The occurrence regularity of wheat midgec (Sitodiplosis mosellana Gehin) was investigated in 1986 -1991. One generation of this kind of insects emergies every year,and its cocoon lies dormant in soil through the winte...The occurrence regularity of wheat midgec (Sitodiplosis mosellana Gehin) was investigated in 1986 -1991. One generation of this kind of insects emergies every year,and its cocoon lies dormant in soil through the winter and summer (total for over ten months). The active larvae have a habit to move up and down with the changes of soil temperature. The field experiment over three years has showed that DDVp, a slow-release pesticide,is significantly effective in reduction of wheat loss due to this pest. Thirteen wheat varieties which are resistant or tolerant to this pest have been obtained in Hebei province. The main points of the technique described in this paper is put in a nutshell: relying mainly on growing the resistant varieties of wheat while making chemical control subsidiary. The chemical control is carried out at the pupal stage to kill the insects as many as possible,and at the adult stage to wipe out the remnants. Over the last three years,as a result of the utilization of this technique in展开更多
Wheat [Triticum aestivum L. (Poaceae)] is the staple diet of people in Pakistan. It is attacked by many types of pests. Therefore the purpose of this study was to assess the impact of climate change on the ecology and...Wheat [Triticum aestivum L. (Poaceae)] is the staple diet of people in Pakistan. It is attacked by many types of pests. Therefore the purpose of this study was to assess the impact of climate change on the ecology and epidemiology of various wheat pests in Punjab, Pakistan. Results indicate that maximum weeds hot spots 242 (5.98%) Phalaris minor, 45 (1.18%) wild oat and 203 (5.01%) broad leaf weeds were noted in 2015. Aphid 31 (0.86%) hot spots were recorded in 2016 while maximum army worm 13 (0.26%) hot spots were noted in 2017. Maximum 70 (1.73%) spots of yellow rust and 85 (2.10%) hot spots of brown rust were observed during 2015 while 84 (4.16%) spots of loose smut were observed during 2017. ANOVA shows that years have no significant difference (P > 0.05) but weeks have significant effect on occurrence of these pest incidences except brown rust. But regression ANOVA was significant (P ≤ 0.05) and regression models equations have been developed on the bases of recorded data. Pest incidence was taken as dependent variable Y and weather factors i.e. minimum temperature as X1, maximum temperature as X2, relative humidity as X3 and rain fall as X4 were taken as independent variables. This study will help in recommendations for moving forward aiming at integration of biology of rust and smut diseases of wheat with changing climate for development of resistant varieties for resilient and durable management of these pathogens.展开更多
作物生长模型是评估作物生产、资源利用及气候变化影响等的有效工具,准确地确定作物模型参数是应用模型的关键。WheatSM(Wheat Growth and Development Simulation Model)模型已在作物生产优化管理上得到一定的应用,并取得较好的效果,...作物生长模型是评估作物生产、资源利用及气候变化影响等的有效工具,准确地确定作物模型参数是应用模型的关键。WheatSM(Wheat Growth and Development Simulation Model)模型已在作物生产优化管理上得到一定的应用,并取得较好的效果,但由于该模型参数较多,模型参数调试复杂。为了快速、准确地确定WheatSM模型参数,简化该模型的调参工作,促进其在农业气象领域中广泛应用,本研究在国内外作物模型参数自动调节方法的基础上,基于PEST(Parameter Estimation)方法构建了WheatSM模型参数的自动调节耦合系统,并对WheatSM模型的发育期和产量参数进行了自动寻优。选择北京上庄作为代表性试验点,以试验点的气象数据、土壤数据和2014~2016年冬小麦不同播期试验数据为基础,应用PEST参数自动优化方法和试错法分别对小麦生长模型WheatSM发育期参数和产量参数进行调试,并将优化结果和试错法的模拟结果进行比较。研究结果表明,基于PEST方法的模型参数调节精准度较高,模拟发育期的误差不大于7天,模拟产量的误差不大于228.63kg·hm^(-2)。同时,与试错法相比,PEST方法具有耗时少、可同时批量处理数据、更高效快捷等优点,使用该自动调参系统可减少参数率定的工作量,节省模型的操作时间,简化工作的复杂度和获得较高的模拟精度。该研究为WheatSM模型参数的自动优化提供一种便捷方法,为提高作物模型参数调试的效率和准确性提供了理论参考和指导。展开更多
