Wheat streak mosaic (WSM), caused by Wheat streak mosaic virus is a viral disease that affects wheat (Triticum aestivum L.), other grains, and numerous grasses over large geographical areas around the world. To improv...Wheat streak mosaic (WSM), caused by Wheat streak mosaic virus is a viral disease that affects wheat (Triticum aestivum L.), other grains, and numerous grasses over large geographical areas around the world. To improve disease management and crop production, it is essential to have adequate methods for monitoring disease epidemics at various scales and multiple times. Remote sensing has become an essential tool for monitoring and quantifying crop stress due to biotic and abiotic factors. The objective of our study was to explore the utility of Landsat 5 TM imagery for detecting, quantifying, and mapping the occurrence of WSM in irrigated commercial wheat fields. The infection and progression of WSM was biweekly assessed in the Texas Panhandle during the 2007-2008 crop years. Diseased-wheat was separated from uninfected wheat on the images using a sub-pixel classifier. The overall classification accuracies were >91% with kappa coefficient between 0.80 and 0.94 for disease detection were achieved. Omission errors varied between 2% and 14%, while commission errors ranged from 1% to 21%. These results indicate that the TM image can be used to accurately detect and quantify disease for site-specific WSM management. Remote detection of WSM using geospatial imagery may substantially improve monitoring, planning, and management practices by overcoming some of the shortcomings of the ground-based surveys such as observer bias and inaccessibility. Remote sensing techniques for accurate disease mapping offer a unique set of advantages including repeatability, large area coverage, and cost-effectiveness over the ground-based methods. Hence, remote detection is particularly and practically critical for repeated disease mo- nitoring and mapping over time and space during the course of a growing season.展开更多
为了给小麦科学防病和抗(耐)病品种的筛选提供参考依据,在小麦黄花叶病重发区,以当地种植品种和黄淮冬麦区主推品种为试验材料,研究了小麦黄花叶病对不同小麦品种叶片SPAD(Soil and Plant Analyzer Development)值、春季总茎数、干物质...为了给小麦科学防病和抗(耐)病品种的筛选提供参考依据,在小麦黄花叶病重发区,以当地种植品种和黄淮冬麦区主推品种为试验材料,研究了小麦黄花叶病对不同小麦品种叶片SPAD(Soil and Plant Analyzer Development)值、春季总茎数、干物质积累量以及成熟期产量性状的影响。结果表明,感病小麦品种临麦4号和矮抗58于起身期(3月11日)开始出现症状,与无症状的临麦4号植株(LM4-N)相比,发病的临麦4号植株(LM4-S)的叶片SPAD值由52.4降至37.3;拔节后期(4月10日)所有品种SPAD值之间均无显著差异。然而,与LM4-N相比,拔节后期LM4-S单位面积总茎数减少51.4%,干物质积累量降低42.2%;成熟期LM4-S单位面积穗数降低21.2%,穗粒数降低24.5%,籽粒产量降低2 944.3kg·hm-2,降幅38.3%。而该病对本研究中的其他抗病表现较好的品种鲁原502、济麦22、良星99和烟农24均未产生显著影响。结合各品种的产量表现,建议在小麦黄花叶病发病地区种植推广鲁原502、济麦22等高产、抗(耐)病性较好的品种。展开更多
文摘Wheat streak mosaic (WSM), caused by Wheat streak mosaic virus is a viral disease that affects wheat (Triticum aestivum L.), other grains, and numerous grasses over large geographical areas around the world. To improve disease management and crop production, it is essential to have adequate methods for monitoring disease epidemics at various scales and multiple times. Remote sensing has become an essential tool for monitoring and quantifying crop stress due to biotic and abiotic factors. The objective of our study was to explore the utility of Landsat 5 TM imagery for detecting, quantifying, and mapping the occurrence of WSM in irrigated commercial wheat fields. The infection and progression of WSM was biweekly assessed in the Texas Panhandle during the 2007-2008 crop years. Diseased-wheat was separated from uninfected wheat on the images using a sub-pixel classifier. The overall classification accuracies were >91% with kappa coefficient between 0.80 and 0.94 for disease detection were achieved. Omission errors varied between 2% and 14%, while commission errors ranged from 1% to 21%. These results indicate that the TM image can be used to accurately detect and quantify disease for site-specific WSM management. Remote detection of WSM using geospatial imagery may substantially improve monitoring, planning, and management practices by overcoming some of the shortcomings of the ground-based surveys such as observer bias and inaccessibility. Remote sensing techniques for accurate disease mapping offer a unique set of advantages including repeatability, large area coverage, and cost-effectiveness over the ground-based methods. Hence, remote detection is particularly and practically critical for repeated disease mo- nitoring and mapping over time and space during the course of a growing season.
