural network (CNN) in feature extraction and long short-term memory (LSTM) in time series processing was proposed for multi-day ahead forecasting of maize CC. Considering the influence of climate change on maize growth, five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting model. The field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and LSTM. The results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R 2 at all forecasting horizons. Subsequently, the performance of CNN-LSTM under univariate (historical maize CC) and multivariate (historical maize CC+microclimatic factors) input was compared, and the results indicated that additional microclimatic factors were effective in improving the forecasting performance. Furthermore, the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed, and the results showed that the highest forecasting accuracy was obtained in the seven leaves stage. Therefore, CNN-LSTM can be considered a useful tool to forecast maize CC.
{"title":"Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM","authors":"Xin Wang, Yu Yang, Xin Zhao, Min Huang, Qibing Zhu","doi":"10.25165/j.ijabe.20231602.7020","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.7020","url":null,"abstract":": Crop coverage (CC) is an important parameter to represent crop growth characteristics, and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions. In this study, a novel CNN-LSTM model that combined the advantages of convolutional neural network (CNN) in feature extraction and long short-term memory (LSTM) in time series processing was proposed for multi-day ahead forecasting of maize CC. Considering the influence of climate change on maize growth, five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting model. The field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and LSTM. The results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R 2 at all forecasting horizons. Subsequently, the performance of CNN-LSTM under univariate (historical maize CC) and multivariate (historical maize CC+microclimatic factors) input was compared, and the results indicated that additional microclimatic factors were effective in improving the forecasting performance. Furthermore, the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed, and the results showed that the highest forecasting accuracy was obtained in the seven leaves stage. Therefore, CNN-LSTM can be considered a useful tool to forecast maize CC.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81512695","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.20231601.6528
Zhiwei Hu, Hua Yang, T. Lou, Hong-Ping Yan
{"title":"Concurrent channel and spatial attention in Fully Convolutional Network for individual pig image segmentation","authors":"Zhiwei Hu, Hua Yang, T. Lou, Hong-Ping Yan","doi":"10.25165/j.ijabe.20231601.6528","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.6528","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90405083","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.20231601.7688
Yu Ren, Wensong Guo, Xufeng Wang, Can Hu, Long Wang, Xiaowei He, Jianfei Xing
{"title":"Separation and mechanical properties of residual film and soil","authors":"Yu Ren, Wensong Guo, Xufeng Wang, Can Hu, Long Wang, Xiaowei He, Jianfei Xing","doi":"10.25165/j.ijabe.20231601.7688","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231601.7688","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72467358","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.20231603.7507
Jia Chen, Qi’an Ding, Wen Yao, Mingxia Shen, Longshen Liu
Timely identification and tracking of abnormal hens in stacked cages are of great significance for precision treatment and the elimination of sick individuals. The head features of the caged-hens are used to overcome observation difficulties caused by the cage and feathers blocking, but it is still hard to identify similar head states. To solve this problem, a fine-grained detection of caged-hens head states was developed using adaptive Brightness Adjustment in combination with Convolutional Neural Networks (FBA-CNN). Grid Region-based CNN (R-CNN), a convolution neural network (CNN), was optimized with the Squeeze-and-Excitation (SE) and Depthwise Over-parameterized Convolutional (DO-Conv) to detect layer heads from cages and to accurately cut them as single-head images. The brightness of each single-head image was adjusted adaptively and classified through the deep convolution neural network based on SE-Resnet50. Finally, we returned to the original image to realize multi-target detection with coordinate mapping. The results showed that the AP@0.5 of layer head detection using the optimized Grid R-CNN was 0.947, the accuracy of classification with SE-Resnet50 was 0.749, the F1 score was 0.637, and the mAP@0.5 of FBA-CNN was 0.846. In summary, this automated method can accurately identify different layer head states in layer cages to provide a basis for follow-up studies of abnormal behavior including dyspnea and cachexia. Keywords: Grid R-CNN, squeeze-and-excitation, Depthwise Over-parameterized Convolutional, adaptive brightness adjustment, fine-grained detection DOI: 10.25165/j.ijabe.20231603.7507 Citation: Chen J, Ding Q A, Yao W, Shen M X, Liu L S. Fine-grained detection of caged-hens head states using adaptive Brightness Adjustment in combination with Convolutional Neural Networks. Int J Agric & Biol Eng, 2023; 16(): 16(3): 208–216.
