基于图像处理技术的小麦条锈病严重度自动分级系统构建

条锈病是小麦上最重要的病害之一,严重影响小麦安全生产。该病害的严重度准确评估对病害预测和病害管理措施制定至关重要。为了实现小麦条锈病严重度的准确评估,本研究基于图像处理技术建立了小麦条锈病严重度自动评估方法,构建了小麦条锈病严重度自动分级系统。借助图像处理软件对获取的小麦条锈病各个严重度级别的发病单叶图像进行了手动分割,获取叶片区域图像和病斑区域图像,并统计获得每一发病单叶的叶片区域和病斑区域的像素数量,计算获得了每一发病单叶的病斑面积占发病叶片总面积的实际百分率。基于图像处理技术,利用4种图像分割方法对发病单叶图像进行叶片区域和病斑区域自动分割,与利用图像处理软件对病害图像进行手动分割所获取结果进行比较,获得了最优的病害图像自动分割方法。利用最优病害图像自动分割方法情况下获得的每一发病单叶的病斑面积占发病叶片总面积的百分率,分别依据每一个严重度级别的基于平均值中间值的实际百分率参考范围和病斑面积实际百分率的99%参考值范围,对每一发病单叶进行严重度评估,评估结果表明,基于病斑面积实际百分率的99%参考值范围的严重度评估方法最优,其严重度评估平均准确率为88.19%。利用最优的病斑图像...     

基于图像处理的玉米叶片含水率诊断方法研究

研究了利用数字图像处理技术进行作物叶片含水率诊断的方法.以温室中培育的90株不同灌水量的盆栽玉米为研究对象,使用佳能IXUS110的1 210万像素数码相机采集离体抽穗期玉米叶片的图像信息,然后利用烘干法测量叶片样本的含水率;利用叶片图像的灰度直方图提取叶片图像的均值、峰态、方差、歪斜度、能量、熵六组特征值.利用提取的20组玉米叶片样本的数据,采用线性回归的方法建立均值与玉米叶片含水率之间的关系模型;使用其余20组样本对模型进行验证,其标准差为0.021.结果表明,利用作物叶片灰度直方图均值参数可以对玉米的叶片含水率进行预测.     

国内一种玉米新病害--玉米黑腐病

1999年7月云南省元谋县城郊玉米地发现一种玉米新病害,发病面积大约70hm2.其典型症状表现为植株矮化,节间缩短,叶片密集簇生,叶片上有黄绿相间的条斑.叶鞘上有坏死或腐烂病斑,幼穗腐烂,不实,严重者绝产,颗粒无收.从坏死或腐烂的叶鞘和果穗上可镜检到分生孢子及分生孢子梗.纯化培养后,并按柯赫氏法则进行接种试验,病原鉴定为柔弱无枝孢Acladium tenellum(Berk.& Curt.)Subram.comb.nov..本文对病害症状、病原形态、发病情况等进行了详细研究,确定该病为玉米一种新病害.     

洛阳牡丹新病害—柱枝孢叶斑病

为了明确河南省洛阳地区牡丹叶部一种新病害的病原种类,对采自田间的典型症状叶片进行了常规组织分离,对单孢菌株进行了形态鉴定、rDNA ITS序列分析及致病性测定.结果表明,病原菌在PDA培养基上菌落初期白色绒毛状,后期红褐色.分生孢子梗通常呈扫帚状,有2～3个分支,末端膨大呈泡囊状.分生孢子直棒状,两端钝圆,有0～1个隔膜,大小为(38.4～86.4)μm× (4.6～5.9)μm,平均60.1 μm×5.2 μm.离体叶片与活体植株接种结果表明,该菌均能侵染牡丹叶片并引起与田间相同的症状.据此将分离物鉴定为Cylindrocladium canadense,将该病害命名为牡丹柱枝孢叶斑病.     

