Patchy weed distribution and site-specific weed control in winter cereals

Site-specific weed control in winter cereals was performed on the same fields every year over a 5-year period (1999–2003). The most common weeds (Apera spica-venti, Galium aparine, Veronica hederifolia, Viola arvensis) were counted by species, at grid points which were georeferenced and the data were analysed spatially. For weed control, weeds were grouped into three classes: grass, broad-leaved weeds (without Galium aparine), and Galium aparine. Based on weed distribution maps generated by the spatial analyses, herbicide application maps were created and site-specific herbicide application was carried out for grouped and or single weed species. This resulted in a significant reduction in herbicide use. Averaging the results for all fields and years, the total field area treated with herbicides was 39% for grass weeds, 44% for broad-leaved weeds (without Galium aparine) and 49% for Galium aparine. Therefore, site-specific weed control has the potential to reduce herbicide use compared to broadcast application, thus giving environmental and economic benefits.     

不同覆盖方式对陕西关中地区冬小麦田杂草群落的影响

为明确不同覆盖方式对陕西关中地区冬小麦田杂草群落的影响,通过连续3a定点覆盖试验,研究麦田杂草的种类、密度、重要值及生物多样性。PermANOVA结果表明,拔节期与成熟期、透明膜与黑膜及9种覆盖处理间的杂草群落均存在显著差异。群落调查结果表明,泽漆是整个生育期内所有处理的优势种,猪殃殃和荠菜主要出现在拔节期,灰绿藜和香附子主要出现在拔节期后。黑色全膜平作处理在整个生育期内杂草总密度最低,杂草种类较少,拔节期除草后的物种丰富度、香农指数和Pielou均匀度指数显著低于对照。因此,黑色全膜平作处理可有效控制麦田杂草种类,降低杂草的密度与生物多样性,可为陕西关中地区冬小麦田合理的耕作与覆盖提供参考。     

Camera steered mechanical weed control in sugar beet,maize and soybean

In sugar beet, maize and soybean, weeds are usually controlled by herbicides uniformly applied across the whole field. Due to restrictions in herbicide use and negative side effects, mechanical weeding plays a major role in integrated weed management (IWM). In 2015 and 2016, eight field experiments were conducted to test the efficacy of an OEM Claas 3-D stereo camera^® in combination with an Einböck Row-Guard^® hoe for controlling weeds. Ducks-foot blades in the inter-row were combined with four different mechanical intra-row weeding elements in sugar beet, maize and soybean and a band sprayer in sugar beet. Average weed densities in the untreated control plots were from 12 to 153 plants m^?2 with Chenopodium album, Polygonum convolvulus, Thlapsi arvense being the most abundant weed species. Camera steered hoeing resulted in 78% weed control efficacy compared to 65% using machine hoeing with manual guidance. Mechanical intra-row elements controlled up to 79% of the weeds in the crop rows. Those elements did not cause significant crop damage except for the treatment with a rotary harrow in maize in 2016. Weed control efficacy was highest in the herbicide treatments with almost 100% followed by herbicide band-applications combined with inter-row hoeing. Mechanical weed control treatments increased white sugar yield by 39%, maize biomass yield by 43% and soybean grain yield by 58% compared to the untreated control in both years. However, yield increase was again higher with chemical weed control. In conclusion, camera guided weed hoeing has improved efficacy and selectivity of mechanical weed control in sugar beet, maize and soybean.     

基于无人机低空遥感的水稻田间杂草分布图研究

目的 获取水稻田的低空遥感图像并分析得到杂草分布图，为田间杂草精准施药提供参考。方法 使用支持向量机(SVM)、K最近邻算法(KNN)和AdaBoost 3种机器学习算法，对经过颜色特征提取和主成分分析(PCA)降维后的无人机拍摄的水稻田杂草可见光图像进行分类比较；引入一种无需提取特征和降维、可自动获取图像特征的卷积神经网络(CNN)，对水稻田杂草图像进行分类以提升分类精度。结果 SVM、KNN和AdaBoost对测试集的测试运行时间分别为0.500 4、2.209 2和0.411 1 s，分类精度分别达到89.75%、85.58%和90.25%，CNN对图像的分类精度达到92.41%，高于上述3种机器学习算法的分类精度。机器学习算法及CNN均能有效识别水稻和杂草，获取杂草的分布信息，生成水稻田间的杂草分布图。结论 CNN对水稻田杂草的分类精度最高，生成的水稻田杂草分布图效果最好。     

