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| 1 | 基于深度学习与图像处理的玉米茎秆识别方法与试验显示文摘以识别玉米秧苗茎秆为目标,采用云台搭载电荷耦合器件(CCD)相机获得玉米秧苗图像,采用Label Image插件制作了玉米秧苗的标记与标签。基于深度学习框架Tensor Flow搭建了多尺度分层特征的卷积神经网络模型,应用4倍膨胀的单位卷积核,获得了玉米秧苗图像的识别模型,其识别准确率为99.65%。将已知玉米秧苗图像划分为最佳子块,求取了各个子块的最佳二值化阈值。选取6种杂草密度在每天5个时间段进行为期3 d的试验,共采集了10800幅图像。试验结果显示,对玉米秧苗茎秆的平均识别准确率为98.93%,且光照条件与田间杂草密度对识别结果没有显著影响(P>0.05)。 | 刘慧力 贾洪雷 王刚 GLATZEL Stephan 袁洪方 黄东岩 | 2020 | 农业机械学报2020，51，4： | 25 |
| 2 | Growing-season soil microbial respiration response to long-term no tillage and spring ridge tillage显示文摘No tillage(NT)and spring ridge tillage(SRT)are two common applications of conservation tillage.Although conservation tillage is known to exert major control over soil microbial respiration(SMR),the growing-season SMR response to these two applications remains elusive.In order to better understand the influence of conservation tillage practices,this experiment was conducted in an experimental field using NT and SRT for 17 years.In situ measurements of SMR,soil temperature and soil water content(SWC)were performed.Soil samples were collected to analyze soil porosity,soil microbial biomass(SMB)and soil enzymatic activities.Results show that the two conservation tillage systems had a significant difference(p<0.05)in terms of SMR;the SMR of NT was 14.7 mg∙C/m^(2)∙h higher than that of SRT.In terms of soil temperature and soil enzymatic activities,the two treatments were not significantly different(p>0.05).Despite SRT increasing the proportion of micro-porosities and meso-porosities,the soil macro-porosities for NT were 7.37%higher than that of SRT,which resulted in higher bacteria and fungi in NT.Owing to SRT damaged the hypha,which had disadvantage in soil microbe protection.Inversely,less soil disturbance was a unique advantage in NT,which was in favor of improving soil macro-pores and SWC.Redundancy analyses(RDA)showed SMR was positively correlated with soil macro-pores,SMB and SWC.Furthermore,the Pearson correlation test indicated that SMB and soil enzymatic activities did not have a significant correlation(p>0.05).This study results suggest that SRT is more conducive to carbon sequestration compared with NT in cropland. | Gang Wang Honglei Jia Jian Zhuang Stephan Glatzel John McLBennett Yingcan Zhu | 2020 | International Journal of Agricultural and Biological Engineering2020，13，4： | 0 |
| 3 | Impacts of increasing maize stalk retention amount on soil respiration and temperature sensitivity显示文摘Conservation tillage with maize stalk retention is an effective method to replenish soil nutrients.Nutrient availability plays a major role in the control of soil respiration(SR).However,it is not known how different degrees of maize stalk retention control SR and its temperature sensitivity(Q_(10)).To investigate the effect of maize stalk retention amount on SR and Q_(10),four maize(Zea mays L.)stalk retention treatments,including(i)control treatment(CT)without maize stalk retention,(ii)standing maize stalk retention(SCR),(iii)partial maize stalk retention with‘three-year cycle’(TYR)and(iv)chopped maize stalk retention(CCR)was set up.In order to investigate the differences in soil nutrient,soil organic carbon(SOC)quality and soil microbial biomass among four treatments,soil analysis with 6 replicates was conducted.The experimental results showed that SR rates were 1.07,0.88,0.59 and 0.37 g/kg of dry soil,and the average Q_(10)was 1.535,1.585,1.62 and 1.725 for CT,SCR,TYR and CCR,respectively.Increasing maize stalk retention led to the reduction of soil microbial abundance and labile carbon compositions.Pearson correlation analysis showed that soil microbial abundance had a positive correlation with SR,while labile carbon fraction had a negative correlation with Q_(10).In short,increasing the amount of maize stalk retention decreases SR while increasing Q_(10)in northeast China.This research could provide a reference value for balancing carbon sequestration and carbon decomposition in farming practice. | Hongfang Yuan Gang Wang Dongyan Huang Stephan Glatzel Jian Zhuang Honglei Jia | 2022 | International Journal of Agricultural and Biological Engineering2022，15，2： | 0 |
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