未来农作物病害发生趋势分析及控制对策探讨

通过从生物进化、生物间的动态平衡、农业生产发展的历史及市场经济等方面对未来农作物病害发生为害趋势和加重发生原因进行的系统分析,认为从总体上看,未来农作物病害的发生为害仍有加重的趋势。进而从宏观上对未来农作物病害的控制对策进行了探讨。     

农作物病害的产量损失叶部病害类

防治病害的目的在于保产,因此需要研究明确各类病害的不同发病程度对农作物产量损失的影响,以便制定出合理的防治指标和防治措施,从而收到最大的经济效益。 农作物的产量是一个众多因素综合影响的结果,因此估算病害对产量的损失程度也是一个极为复杂的问题。农作物的叶部病害,大多数是由病菌吸取寄主体内的营养,减少寄主的光合叶面积并干扰其有机物质运输积累和水分生理等从而导致减产,而并不直接为害农作物的果、穗等收获部分,这样,病害发生的强度和产量的关系就更加复杂化了。有时由于植物本身的补偿能力,使得病害在一定范围内对农作物产量并没有影响,这就要求我们制定出病害引起产量损失     

对水稻白叶枯病、纹枯病分级标准的商榷

病情指数是病害发生普遍程度与严重程度的综合体现,其数值越大,表示病害越严重。但是,要计算病情指数必须对病害进行分级,在水稻白叶枯病、纹枯病的调查过程中,当出现白叶枯病病斑面积界于叶面积的1/2—3/5之间,纹枯病顶叶以下各叶鞘或叶片发病时,参照《农作物主要病虫测报办法》分级标准就无法划定级别,笔者认为这两种     

涝灾对农作物病害发生的影响及其防治措施

在农业生产中时常会遇到各种各样的气象灾害 ,我区常见的主要气象灾害有水灾、旱灾、冷害、风雹灾 4大类。2 0 0 1年由于受来自南太平洋 3号台风“榴连”及 4号台风“尤特”的影响 ,广西 7月上旬普降大雨 ,发生了百年一遇的特大洪涝灾害 ,致使农作物受害面积超过 4万 hm2 ,其中绝收面积超过 1 .33万hm2 ,损失惨重。本文主要对涝灾后农作物病害的发生趋势进行分析 ,并介绍涝灾后农作物病害的防治措施。1　植物病害的发生特点　　植物病害可粗分为生理性病害与侵染性 (传染性 )病害两大类 ,前者的致病因素包括缺肥、营养元素过量、重金属或药…     

武威市玉米苗期叶枯病病原及发生特点

２０００年武威市凉州区和古浪县玉米制种田普遍发生一种苗期叶枯类病害 ,其症状与目前已报道的各种玉米苗期病害均有明显差别 ,生产中防治难度较大。为了及时掌握该病的致病菌及发生规律 ,为大田防治提供依据 ,笔者对该病害的发生危害情况进行了全面调查 ,并配合省农业科学院植保所 ,对病原菌进行了鉴定。１为害症状该病害典型症状为 :叶片生灰绿色至黄白色线状条斑 ,与叶脉平行 ,具不规则或波浪形边缘 ;喇叭口多不张开且叶尖端向下扭曲 ;玉米节部呈褐色。多数病株根系发育正常 ,亦有少数病株根毛减少 ,初生根老化 ,次生根少或无 ,叶片自下…     

广西防城金花茶病虫害调查初报

为摸清广西防城金花茶保护区金花茶病虫害的种类及其发生危害情况,采用普查和抽查相结合的方法,并参考相关文献,2009-2010年对防城金花茶保护区金花茶主要病虫害种类及危害程度进行调查和鉴定.结果表明,防城金花茶保护区金花茶病害有6种,其中危害叶片的病害主要有炭疽病、赤叶枯病、藻斑病、煤烟病等;虫害有10种,以食叶性害虫为主.分析了原生群落不同海拔的金花茶植株与单株叶片藻斑病及炭疽病发病率的差异.提出了对金花茶病虫害防治工作的建议.     