Identification of broad-leaved dock (Rumex obtusifolius L.) on grassland by means of digital image processing

Digital image processing has the potential to support the identification of plant species required for site-specific weed control in grassland swards. The present study focuses on the identification of one of the most invasive and persistent weed species on European grassland, the broad-leaved dock (Rumex obtusifolius L., R.o.), in complex mixtures of perennial ryegrass with R.o. and other herbs.A total of 108 digital photographs were obtained from a field experiment under constant recording geometry and illumination conditions. An object-oriented image classification was performed. Image segmentation was done by transforming the red, green, blue (RGB) colour images to greyscale intensity images. Based on that, local homogeneity images were calculated and a homogeneity threshold (0.97) was applied to derive binary images. Finally, morphological opening was performed. The remaining contiguous regions were considered to be objects. Features describing shape, colour and texture were calculated for each of these objects. A Maximum-likelihood classification was done to discriminate between the weed species. In addition, rank analysis was used to test how combinations of features influenced the classification result.The detection rate of R.o. varied with the training dataset used for classification. Average R.o. detection rates ranged from 71 to 95% for the 108 images, which included more than 3,600 objects. Misclassifications of R.o. occurred mainly with Plantago major (P.m.). Between 9 and 16% R.o. objects were classified incorrectly as P.m. and 17–24% P.m. objects were misclassified as R.o. The classification result was influenced by the defined object classes (R.o., P.m., T.o., soil, residue vs. R.o., residue). For instance, classification rates were 86–91% and 65–82% for R.o. exclusively and R.o. against the remaining herb species, respectively.     

Plant species identification using Elliptic Fourier leaf shape analysis

Elliptic Fourier (EF) and discriminant analyses were used to identify young soybean (Glycine max (L.) merrill), sunflower (Helianthus pumilus), redroot pigweed (Amaranthus retroflexus) and velvetleaf (Abutilon theophrasti Medicus) plants, based on leaf shape. Chain encoded, Elliptic Fourier harmonic functions were generated based on leaf boundary. A complexity index of the leaf shape was computed using the variation between consecutive EF functions. Principle component analysis was used to select the Fourier coefficients with the best discriminatory power. Canonical discriminant analysis was used to develop species identification models based on leaf shapes extracted from plant color images during the second and third weeks after germination. The classification results showed that plant species during the third week were successfully identified with an average of correct classification rate of 89.4%. The discriminant model correctly classified on average: 77.9% of redroot pigweed, 93.8% of sunflower, 89.4% of velvetleaf and 96.5% of soybean. Using all of the leaves extracted from the second and the third weeks, the overall classification accuracy was 89.2%. The discriminant model correctly classified 76.4% of redroot pigweed, 93.6% of sunflower, 81.6% of velvetleaf, 91.5% of soybean leaf extracted from trifoliolate and 90.9% of soybean unifoliolate leaves. The Elliptic Fourier shape feature analysis could be an important and accurate tool for weed species identification and mapping.     

基于改进模糊C均值聚类算法的草坪杂草识别

【目的】为了实现草坪杂草管理的精准化施药，针对自然环境中杂草与草坪颜色相近导致杂草难以分割的问题，提出一种改进模糊C均值(Fuzzy C-means, FCM)聚类的分割算法。【方法】利用超绿算子提取感兴趣区域，融合HSV空间的多通道信息进行图像预处理，扩大杂草与草坪的特征差异。使用区域面积约束滤波范围，去除预处理图像中的草坪背景噪声，降低中值滤波造成的目标区域灰度级损失。提出一种各向灰度分布差异(Difference of gray distribution, DGD)检测算子，在聚类过程中引入像素周围不同方向的灰度分布差异特征实现草坪杂草分割。【结果】与传统FCM、FCM-S2、FCMNLS以及RSFCM算法相比，本文算法对大多数噪声区域抑制效果较好，可以实现较为理想的杂草分割效果。本文算法能有效分割草坪杂草，平均分割准确率达到91.45%，比FCM、FCM-S2、FCMNLS和RSFCM算法分别提高16.35%、4.12%、6.80%和8.06%。【结论】本文算法可有效地分割自然环境中的草坪杂草，为草坪杂草精准化施药提供了条件，具有实际应用价值。     

