刺槐光合生理生态特性日变化研究

为了解刺槐光合生理生态特性及生长的适宜生态条件,培育出优质高产的林木,采用LI-6400光合测定系统对刺槐的光合生理特性日变化及其与气象因子关系进行了研究。结果表：（1）刺槐的Pn日变化呈“双峰型”曲线,有明显的“午休”现象,第一个峰出现在9：00左右,第二个峰出现在15：00左右,第一个峰值明显高于第二个峰值。（2）Tr在很大程度上决定于气孔的活动状态,Ci因为Gs的降低而减少,刺槐的光合速率控制因子为气孔限制。（3）Pn与Gs成极显著正相关,与Tr成显著性正相关。环境因子主要是影响叶片的Tr、Vpdl和Tl来影响Pn,PAR对Pn的影响较大,但可能不是导致刺槐光合“午休”的直接因素。     

基于幅值迭代剪枝的多目标奶牛进食行为识别方法

针对奶牛进食行为监测通常要为每头奶牛配备监测设备,但受限于设备成本,很多应用于奶牛养殖场的奶牛行为监测方法难以普及的问题,提出了一种多目标奶牛进食行为识别方法,基于YOLO v3算法,根据目标差异,将牛舍中的奶牛分为3类目标来实现奶牛进食行为监测,以通过单台设备监测多头奶牛的进食行为.YOLO v3算法具有计算成本高、...     

基于改进DeepSORT的群养生猪行为识别与跟踪方法

为改善猪只重叠与遮挡造成的猪只身份编号（Identity,ID）频繁跳变,在YOLO v5s检测算法基础上,提出了改进DeepSORT行为跟踪算法。该算法改进包括两方面：一针对特定场景下猪只数量稳定的特点,改进跟踪算法的轨迹生成与匹配过程,降低ID切换次数,提升跟踪稳定性;二将YOLO v5s检测算法中的行为类别信息引入跟踪算法中,在跟踪中实现准确的猪只行为识别。实验结果表明,在目标检测方面,YOLO v5s的mAP为99.3%,F1值为98.7%。在重识别方面,实验的Top-1准确率达到99.88%。在跟踪方面,改进DeepSORT算法的MOTA为91.9%,IDF1为89.2%,IDS为33;与DeepSORT算法对比,MOTA和IDF1分别提升了1.0、16.9个百分点,IDS下降了83.8%。改进DeepSORT算法在群养环境下能够实现稳定ID的猪只行为跟踪,能够为无接触式的生猪自动监测提供技术支持。     

基于毛细管网的日光温室主动式集放热系统研究

为了对比日光温室传统保温蓄热后墙与基于毛细管网的主动式集放热系统（AHSCTM）的集放热性能,对AHSCTM的集放热性能进行了测试,构建了AHSCTM水温模型,利用一维差分法对相同环境条件下的外保温复合墙（370mm黏土砖和100mm聚苯乙烯板复合而成）日间储热量和夜间放热量进行了模拟。结果表明,AHSCTM的日间储热量和夜间放热量分别为相同条件下外保温复合墙的84.4%~111.3%和74.8%~100.7%,AHSCTM的COP（Coefficient of performance）为1.1~2.4。在夜间运行期间,AHSCTM放热量是相同时间段内外保温复合墙的98.2%~172.5%。因此,与外保温复合墙相比,AHSCTM有利于提高室内最低气温。改进AHSCTM的日间储热量和夜间放热量得到大幅提升,分别较外保温复合墙高67.6%~112.1%和69.0%~128.3%,COP可达2.8~7.0。改进AHSCTM的储放热性能优于外保温复合墙,说明利用改进AHSCTM配合保温墙体替代传统保温蓄热后墙是可行的。     

基于机器视觉的奶牛发情行为自动识别方法

及时检测奶牛发情、适时人工授精、减少空怀奶牛,是奶牛养殖场增加产奶量的关键手段。针对基于运动量和体温等体征的接触式奶牛发情识别方法会造成奶牛应激反应且识别准确率不高的问题,提出了一种非接触式奶牛发情行为自动识别方法。该方法首先使用改进的高斯混合模型实现运动奶牛目标检测,然后基于颜色和纹理信息去除干扰背景,再利用AlexNet深度学习网络训练奶牛行为分类网络模型,识别奶牛爬跨行为,最终实现对奶牛发情行为的自动识别。在供试数据集上的试验结果表明,本文方法对奶牛发情的识别准确率为100%,召回率为88.24%。本文方法可应用于奶牛养殖场的日常发情监测中,为生产管理提供辅助决策。     