Attention embedded lightweight network for maize disease recognition

Crop disease has a negative impact on food security. If diverse crop diseases are not identified in time, they can spread and influence the quality, quantity, and production of grain. Severe crop diseases can even result in complete failure of the harvest. Recent developments in deep learning, particularly convolutional neural networks (CNNs), have exhibited impressive performance in both image recognition and classification. In this study, we propose a novel network architecture, namely Mobile-DANet, to identify maize crop diseases. Based on DenseNet, we retained the structure of the transition layers and used the depthwise separable convolution in dense blocks instead of the traditional convolution layers, and then embedded the attention module to learn the importance of interchannel relationship and spatial points for input features. In addition, transfer learning was used in model training. By this means, we improved the accuracy of the model while saving more computational power than deep CNNs. This model achieved an average accuracy of 98.50% on the open maize data set, and even with complicated backdrop conditions, Mobile-DANet realized an average accuracy of 95.86% for identifying maize crop diseases on a local data set. The experimental findings show the effectiveness and feasibility of the Mobile-DANet. Our data set is available at https://github.com/xtu502/maize-disease-identification .     

Identification of tea foliar diseases and pest damage under practical field conditions using a convolutional neural network

Lesions of tea (Camellia sinensis) leaves are detrimental to the growth of tea crops. Their adverse effects include further disease of tea leaves and a direct reduction in yield and profit. Therefore, early detection and on-site monitoring of tea leaf lesions are necessary for effective management to control infections and prevent further yield loss. In this study, 1,822 images of tea leaves with lesions caused by three diseases (brown blight, Colletotrichum camelliae; blister blight, Exobasidium vexans; and algal leaf spot, Cephaleuros virescens) and four pests (leaf miner, Tropicomyia theae; tea thrip, Scirtothrips dorsalis; tea leaf roller, Homona magnanima; and tea mosquito bug, Helopeltis fasciaticollis) were collected from northern and central Taiwan. A faster region-based convolutional neural network (Faster R-CNN) was then trained to detect the locations of the lesions on the leaves and to identify the causes of the lesions. The trained Faster R-CNN detector achieved a precision of 77.5%, recall of 70.6%, an F1 score of 73.91%, and a mean average precision of 66.02%. An overall accuracy of 89.4% was obtained for identification of the seven classes of tea diseases and pests. The developed detector could assist tea farmers in identifying the causes of lesions in real time.     

Predicting light leaf spot (Pyrenopeziza brassicae) risk on winter oilseed rape (Brassica napus) in England and Wales, using survey, weather and crop information

Data from surveys of winter oilseed rape crops in England and Wales in growing seasons with harvests in 1987–99 were used to construct statistical models to predict, in autumn (October), the incidence of light leaf spot caused by Pyrenopeziza brassicae on winter oilseed rape crops the following spring (March/April), at both regional and individual crop scales. Regions (groups of counties) with similar seasonal patterns of incidence (percentage of plants affected) of light leaf spot were defined by using principal coordinates analysis on the survey data. At the regional scale, explanatory variables for the statistical models were regional weather (mean summer temperature and mean monthly winter rainfall) and survey data for regional light leaf spot incidence (percentage of plants with affected pods) in July of the previous season. At the crop scale, further explanatory variables were crop cultivar (light leaf spot resistance rating), sowing date (number of weeks before/after 1 September), autumn fungicide use and light leaf spot incidence in autumn. Risk of severe light leaf spot (> 25% plants affected) in a crop in spring was also predicted, and uncertainty in predictions was assessed. The models were validated using data from spring surveys of winter oilseed rape crops in England and Wales from 2000 to 2003, and reasons for uncertainty in predictions for individual crops are discussed.     

Automated identification of sugar beet diseases using smartphones

Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.     

