前桥摆转转向式四驱底盘结构形状指数的优化

前桥摆转转向式四驱底盘可直接转入下一畦作业,提高了小地块工作行程率,在地头宽度小于等于1个作业宽度时,更有利于机耕过程中的转向操作。为此,通过ADAMS软件仿真,以最小地头宽度为目标对底盘结构参数进行了优化。仿真结果表明,底盘在作业过程中从一畦进入地头直接转入相邻的下一畦时,后内侧轮基本上不吃入上一畦,最小地头宽度与长宽比成反比。在底盘转向时行走轮不干涉后悬挂农具的前提下,底盘最小的理论长宽比应为0.5:1。但由于底盘长宽比受行走轮直径、轮宽及重心的影响,底盘的合理长宽比值范围需趋近于理论长宽比。当机具作业宽幅大于等于机具轮宽时,可直接做倒U形转向;否则采用"4"和歪梨形组合掉头转向,实现地头宽度等于1个作业幅宽的转向。     

基于空间运行轨迹的农机往复作业行距估算方法

为了快速评估农机作业重漏状况,针对农机往复作业模式下,通过在农机上装载智能终端设备,采集车载终端GNSS信息,分析农机空间运行轨迹数据,统计方位角分布频次,计算农机作业方向,提取农机作业线,进一步建立了农机作业行距评估模型。为了验证农机作业行距评估模型,于2021年6月25日在北京小汤山国家精准农业研究示范基地开展实验,利用装载有自动导航系统的拖拉机,设定不同的行距,且每个行距进行往复直线作业,验证作业方向计算、作业线提取和作业行距提取等模型。结果表明,与实际相比,模型计算的作业行距平均误差为3.07%,均方根误差为0.14m,从整体上来看,识别的农机作业平均行距和实际行距比较相符,可为农机作业重叠、遗漏等农机作业质量评估提供数据支撑。     

Long-term impacts of an organophosphate-based regime of pesticides on field and field-edge Collembola communities

During a 6-year study, effects of two contrasting regimes of pesticide use on pitfall and suction catches of Collembola were monitored in an arable field under a rotation of grass and winter wheat. Current farm practice (CFP) represented conventional fungicide and herbicide use plus applications of organophosphorus (OP) insecticides, whereas reduced input approach (RIA) utilised minimum inputs of fungicides and herbicides and excluded any use of insecticides. Compared with RIA, the CFP regime caused a substantial decline in the abundance and diversity of Collembola in the field, including the local disappearance of one species, without recovery during the study. At the field edge, which was protected during OP applications by a 6-m unsprayed buffer zone, effects of the CFP regime were less severe, and were not persistent in the long term. Some Collembola species occurred only in field-edge samples. Pitfall and suction sampling yielded remarkably similar patterns of catches, indicating that pitfall trapping may be appropriate for detecting long-term changes in collembolan abundance caused by intensive agricultural management practices.     

基于机器视觉的农田地头边界线检测方法

在非结构化复杂农田作业环境中,为实现农机在地头处的自主导航转弯,首先需及时、准确地感知地头的空间位置信息,尤其是地头边界位置。本文基于机器视觉技术,首先依据农田内外像素灰度的跳变特征来判断地头是否出现,通过建立正向和负向分布偏差两个度量确定是否存在该灰度跳变特征;随后,将图像沿水平方向平均分成8个子处理区域,针对各子处理区域求取其行灰度平均值分布图,基于局部加权回归法对其进行平滑处理,建立按序离群度参数,通过寻找平滑曲线上首个按序离群程度较大的波峰点或波谷点以及相应的跳前波谷点或波峰点,最终确定跳变特征点的像素坐标,并基于稳健回归法线性拟合跳变特征点,获取实际非规整地头边界的主体延伸方位线;最后,将主体延伸方位线向下平行移动,当其线上像素的灰度平均值接近于田内像素的灰度分布特征时,认为抵达安全位置处,由此获得农机在当前地头处安全转向掉头的边界线。试验结果表明,判断地头出现的准确率不低于96%,地头边界线检测准确率不低于92%。