International Journal of Agricultural and Biological Engineering最新文献

{"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":"65 1","pages":""},"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}

{"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":"1 1","pages":""},"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}

{"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":"112 1","pages":""},"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":"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":"14 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":"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}

{"title":"Effects of LED light spectrum on the growth and energy use efficiency of eggplant transplants","authors":"Hao Yang, Ting Wang, Fang Ji, Qing Zhou, Jianfeng Wang","doi":"10.25165/j.ijabe.20231603.7260","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7260","url":null,"abstract":"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.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"18 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":"135357563","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":"Influences of DRA and non-DRA vision on the visual responses of locusts stimulated by linearly polarized and unpolarized lights","authors":"Shengguang Zou, Tao Liu, Yicheng Ma, Pingchuan Zhang, Qihang Liu","doi":"10.25165/j.ijabe.20231603.7959","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.7959","url":null,"abstract":"Locust and grasshopper plagues pose a serious threat to crop production in many areas worldwide. However, there is a lack of effective, quick-acting methods to control such outbreaks. Methods exploiting the phototactic response of these insects are receiving increasing attention. The current study investigated the effect of linearly polarized and unpolarized light on locust phototactic and polarotactic responses, in particular the function of their dorsal rim area (DRA) and non-DRA visual fields. The results showed that the polarotactic function weight of DRA vision was stimulated by linearly polarized ultraviolet (UV) and violet light, the phototactic function weight was induced by blue, green, and orange light, and under linearly polarized light, the functional effect of DRA vision was strongest in response to linearly polarized violet light. Moreover, the locust visual response effect was related to spectral light attributes, with the linear polarization effect intensifying in response to the short-range vision sensitivity of non-DRA visual fields, whereas DRA vision regulated the short-range sensitivity of compound eye vision. When illumination increased, the synergistic enhancement effects of linearly polarized ultraviolet and violet light were significant, whereas the visual sensitivity was restricted significantly by linearly polarized blue, green, or orange light. Thus, non-DRA vision determined, while DRA vision enhanced, the phototactic response sensitivity, whereas, in linearly polarized UV or violet light, non-DRA vision determined, while DRA vision enhanced, the visual trend and polarotaxic aggregation sensitivity, with opposite effects in linearly polarized blue, green, or orange light. When illumination increased, there was a driving effect caused by linearly polarized violet light on non-DRA vision, whereas at short-wave lengths, the control effect induced by linearly polarized orange light was optimal; however, the photo-induced effect of linearly polarized violet light and the visual distance control effect of linearly polarized orange light were optimal. These results provide theoretical support for the photo-induced mechanism of the locust visual response effect and for the development of linearly polarized light sources for the environmentally friendly prevention and control of locust populations. Keywords: Locusta migratoria, linearly polarized light, spectral light, visual response, DRA and non-DRA vision DOI: 10.25165/j.ijabe.20231603.7959 Citation: Zou S G, Liu T, Ma Y C, Zhang P C Liu Q H. Influences of DRA and non-DRA vision on visual responses of locusts stimulated by linearly polarized and unpolarized lights. Int J Agric & Biol Eng, 2023; 16(3): 15–22.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"320 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":"135358843","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":"Generative AI in education: To embrace it or not？","authors":"Samuel Ariyo Okaiyeto, Junwen Bai, Hongwei Xiao","doi":"10.25165/j.ijabe.20231603.8486","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231603.8486","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"265 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":"135361272","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":"Energy use efficiency and greenhouse gas emissions (GHG) analysis of garlic cultivation in Turkey","authors":"Mehmet Fırat Baran, Cihan Demir, Ahmet Konuralp Eliçin, Osman