[目的/意义]针对小麦叶片病虫害在自然环境下形态和颜色特征较为复杂、区分度较低等特点,提出一种高质量高效的病虫害检测模型,即YOLOv8-SS (You Only Look Once Version 8-SS),为病虫害的预防与科学化治理提供准确的依据。[方法]基于YO...[目的/意义]针对小麦叶片病虫害在自然环境下形态和颜色特征较为复杂、区分度较低等特点,提出一种高质量高效的病虫害检测模型,即YOLOv8-SS (You Only Look Once Version 8-SS),为病虫害的预防与科学化治理提供准确的依据。[方法]基于YOLOv8算法,采用改进的轻量级卷积神经网络ShuffleNet V2作为主干网络提取图像特征即YOLOv8-S,在保持检测精度的同时,减少模型的参数数量和计算负载;在此基础上增加小目标检测层和注意力机制SEnet (Squeeze and Excitation Network),对YOLOv8-S进行改进,在不降低检测速度和不损失模型轻量化程度的情况下提高检测精度,提出YOLOv8-SS小麦叶片病虫害检测模型。[结果与讨论]YOLOv8-SS模型在实验数据集上的平均识别精度和检测准确率分别达89.41%和91.00%,对比原模型分别提高10.11%和7.42%。因此,本研究所提出的方法可显著提高农作物病虫害的检测鲁棒性,并增强模型对小目标图像特征的提取能力,从而高效准确地进行病虫害的检测和识别。[结论]本研究使用的方法具有广泛适用性,可应用于大规模农作物病虫害检测的实际场景中。展开更多
文摘The occurrence regularity of wheat midgec (Sitodiplosis mosellana Gehin) was investigated in 1986 -1991. One generation of this kind of insects emergies every year,and its cocoon lies dormant in soil through the winter and summer (total for over ten months). The active larvae have a habit to move up and down with the changes of soil temperature. The field experiment over three years has showed that DDVp, a slow-release pesticide,is significantly effective in reduction of wheat loss due to this pest. Thirteen wheat varieties which are resistant or tolerant to this pest have been obtained in Hebei province. The main points of the technique described in this paper is put in a nutshell: relying mainly on growing the resistant varieties of wheat while making chemical control subsidiary. The chemical control is carried out at the pupal stage to kill the insects as many as possible,and at the adult stage to wipe out the remnants. Over the last three years,as a result of the utilization of this technique in
文摘Wheat [Triticum aestivum L. (Poaceae)] is the staple diet of people in Pakistan. It is attacked by many types of pests. Therefore the purpose of this study was to assess the impact of climate change on the ecology and epidemiology of various wheat pests in Punjab, Pakistan. Results indicate that maximum weeds hot spots 242 (5.98%) Phalaris minor, 45 (1.18%) wild oat and 203 (5.01%) broad leaf weeds were noted in 2015. Aphid 31 (0.86%) hot spots were recorded in 2016 while maximum army worm 13 (0.26%) hot spots were noted in 2017. Maximum 70 (1.73%) spots of yellow rust and 85 (2.10%) hot spots of brown rust were observed during 2015 while 84 (4.16%) spots of loose smut were observed during 2017. ANOVA shows that years have no significant difference (P > 0.05) but weeks have significant effect on occurrence of these pest incidences except brown rust. But regression ANOVA was significant (P ≤ 0.05) and regression models equations have been developed on the bases of recorded data. Pest incidence was taken as dependent variable Y and weather factors i.e. minimum temperature as X1, maximum temperature as X2, relative humidity as X3 and rain fall as X4 were taken as independent variables. This study will help in recommendations for moving forward aiming at integration of biology of rust and smut diseases of wheat with changing climate for development of resistant varieties for resilient and durable management of these pathogens.
文摘作物生长模型是评估作物生产、资源利用及气候变化影响等的有效工具,准确地确定作物模型参数是应用模型的关键。WheatSM(Wheat Growth and Development Simulation Model)模型已在作物生产优化管理上得到一定的应用,并取得较好的效果,但由于该模型参数较多,模型参数调试复杂。为了快速、准确地确定WheatSM模型参数,简化该模型的调参工作,促进其在农业气象领域中广泛应用,本研究在国内外作物模型参数自动调节方法的基础上,基于PEST(Parameter Estimation)方法构建了WheatSM模型参数的自动调节耦合系统,并对WheatSM模型的发育期和产量参数进行了自动寻优。选择北京上庄作为代表性试验点,以试验点的气象数据、土壤数据和2014~2016年冬小麦不同播期试验数据为基础,应用PEST参数自动优化方法和试错法分别对小麦生长模型WheatSM发育期参数和产量参数进行调试,并将优化结果和试错法的模拟结果进行比较。研究结果表明,基于PEST方法的模型参数调节精准度较高,模拟发育期的误差不大于7天,模拟产量的误差不大于228.63kg·hm^(-2)。同时,与试错法相比,PEST方法具有耗时少、可同时批量处理数据、更高效快捷等优点,使用该自动调参系统可减少参数率定的工作量,节省模型的操作时间,简化工作的复杂度和获得较高的模拟精度。该研究为WheatSM模型参数的自动优化提供一种便捷方法,为提高作物模型参数调试的效率和准确性提供了理论参考和指导。
文摘[目的/意义]针对小麦叶片病虫害在自然环境下形态和颜色特征较为复杂、区分度较低等特点,提出一种高质量高效的病虫害检测模型,即YOLOv8-SS (You Only Look Once Version 8-SS),为病虫害的预防与科学化治理提供准确的依据。[方法]基于YOLOv8算法,采用改进的轻量级卷积神经网络ShuffleNet V2作为主干网络提取图像特征即YOLOv8-S,在保持检测精度的同时,减少模型的参数数量和计算负载;在此基础上增加小目标检测层和注意力机制SEnet (Squeeze and Excitation Network),对YOLOv8-S进行改进,在不降低检测速度和不损失模型轻量化程度的情况下提高检测精度,提出YOLOv8-SS小麦叶片病虫害检测模型。[结果与讨论]YOLOv8-SS模型在实验数据集上的平均识别精度和检测准确率分别达89.41%和91.00%,对比原模型分别提高10.11%和7.42%。因此,本研究所提出的方法可显著提高农作物病虫害的检测鲁棒性,并增强模型对小目标图像特征的提取能力,从而高效准确地进行病虫害的检测和识别。[结论]本研究使用的方法具有广泛适用性,可应用于大规模农作物病虫害检测的实际场景中。