大型喷杆喷雾机钟摆式主被动悬架自适应鲁棒控制研究

针对大型喷雾机喷杆钟摆式主被动悬架系统存在的参数不确定性和随机干扰导致控制精度低、稳定性差的问题,对基于模型补偿的自适应鲁棒控制算法进行研究。建立了钟摆式主被动悬架的非线性动力学模型和调节机构几何方程,基于模型设计了自适应鲁棒控制器,综合悬架系统和电液位置伺服系统模型中存在的参数不确定性,同时兼顾系统未补偿的摩擦力和外部扰动等不确定非线性因素,通过理论分析和试验证明,在同时存在模型参数不确定和不确定非线性的情况下,设计的控制器可以保证系统输出跟踪控制的暂态性能和稳态精度。以单出杆液压作动器驱动的28m大型喷杆主被动悬架为例,借助建立的大型喷杆悬架半实物仿真平台进行了控制算法的试验验证,并使用Stewart六自由度运动平台模拟底盘的运动干扰,与反馈线性化控制器、鲁棒反馈控制器、PID控制器进行了试验对比,结果表明,设计的基于模型补偿的自适应鲁棒控制器最大跟踪误差0.148°,而反馈线性化控制器最大跟踪误差0.201°,鲁棒反馈控制器最大跟踪误差0.51°,PID控制器最大跟踪误差0.48°。设计的控制器在同时存在参数不确定性和扰动的情况下,使用较小的反馈增益能够保证渐进跟踪性能和稳态跟踪精度。     

基于YOLO v3与图结构模型的群养猪只头尾辨别方法

在利用视频监控技术对群养猪只进行自动行为监测时,对猪只准确定位并辨别其头尾位置对提高监测水平至关重要,基于此提出一种基于YOLO v3(You only look once v3)模型与图结构模型(Pictorial structure models)的猪只头尾辨别方法。首先,利用基于深度卷积神经网络的YOLO v3目标检测模型,训练猪只整体及其头部和尾部3类目标的检测器,从而在输入图像中获得猪只整体及头尾部所有的检测结果;然后,引入图结构模型,描述猪只的头尾结构特征,对每个猪只整体检测矩形框内的头尾部位组合计算匹配得分,选择最优的部位组合方式;对部分部位漏检的情况,采取阈值分割与前景椭圆拟合的方法,根据椭圆长轴推理出缺失部位。在实际猪场环境下,通过俯拍获得猪舍监控视频,建立了图像数据集,并进行了检测实验。实验结果表明,与直接利用YOLO v3模型相比,本文方法对头尾定位的精确率和召回率均有一定提高。本文方法对猪只头尾辨别精确率达到96.22%,与其他方法相比具有明显优势。     

Does root-sourced ABA play a role for regulation of stomata under drought in quinoa (Chenopodium quinoa Willd.)

The Andean seed crop quinoa (Chenopodium quinoa Willd.) is traditionally grown under drought and other adverse conditions that constrain crop production in the Andes, and it is regarded as having considerable tolerance to soil drying. The objective of this research was to study how chemical and hydraulic signalling from the root system controlled gas exchange in a drying soil in quinoa. It was observed that during soil drying, relative g_s and photosynthesis A_max (drought stressed/fully watered plants) equalled 1, until the fraction of transpirable soil water (FTSW) decreased to 0.82 ± 0.152 and 0.33 ± 0.061, respectively, at bud formation, indicating that photosynthesis was maintained after stomata closure. The relationship between relative g_s and relative A_max at bud formation was represented by a logarithmic function (r² = 0.79), which resulted in a photosynthetic water use efficiency WUE_Amax_/gs$\text{WU}{\text{E}}_{A_{\text{max}}/g_{\text{s}}}$ of 1 when FTSW > 0.8, and increased by 50% with soil drying to FTSW 0.7–0.4. Mild soil drying slightly increased ABA in the xylem. It is concluded that during soil drying, quinoa plants have a sensitive stomatal closure, by which the plants are able to maintain leaf water potential (ψ_l) and A_max, resulting in an increase of WUE. Root originated ABA plays a role in stomata performance during soil drying. ABA regulation seems to be one of the mechanisms utilised by quinoa when facing drought inducing decrease of turgor of stomata guard cells.     