{"title":"Fine-grained detection of caged-hen head states using adaptive Brightness Adjustment in combination with Convolutional Neural Networks","authors":"Jia Chen, Qi’an Ding, Wen Yao, Mingxia Shen, Longshen Liu","doi":"10.25165/j.ijabe.20231603.7507","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7507","url":null,"abstract":"Timely identification and tracking of abnormal hens in stacked cages are of great significance for precision treatment and the elimination of sick individuals. The head features of the caged-hens are used to overcome observation difficulties caused by the cage and feathers blocking, but it is still hard to identify similar head states. To solve this problem, a fine-grained detection of caged-hens head states was developed using adaptive Brightness Adjustment in combination with Convolutional Neural Networks (FBA-CNN). Grid Region-based CNN (R-CNN), a convolution neural network (CNN), was optimized with the Squeeze-and-Excitation (SE) and Depthwise Over-parameterized Convolutional (DO-Conv) to detect layer heads from cages and to accurately cut them as single-head images. The brightness of each single-head image was adjusted adaptively and classified through the deep convolution neural network based on SE-Resnet50. Finally, we returned to the original image to realize multi-target detection with coordinate mapping. The results showed that the AP@0.5 of layer head detection using the optimized Grid R-CNN was 0.947, the accuracy of classification with SE-Resnet50 was 0.749, the F1 score was 0.637, and the mAP@0.5 of FBA-CNN was 0.846. In summary, this automated method can accurately identify different layer head states in layer cages to provide a basis for follow-up studies of abnormal behavior including dyspnea and cachexia. Keywords: Grid R-CNN, squeeze-and-excitation, Depthwise Over-parameterized Convolutional, adaptive brightness adjustment, fine-grained detection DOI: 10.25165/j.ijabe.20231603.7507 Citation: Chen J, Ding Q A, Yao W, Shen M X, Liu L S. Fine-grained detection of caged-hens head states using adaptive Brightness Adjustment in combination with Convolutional Neural Networks. Int J Agric & Biol Eng, 2023; 16(): 16(3): 208–216.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135357308","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.20231603.7260
Hao Yang, Ting Wang, Fang Ji, Qing Zhou, Jianfeng Wang
To enhance the transplants' growth and reduce energy use efficiency, Eggplant (Solanum melongena L.) transplants (cv. Jingqie 21) were cultivated in a plant factory laboratory under different LED light spectrums. The experimental treatments included white plus blue LED lights (R: B=0.5, WB0.5), white LED lights (R: B=0.9, W0.9), white plus red LED lights (R: B=2.7, WR2.7), white plus red plus UV lights (R: B=3.8, WRUV3.8), and red plus blue plus green LED lights (R: B=5.4, RBG5.4). The transplants were grown for 30 d under a light intensity of 250 μmol/m2·s and a photoperiod of 16 h/d. The morphological indicators and biomass accumulation of eggplant transplants were significantly higher in the W0.9 treatment compared to the other experimental treatments. The photosynthetic quantum yield in the W0.9 treatment exhibited an increase of over 22% compared to that in the WR2.7 treatment. The shoot dry weight of the W0.9 treatment reached (381±41) mg/plant and the leaf area was (113.3±8.9) cm2, indicating a higher health index compared to the other treatments. However, there were no significant differences in the net photosynthetic rate of the leaves among all treatments. The energy yield (EY) of the W0.9 treatment was (37.7±1.8) g/kW·h, which was higher than others. Therefore, considering the high quality of transplants and the maximization of energy use efficiency, the LED light spectrum in the eggplant transplants production was recommended to the white LED light with an R: B ratio of 0.9. Keywords: eggplant transplants, LED light spectrum, growth, energy use efficiency DOI: Citation: Yang H, Wang T, Ji F, Zhou Q, Wang J F. Effects of LED light spectrum on the growth and energy use efficiency of eggplant transplants. Int J Agric & Biol Eng, 2023; 16(3): 23–29.
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