International Journal of Agricultural and Biological Engineering最新文献_第9页

Development of the precision feeding system for sows via a rule-based expert system 基于规则的专家系统开发母猪精密饲养系统

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231602.7427

Chong Chen, Xingqiao Liu, Chao Liu, Qin Pan

{"title":"Development of the precision feeding system for sows via a rule-based expert system","authors":"Chong Chen, Xingqiao Liu, Chao Liu, Qin Pan","doi":"10.25165/j.ijabe.20231602.7427","DOIUrl":"https://doi.org/10.25165/j.ijabe.20231602.7427","url":null,"abstract":": To precisely meet the nutritional requirements of sows during the stages of pregnancy and lactation, a precision feeding system was developed by using the intelligent sow feeder combined with a rule-based expert system and the Internet of Things (IoTs). The model of uncertain knowledge representation was established for inference by using the certainty factor. The daily feeding amount of each sow was calculated by the expert system. An improved pattern matching algorithm Reused Degree Model-RETE (RDM-RETE) was proposed for the decision of daily feeding amount, which sped up inference by optimizing the RETE network topology. A prediction model of daily feeding amount was established by a rule-based expert system and the precision feeding was achieved by an accurate control technology of variable volume. The experimental results demonstrated that the HASH-RDM-RETE algorithm could effectively reduce the network complexity and improve the inference efficiency. The feeding amount decided by the expert system was a logarithmic model, which was consistent with the feeding law of lactating sows. The inferential feeding amount was adopted as the predicted feed intake and the coefficient of correlation between predicted feed intake and actual feed intake was greater than or equal to 0.99. Each sow was fed at different feeding intervals and different feed amounts for each meal in a day. The feed intake was 26.84% higher than that of artificial feeding during lactation days ( p <0.05). The piglets weaned per sow per year (PSY) can be increased by 1.51 compared with that of relatively high levels in domestic pig farms. This system is stable in feeding and lowers the breeding cost that can be applied in precision feeding in swine production.","PeriodicalId":13895,"journal":{"name":"International Journal of Agricultural and Biological Engineering","volume":"7 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76365158","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}

引用次数: 1

Smart control system for the precision cultivation of black fungus 黑木耳精密栽培智能控制系统

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231601.6257

Yongcheng Jiang, Jianjun Li, Yang Li, Shide Li, Xin Song, H. Wu, Shushan Zhu

引用次数: 0

Separation and mechanical properties of residual film and soil 残膜与土壤的分离与力学特性

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231601.7688

Yu Ren, Wensong Guo, Xufeng Wang, Can Hu, Long Wang, Xiaowei He, Jianfei Xing

引用次数: 0

Concurrent channel and spatial attention in Fully Convolutional Network for individual pig image segmentation 猪个体图像分割的全卷积网络并行通道和空间注意

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231601.6528

Zhiwei Hu, Hua Yang, T. Lou, Hong-Ping Yan

引用次数: 3

Design of a fixed-pipe cold aerosol spraying system for chemical application in greenhouse 温室化工用固定管冷喷雾系统的设计

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231601.6573

Shilin Wang, Daipeng Lu, Xue Li, Xiaohui Lei, Yuxin Tang, Xiaolan Lyu

引用次数: 0

Recognition of field roads based on improved U-Net++ Network 基于改进U-Net++网络的野外道路识别

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231602.7941

Lili Yang, Yuanbo Li, Mengshuai Chang, Yuanyuan Xu, Bingbing Hu, Xinxin Wang, Caicong Wu

{"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}

引用次数: 0

Experimental and numerical study on the shrinkage-deformation of carrot slices during hot air drying 热风干燥过程中胡萝卜片收缩变形的实验与数值研究

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231601.6736

Dalong Jiang, Congcong Li, Zifan Lin, Yun-tian Wu, Hongjuan Pei, M. Zielińska, Hongwei Xiao

引用次数: 1

Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM 整合田间图像和小气候数据，利用CNN-LSTM实现玉米作物覆盖多日预报

IF 2.4 2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231602.7020

Xin Wang, Yu Yang, Xin Zhao, Min Huang, Qibing Zhu

{"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":"31 1","pages":""},"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}

引用次数: 0

Effects of LED light spectrum on the growth and energy use efficiency of eggplant transplants LED光谱对茄子移栽生长及能源利用效率的影响

2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231603.7260

Hao Yang, Ting Wang, Fang Ji, Qing Zhou, Jianfeng Wang

{"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}

引用次数: 0

Energy use efficiency and greenhouse gas emissions (GHG) analysis of garlic cultivation in Turkey 土耳其大蒜种植的能源利用效率和温室气体排放(GHG)分析

2区农林科学

International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI: 10.25165/j.ijabe.20231604.7599

Mehmet Fırat Baran, Cihan Demir, Ahmet Konuralp Eliçin, Osman Gökdoğan

{"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}

引用次数: 0