Responses of the antioxidant system to fluroxypyr in foxtail millet (Setaria italica L.) at the seedling stage

Foxtail millet(Setaria italica L.) is an important food and fodder crop in semi-arid areas. However, there are few herbicides suitable for use on weed control in field-grown foxtail millet during the post-emergence herbicides stage. The present study was conducted using four concentrations(0.5, 1, 2, and 4 L ai ha–1) of foliar-applied fluroxypyr, and the effect of fluroxypyr on selected metabolic and stress-related parameters in foxtail millet were assessed after 15 days. In this study, increasing concentrations decreased plant height and accumulation of chlorophylls. Our results also showed that malondialdehyde(MDA) accumulated in response to fluroxypyr application, demonstrating increased lipid peroxidation due to excessive reactive oxygen species production. In response to this oxidative stress, the activities of antioxidant enzymes were generally enhanced. Non-enzymatic antioxidant defense systems, which function in concert with antioxidant enzymes, can also protect plant cells from oxidative damage by scavenging reactive oxygen species(ROS). In conclusion, the hybrid variety(Zhangzagu) exhibited a greater tolerance to fluroxypyr than did the conventional variety Jingu 21, which might be associated with the antioxidant mechanisms of Zhangzagu hybrid millet.     

Hyperspectral species mapping for automatic weed control in tomato under thermal environmental stress

This work studied the impacts of variations in environmental temperature on hyperspectral imaging features in the visible and near infrared regions for robust species identification for weed mapping in tomato production. Six major Californian processing tomato cultivars, black nightshade (Solanum nigrum L.) and redroot pigweed (Amaranthus retroflexus L.) were grown under a variety of diurnal temperature ranges simulating conditions common in the Californian springtime planting period and one additional treatment simulating greenhouse growing conditions. The principal change in canopy reflectance with varying temperature occurred in the 480-670 and 720-810 nm regions. The overall classification rate ranged from 62.5% to 91.6% when classifiers trained under single temperatures were applied to plants grown at different temperatures. Eliminating the 480-670 nm region from the classifier’s feature set mitigated the temperature effect by stabilizing the total crop vs. weed classification rate at 86.4% over the temperature ranges. A site-specific recalibration method was also successful in alleviating the bias created by calibrating the models on the extreme temperatures and increased the classification accuracy to 90.3%. A global calibration method, incorporating all four temperature conditions in the classifier feature space, provided the best average total classification accuracy of 92.2% out of the methods studied, and was fairly robust to the varying diurnal temperature conditions.     

Weed Detection Using Canopy Reflection

For site-specific application of herbicides, automatic detection and evaluation of weeds is desirable. Since reflectance of crop, weeds and soil differs in the visual and near infrared wavelengths, there is potential for using reflection measurements at different wavelengths to distinguish between them. Reflectance spectra of crop and weed canopies were used to evaluate the possibilities of weed detection with reflection measurements in laboratory circumstances. Sugarbeet and maize and 7 weed species were included in the measurements. Classification into crop and weeds was possible in laboratory tests, using a limited number of wavelength band ratios. Crop and weed spectra could be separated with more than 97% correct classification. Field measurements of crop and weed reflection were conducted for testing spectral weed detection. Canopy reflection was measured with a line spectrograph in the wavelength range from 480 to 820 nm (visual to near infrared) with ambient light. The discriminant model uses a limited number of narrow wavelength bands. Over 90% of crop and weed spectra can be identified correctly, when the discriminant model is specific to the prevailing light conditions.     