基于遥感数据的新疆开-孔河流域农业区种植结构提取

农作物种植结构是农作物空间格局的重要组成部分,是区域土地资源和水资源优化配置的基础。以新疆开―孔河流域农业区为研究区域,综合利用作物物候信息和2016年的MODIS NDVI时序曲线,获得不同作物生长差异明显的关键期,选择关键期的Landsat 8 OLI影像,构建主要作物提取知识规则,基于决策树方法开展农作物的分类识别。开―孔河农业区2016年主要作物种植面积为5.07×10^5hm^2,其中棉花种植面积最大,为1.97×10^5hm^2,玉米、小麦次之。博斯腾湖和开都河农业区以辣椒、玉米和小麦为主要作物,种植结构比较零散;孔雀河农业区种植结构比较单一,以棉花和香梨为主要作物。与仅利用时间序列的MODIS数据进行作物分类识别的结果对比表明,综合利用MODIS和Landsat数据的作物识别精度有显著提高,总体分类精度从62.58%提高到88.37%,kappa系数从0.53提高到0.86。该方法综合利用了MODIS数据的时序特征和Landsat数据较高的空间分辨率特征,有效地避免了MODIS数据空间分辨率不足而导致分类精度较差的情况,又避免了Landsat数据时间分辨率不足所引起的时相选择盲目性或数据冗余,在干旱区农业种植结构的提取领域具有一定的应用价值。     

1991—2010 年中国主要粮食作物生物灾害发生特征分析

生物灾害发生种类繁多和暴发频繁,是威胁我国粮食安全生产的重要因素。利用中国植物保护统计数据、粮食作物产量数据和农田土地覆盖类型分布遥感数据,重点分析中国1991年到2010年20年期间四类重要粮食作物病虫害发生面积和发生强度的变化趋势,以及其空间分布范围。结果表明：从1991年到2010年水稻、玉米和大豆病虫害发生面积均显著增加;水稻、小麦、玉米和大豆病虫害发生强度均显著增加。随着粮食作物病虫害发生面积增大,其发生强度均呈增加趋势。病害与虫害相比,无论是发生面积,还是发生强度,粮食作物的虫害均高于病害。粮食作物病虫害主要分布在我国中东部的粮食主产区,西部地区主要粮食作物病虫害发生相对较轻。为了有效地防止或减少病虫害等生物灾害对粮食生产安全的影响,目前应当加强农田景观变化和气候变化等对粮食农作物病虫害的影响诊断、发生与灾变的风险评估和监测预警,改善农田生态环境,开展区域性农田生态系统病虫害整合治理研究,并建立相应的对策。     

植保无人飞机低空低容量喷雾技术应用与展望

近十年来,我国植保无人飞机迅猛发展,应用的农作物范围越来越广,不仅在水稻、小麦、玉米等主要粮食作物得到了应用,在橡胶、槟榔等高大植株的病虫害防治中更有其独特优势,已经初步形成了我国植保无人飞机低空低容量喷雾的喷头配置、配套药剂、飞防助剂、作业参数等技术体系,对于重要农作物病虫害如稻纵卷叶螟、水稻纹枯病、小麦蚜虫、玉米黏虫等防治效果均在80%以上,在各地病虫害防控中发挥了重要作用。但是,植保无人飞机喷雾作业过程中,还存在炸机或失控、雾滴飘移药害、药液分层结块、防治效果不稳定等问题。通过汇总分析植保无人飞机在重要病虫草害防治工作的成功经验和安全事故,本文提出植保无人飞机低容量喷雾技术将会得到更广泛的应用,植保无人飞机专用药剂和配套助剂、变量施药、多传感器数据融合、多机协同、精准施药、施药标准和规范等都将得到长足的发展,为现代农业和智慧农业发展提供技术支持。     

Early development of light leaf spot (Pyrenopeziza brassicae) on winter oilseed rape (Brassica napus) in relation to temperature and leaf wetness.