Changes in fox squirrel anti-predator behaviors across the urban–rural gradient

Predator stimuli created by humans in the urban environment may alter animals’ anti-predator behaviors. I hypothesized that habituation would cause anti-predator behaviors to decrease in urban settings in response to humans. Additionally, I hypothesized that populations habituated to humans would show reduced responses to other predator stimuli. I observed three populations of squirrels (urban, suburban and rural) responses to human approaches, red-tailed hawk vocalizations (Buteo jamaicensis) and coyote (Canis latrans) vocalizations. Mahalanobis distances of anti-predator behaviors in response to human approaches were consistent with the urban–rural gradient. Flight initiation distances (X ² = 26.33, df = 2, P < 0.001) and amount of time dedicated to anti-predator behavior (X ² = 10.94, df = 2, P = 0.004) in response to human approaches were also consistent with the urban–rural gradient. Supporting the habituation hypothesis, naive juvenile squirrels increased flight initiation distances (X ² = 35.89, df = 1, P < 0.001) and time dedicated to anti-predator behaviors (X ² = 9.46, df = 1, P = 0.002) relative to adult squirrels in the same urban environment. Time dedicated to anti-predator behaviors differed among all three sites in response to both coyote (X ² = 9.83, df = 2, P = 0.007) and hawk (X ² = 6.50, df = 2, P = 0.035) vocalizations. Responses to both vocalizations on rural sites (coyote = 45%, hawk = 55%) greater than twice that found on the urban sites (coyote = 11%, hawk = 20%). This is possibly the first case of a transfer of habituation demonstrated under field conditions.     

Simulation of transpiration, drainage, N uptake, nitrate leaching, and N uptake concentration in tomato grown in open substrate

Free-drainage or “open” substrate system used for vegetable production in greenhouses is associated with appreciable NO₃⁻ leaching losses and drainage volumes. Simulation models of crop N uptake, N leaching, water use and drainage of crops in these systems will be useful for crop and water resource management, and environmental assessment. This work (i) modified the TOMGRO model to simulate N uptake for tomato grown in greenhouses in SE Spain, (ii) modified the PrHo model to simulate transpiration of tomato grown in substrate and (iii) developed an aggregated model combining TOMGRO and PrHo to calculate N uptake concentrations and drainage NO₃⁻ concentration. The component models simulate NO₃⁻-N leached by subtracting simulated N uptake from measured applied N, and drainage by subtracting simulated transpiration from measured irrigation. Three tomato crops grown sequentially in free-draining rock wool in a plastic greenhouse were used for calibration and validation. Measured daily transpiration was determined by the water balance method from daily measurements of irrigation and drainage. Measured N uptake was determined by N balance, using data of volumes and of concentrations of NO₃⁻ and NH₄⁺ in applied nutrient solution and drainage. Accuracy of the two modified component models and aggregated model was assessed by comparing simulated to measured values using linear regression analysis, comparison of slope and intercept values of regression equations, and root mean squared error (RMSE) values. For the three crops, the modified TOMGRO provided accurate simulations of cumulative crop N uptake, (RMSE = 6.4, 1.9 and 2.6% of total N uptake) and NO₃⁻-N leached (RMSE = 11.0, 10.3, and 6.1% of total NO₃⁻-N leached). The modified PrHo provided accurate simulation of cumulative transpiration (RMSE = 4.3, 1.7 and 2.4% of total transpiration) and cumulative drainage (RMSE = 13.8, 6.9, 7.4% of total drainage). For the four cumulative parameters, slopes and intercepts of the linear regressions were mostly not statistically significant (P < 0.05) from one and zero, respectively, and coefficient of determination (r²) values were 0.96-0.98. Simulated values of total drainage volumes for the three crops were +21, +1 and −13% of measured total drainage volumes. The aggregated TOMGRO-PrHo model generally provided accurate simulation of crop N uptake concentration after 30-40 days of transplanting, with an average RMSE of approximately 2 mmol L⁻¹. Simulated values of average NO₃⁻ concentration in drainage, obtained with the aggregated model, were −7, +18 and +31% of measured values.