Gökdoğan","doi":"10.25165/j.ijabe.20231604.7599","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231604.7599","url":null,"abstract":"This study has been conducted with the purpose of determining energy use efficiency and greenhouse gas emissions of garlic cultivation during the 2020-2021 cultivation season in Adıyaman province of Turkey. Questionnaires, observations and field works were performed in 134 garlic farms in the region through simple random method. In garlic cultivation, energy input was calculated as 32103.20 MJ/hm2 and energy output was calculated as 30096 MJ/hm2. With regards to the three highest inputs in garlic production, 46.66% of the energy inputs consisted of chemical fertilizers energy (14979.26 MJ/hm2), 11.29% consisted of farmyard manure energy (3625.71 MJ/hm2) and 10.48% consisted of human labour energy (3363.36 MJ/hm2). Energy use efficiency, specific energy, energy productivity and net energy in garlic cultivation were calculated as 0.94, 1.71 MJ/kg, 0.59 kg/MJ, and −2007.20 MJ/hm2, respectively. The total energy input consumed in garlic cultivation was classified as 27.19% direct energy, 72.81% indirect energy, 35.17% renewable energy and 64.87% non-renewable energy. Total GHG emissions and GHG ratio were calculated as 8636.60 kgCO2-eq/hm2 and 0.46 kgCO2-eq/kg, respectively. Keywords: energy use efficiency, garlic, greenhouse gas emissions, specific energy, Turkey DOI: 10.25165/j.ijabe.20231604.7599 Citation: Baran M F, Demir C, Eliçin A K, Gökdoğan O. Energy use efficiency and greenhouse gas emissions (GHG) analysis of garlic cultivation in Turkey. Int J Agric & Biol Eng, 2023; 16(4): 63-67.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"46 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":"135658904","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":"Formation mechanism for the laying angle of hemp harvester based on ANSYS-ADAMS","authors":"Jicheng Huang, Li Tan, Kunpeng Tian, Bin Zhang, Aimin Ji, Haolu Liu, Cheng Shen","doi":"10.25165/j.ijabe.20231604.7978","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231604.7978","url":null,"abstract":"Aiming at the problem of large differences in the laying angle and posture of plants cut by the hemp harvester, which is unfavorable for the subsequent picking-up, this paper analyzed the laying process and laying angles, and built a conveyor-plant rigid-flexible coupling model for simulating the laying of hemp plant. Moreover, the operating parameters were tested and optimized based on the central composite design theory, and carried out multi-objective optimization with the minimum laying angle as the response index. Firstly, the formation mechanism of the laying angle of hemp harvester was studied. Secondly, a test was designed with the quadratic orthogonal rotational combination test method, with the data being processed by Design-Expert. A regression mathematical model of the laying angle was built, and the influence of the interactions between factors on the laying angle was analyzed with the response surface method. Furthermore, multi-objective optimization was conducted on the regression model according to the actual production design requirements. As a result, the best combination was obtained, that is, when the forward speed is 0.7 m/s, speed ratio 1.40, and stubble height 95 mm, the minimum laying angle can be obtained, namely 124.9°. The optimization parameters were verified by the simulation and field tests. The simulation test showed that the simulated laying angle is 125.2°, with a relative error of 0.24% from the theoretical value, under the best combination of parameters. The field test showed that the average laying angle of hemp plant is 121.8°, with a relative error of 2.5% from the theoretical value, under the best combination of parameters. The results may provide a reference for the structural improvement and operating parameter control of hemp harvesters. Keywords: agricultural machinery, hemp, laying angle, rigid-flexible coupling model, optimization, response surface analysis DOI: 10.25165/j.ijabe.20231604.7978 Citation: Huang J C, Tan L, Tian K P, Zhang B, Ji A M, Liu H L, Shen C. Formation mechanism for the laying angle of hemp harvester based on ANSYS-ADAMS. Int J Agric & Biol Eng, 2023; 16(4): 109–115.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"105 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":"135659789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of the pin-roller metering device and tube effect for wheat seeds and granular fertilizers based on DEM","authors":"S. Adilet, Kaiyu Zhao, Guangyao Liu, Nukeshev Sayakhat, Jun Chen, Guangrui Hu, Lingxin Bu, Yu Chen, Hongling Jin, Shuo Zhang, Zagainov Nikolay, Muratkhan Marat","doi":"10.25165/j.ijabe.20231602.7721","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.7721","url":null,"abstract":"","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"112 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76986766","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}