A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution

In Gebhardt et al. (2006) an object-oriented image classification algorithm was introduced for detecting Rumex obtusifolius (RUMOB) and other weeds in mixed grassland swards, based on shape, colour and texture features. This paper describes a new algorithm that improves classification accuracy. The leaves of the typical grassland weeds (RUMOB, Taraxacum officinale, Plantago major) and other homogeneous regions were segmented automatically in digital colour images using local homogeneity and morphological operations. Additional texture and colour features were identified that contribute to the differentiation between grassland weeds using a stepwise discriminant analysis. Maximum-likelihood classification was performed on the variables retained after discriminant analysis. Classification accuracy was improved by up to 83% and Rumex detection rates of 93% were achieved. The effect of image resolution on classification results was investigated. The eight million pixel images were upscaled in six stages to create images with decreasing pixel resolution. Rumex detection rates of over 90% were obtained at almost all resolutions, and there was only moderate misclassification of other objects to RUMOB. Image processing time ranged from 45 s for the full resolution images to 2.5 s for the lowest resolution ones.     

光和不同打破种子休眠方法对紫茎泽兰种子萌发及幼苗状态的影响

紫茎泽兰是著名的外来入侵植物,作为入侵的第一步,发芽及其幼苗生长应该与其强入侵能力有关.基于此,通过不同光照强度处理和不同打破休眠方法的双因素实验,旨在探讨紫茎泽兰种子是否具有需光萌发特性以及低温、水杨酸、聚乙二醇,硝酸钾等常规打破休眠方法和光照如何共同影响其萌发、幼苗生长等问题.结果表明:在全光照条件下,不同处理的紫茎泽兰种子的萌发率均大于63％,铝箔纸覆盖的遮光条件(0.23％光照)萌发率均大于60％,而在完全黑暗条件下,其萌发率较低(均小于30％),这表明紫茎泽兰种子具有需光萌发的特性.有别于以往对其它植物种子的报道,低温处理、水杨酸处理、聚乙二醇处理和硝酸钾处理不能代替光照打破种子休眠,显示紫茎泽兰种子可能处于一种强迫休眠状态(种子静态).全光照与水杨酸处理、PEG处理对幼苗生长具有交互影响:黑暗下水杨酸处理浓度与幼苗生物量成正相关(P＜0.05),但全光照和加铝箔下不相关(P＞0.05);全光照下PEG处理浓度与根长显著正相关(P＜0.05),而加铝箔和黑暗下不相关(P＞0.05).紫茎泽兰种子需光萌发特征及其幼苗生长特点是人为破坏表土壤、深层土壤种子库地表化导致快速入侵的基础.结果也为通过引入适宜树种造林来控制光照因子对紫茎泽兰进行生态控制提供了理论依据.     

化学除草剂在林业上的应用

使用果尔，禾纳斯，丁草胺和克芜踪等10余种除草剂，对浙江省的圃地，草坪，地被及林地等的化学除草进行了应用研究。研究结果表明：化学除草剂种类多，效用不同，应依不同目的有选择地合理运用，几种除草剂合用效果更好，如把果尔，禾纳斯和丁草胺分别加入克芜踪，除草效果均优于其单一使用，除草率都在91％以上，化学作草明显省工省成本，只是不同植物类型的区域，节省的成本多少不同，在实行化学除草时，各种植物对药物的敏感性不一样，绝大部分除草剂都能保护主体植物而杀灭杂草，但如果施用量不当，会产生药害，表6参8     

Impact of Tillage and Herbicides on Weed Density,Yield and Quality of Cotton in Wheat Based Cropping System

Conservation tillage may improve yield of cotton in addition to improvement in soil quality if practiced for longer period. However, the practice may not be productive in short-term particularly when severe weeds are infesting the crops such as Cynodon dactylon, Conyza canadensis, Tribulus terrestris, and Cyperus rotundus, etc. Recent studies indicate that conventional tillage (CT) is more productive than zero tillage (ZT)/reduced tillage (RT). Performance of cotton under three tillage systems, viz., ZT, RT and CT; and five herbicides, i.e., haloxyfop-R-methyl 10.8 EC (108 g a.i. ha^?1), lactofen 24 EC (168 g a.i. ha^?1), haloxyfop 10.8 EC + lactofen 24 EC, hand weeding, and weedy check were evaluated during 2010-2011 at Gomal University, D.I. Khan, Pakistan, to explore the best management option for effective weed control, enhanced yield and quality of cotton grown after wheat. The results revealed that hand weeding and Haloxyfop as post emergence alone or in combination with Lactofen reduced weed density to the minimum irrespective of the tillage systems. Excessive rainfall and cooler temperature limited cotton growth and yield in 2010. The adverse weather conditions had more adverse effect on boll weight under ZT and RT than CT. Haloxyfop + lactofen produced higher seed cotton yield in RT than ZT, however, it could not exceed CT. Broad-spectrum herbicides × CT produced the highest number of bolls/plant, boll weight and seed cotton yield. Fiber quality and net returns were also the highest in broad-spectrum herbicides × CT. In conclusion, broad-spectrum herbicides under CT were more productive in wheat based cropping system on silty clay soil of D.I. Khan.     