In controlled environment experiments to study early development of light leaf spot, lesions developed with leaf wetness durations of 16 to 48 h after inoculation of oilseed rape with conidial suspensions of Pyrenopeziza brassicae at 12 or 18°C, but not with leaf wetness durations of 0 to 13h. The incubation period was 21 to 22 days at 12°C and 14 to 18 days at 18°C for leaf wetness durations of 16 to 48 h. The latent period was 21 to 23 days at 12°C and 18 to 19 days at 18°C, and the total number of lesions increased with increasing leaf wetness duration at both temperatures. In field experiments, light leaf spot always developed on oilseed rape with a leaf wetness duration of 48 h after inoculation in both 1990/1991 and 1991/1992, but the percentage leaf area affected was less on plants placed in an oilseed rape crop than on those placed in a glasshouse. Plants moved to an oilseed rape crop immediately after inoculation nearly always developed light leaf spot symptoms when they were inoculated between 19 October 1990 and 1 March 1991 or between 27 September 1991 and 14 February 1992, but plants inoculated between 31 August and 16 October 1990 or on 20 September 1991, when estimated leaf wetness duration was less than 16 h for several days after they were placed in crops, did not develop symptoms. The latent period of light leaf spot on plants transferred to the oilseed rape crop was 15 to 40 days, and there was an approximately linear relationship between 1 (latent period) and mean temperature during this period. The accumulated temperature during the latent period ranged from c. 150 to 250 day-degrees. The severity of lesions on these plants increased with increasing temperature from 5 to 15°C.     

Leaf scald (Xanthomonas albilineans) incidence and its effect on yield in seven sugarcane cultivars in Guadeloupe

Seven 5—month-old sugarcane cultivars difTering in resistance to leaf scald disease were inoculated by the decapitation technique with Xanthomonas albilineans. The effects of disease progress and incidence on yields were studied for the plant (first harvest) and two ratoon crops (second and third harvest). The percentage of diseased stalks and disease severity at first harvest 5 months after inoculation were 0·7 and 0·4, respectively, for the most resistant cultivar and 71·0% and 63·3, respectively, for the most susceptible cultivar. They decreased in all cultivars in both ratoon crops, but were still important in one cultivar (B69379). Significant ( P = 0·05) yield reductions of 12% and 21% occurred in two of the seven cultivars (B69566 and B69379, respectively). The number of symptomatic sugarcane stalks in the first ratoon crop (second harvest) was lower than the number of stalks colonized by the pathogen. Symptoms and yield losses of cultivars R570 and B69566 varied with the crop. Yield losses occurred in cultivar R570 only in the plant crop when this cultivar displayed numerous symptoms. Cultivar B69566 appeared to recover from the disease to a certain extent from the plant to the second ratoon crop (third harvest), as did the resistant cultivars in the first ratoon crop. In contrast, severe leaf scald symptoms were observed in the case of cultivar B69379 regardless of the crop, and significant yield losses occurred in the two ratoon crops. These results support the recommendation that cultivar B69379 should not be replanted in Guadeloupe.     

The production and release of ascospores of Pyrenopeziza brassicae on oilseed rape

The results of observation for three seasons in oilseed rape crops of the production and release of ascopores of Pyrenopeziza brassicae , the cause of light leaf spot disease of brassicas, are presented. Large apothecia (1–2 mm in diameter) take at least 3 weeks to develop on leaf petioles after infected leaves die, but small apothecia (50–200 um) may form on leaf lamellae after about 15 days. Apothecia were found on all 12 oilseed rape varieties examined. Spore trapping experiments in infected crops show that ascospore release is associated with rain, but most ascospores are released after rainfall when the crop debris bearing apothecia are wet. Ascospores can be released for up to 5 days after rain. Laboratory measurements show that apothecia can continue to release ascospores for up to 3 weeks even when they are subjected to wet and dry cycles. The consequences of the widespread occurrence of the sexual stage of P. brassicae are discussed and possible cycles of disease and spore production in oilseed crops are suggested.