黄河三角洲地区除草剂对紫花苜蓿产量及杂草群落影响的初探

针对紫花苜蓿田杂草,选用咪唑乙烟酸、精喹禾灵、高效氟吡甲禾灵和乙氧氟草醚4种除草剂,研究了不同除草剂和浓度对苜蓿产量和杂草群落特征的影响。结果表明,喷施咪唑乙烟酸、高效氟吡甲禾灵均能提高苜蓿产量,咪唑乙烟酸2000mL·hm^-2、高效氟吡甲禾灵700mL·hm^-2增产效果最好,喷施乙氧氟草醚明显抑制苜蓿生长;咪唑乙烟酸、精喹禾灵、乙氧氟草醚能明显降低杂草种类。从物种重要值来看,马唐、马齿苋、稗草等属于较难防除杂草;喷施除草剂对杂草群落产生了影响,物种多样性指数随药剂浓度的增大均呈下降趋势。从试验结果综合判断,除草剂最佳选择为咪唑乙烟酸2000mL·hm^-2,其次为高效氟吡甲禾灵700mL·hm^-2。     

吉林省玉米田杂草发生与危害现状的研究

掌握不同生态条件下的玉米田杂草发生种类及优势种群情况,对于制定杂草综合治理方案,科学选用化学除草剂及其配套技术具有十分重要的指导意义。本文从生态学角度对吉林省玉米田杂草种群数量和群落组成进行了系统研究。结果表明,吉林省玉米田常见杂草有39种,分属16科。并根据不同生态区杂草的危害程度和优势度,确定了不同生态区主要杂草群落组成。     

Potential of a terrestrial LiDAR-based system to characterise weed vegetation in maize crops

LiDAR (Light Detection And Ranging) is a remote-sensing technique for the measurement of the distance between the sensor and a target. A LiDAR-based detection procedure was tested for characterisation of the weed vegetation present in the inter-row area of a maize field. This procedure was based on the hypothesis that weed species with different heights can be precisely detected and discriminated using non-contact ranging sensors such as LiDAR. The sensor was placed in the front of an all-terrain vehicle, scanning downwards in a vertical plane, perpendicular to the ground, in order to detect the profile of the vegetation (crop and weeds) above the ground. Measurements were taken on a maize field on 3 m wide (0.45 m²) plots at the time of post-emergence herbicide treatments. Four replications were assessed for each of the four major weed species: Sorghum halepense, Cyperus rotundus, Datura ferox and Xanthium strumarium. The sensor readings were correlated with actual, manually determined, height values (r² = 0.88). With canonical discriminant analysis the high capabilities of the system to discriminate tall weeds (S. halepense) from shorter ones were quantified. The classification table showed 77.7% of the original grouped cases (i.e., 4800 sampling units) correctly classified for S. halepense. These results indicate that LiDAR sensors are a promising tool for weed detection and discrimination, presenting significant advantages over other types of non-contact ranging sensors such as a higher sampling resolution and its ability to scan at high sampling rates.     

基于深度残差网络的茶园杂草分类及模型压缩方法研究

为提高茶园杂草分类深度模型的准确性,减少深度模型的冗余参数问题.以茶园常见的10类杂草图像为数据样本,分别基于深度学习的ResNet50、VGGNet和AlexNet网络结构构建杂草分类模型;在此基础上,进一步利用剪枝算法压缩深度模型ResNet50.通过实验对比3个模型测试集的平均准确率分别为0.86、0.72和0.63;此外,通过对比ResNet50的茶园杂草模型在训练集和测试集上压缩前后效果,显示结果基本一致.研究表明ResNet50在这3个模型中是最优分类模型,且压缩后的深度模型ResNet50提升了模型的性能.因此,该研究也为移动端设备的分类提供了理论基础.