共查询到20条相似文献，搜索用时 25 毫秒
1.
Ana Isabel de Castro Francisca López-Granados Montserrat Jurado-Expósito 《Precision Agriculture》2013,14(4):392-413
This article explores the potential use of multi-spectral high-spatial resolution QuickBird imagery to detect cruciferous weed patches in winter wheat fields. In the present study, research was conducted on six individual naturally infested fields (field-scale study: field area ranging between 3 and 52 ha) and on a QuickBird-segmented winter wheat image (broad-scale study: area covering approximately 263 winter wheat fields, approximately 2 656 ha) located in the province of Córdoba (southern Spain). To evaluate the feasibility of mapping cruciferous weed patches in both the field-scale and broad-scale studies, two supervised classification methods were used: the Maximum likelihood classifier (MLC) and vegetation indices. Then, the best classification methods were selected to develop in-season site-specific cruciferous weed patch treatment maps. The analysis showed that cruciferous weed patches were accurately discriminated in both field-scale and broad-scale scenarios. Thus, considering the broad-scale study, classification accuracies of 91.3 and 89.45 % were obtained using the MLC and blue/green (B/G) vegetation indices, respectively. The site-specific treatment maps obtained from the best classifiers indicated that there is a great potential for reducing herbicide use through in-season, cruciferous weed patch site-specific control on both a field-scale and broad-scale. For example, it can be determined that by applying site-specific treatment maps on a broad-scale, herbicide savings of 61.31 % for the no-treatment areas and 13.02 % for the low-dose herbicide areas were obtained. 相似文献
2.
Dustin Severtson Nik Callow Ken Flower Andreas Neuhaus Matt Olejnik Christian Nansen 《Precision Agriculture》2016,17(6):659-677
There is growing evidence that potassium deficiency in crop plants increases their susceptibility to herbivorous arthropods. The ability to remotely detect potassium deficiency in plants would be advantageous in targeting arthropod sampling and spatially optimizing potassium fertilizer to reduce yield loss due to the arthropod infestations. Four potassium fertilizer regimes were established in field plots of canola, with soil and plant nutrient concentrations tested on three occasions: 69 (seedling), 96 (stem elongation), and 113 (early flowering) days after sowing (DAS). On these dates, unmanned aerial vehicle (UAV) multi-spectral images of each plot were acquired at 15 and 120 m above ground achieving spatial (pixel) resolutions of 8.1 and 65 mm, respectively. At 69 and 96 DAS, field plants were transported to a laboratory with controlled lighting and imaged with a 240-band (390–890 nm) hyperspectral camera. At 113 DAS, all plots had become naturally infested with green peach aphids (Hemiptera: Aphididae), and intensive aphid counts were conducted. Potassium deficiency caused significant: (1) increase in concentrations of nitrogen in youngest mature leaves, (2) increase in green peach aphid density, (3) decrease in vegetation cover, (4) decrease in normalized difference vegetation indices (NDVI) and decrease in canola seed yield. UAV imagery with 65 mm spatial resolution showed higher classification accuracy (72–100 %) than airborne imagery with 8 mm resolution (69–94 %), and bench top hyperspectral imagery acquired from field plants in laboratory conditions (78–88 %). When non-leaf pixels were removed from the UAV data, classification accuracies increased for 8 mm and 65 mm resolution images acquired 96 and 113 DAS. The study supports findings that UAV-acquired imagery has potential to identify regions containing nutrient deficiency and likely increased arthropod performance. 相似文献
3.
应用多光谱数字图像识别苗期作物与杂草 总被引：2，自引：0，他引：2
通过对多光谱成像仪获得的数字图片,采用一定的目标分割与形态学处理,对豆苗和杂草进行识别判断.为解决识别速度与正确率的矛盾,以豆苗和杂草图像的识别为例,提出一种基于多光谱图像算法的杂草识别新方法.应用3CCD多光谱成像仪获取豆苗与杂草图像,以多光谱图像的近红外IR通道图像为基础,利用图像分割和形态学方法,将所有豆苗叶子影像提取出来.对于剩下的2种杂草(牛筋草,空心莲子草)图像,先利用图像分析工具统计出图像块的长度、宽度、面积等基本特征参数,并根据它们形状的不同,总结出两条简单的判别规则,进行进一步的识别.本试验对147个目标进行判断,其中误判14个,正确率为90.5%,表明该方法算法简单、计算量小、速度快,能够有效识别这2种杂草,为田间杂草的快速识别提供了一种新方法. 相似文献
4.
Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat 总被引：1，自引：0，他引：1
High spatial resolution images taken by unmanned aerial vehicles (UAVs) have been shown to have the potential for monitoring agronomic and environmental variables. However, it is necessary to capture a large number of overlapped images that must be mosaicked together to produce a single and accurate ortho-image (also called an ortho-mosaicked image) representing the entire area of work. Thus, ground control points (GCPs) must be acquired to ensure the accuracy of the mosaicking process. UAV ortho-mosaics are becoming an important tool for early site-specific weed management (ESSWM), as the discrimination of small plants (crop and weeds) at early growth stages is subject to serious limitations using other types of remote platforms with coarse spatial resolutions, such as satellite or conventional aerial platforms. Small changes in flight altitude are crucial for low-altitude image acquisition because these variations can cause important differences in the spatial resolution of the ortho-images. Furthermore, a decrease of flying altitude reduces the area covered by each single overlapped image, which implies an increase of both the sequence of images and the complexity of the image mosaicking procedure to obtain an ortho-image covering the whole study area. This study was carried out in two wheat fields naturally infested by broad-leaved and grass weeds at a very early phenological stage. The geometric accuracy differences and crop line alignment among ortho-mosaics created from UAV image series were investigated while taking into account three different flight altitudes (30, 60 and 100 m) and a number of GCPs (from 11 to 45). The results did not show relevant differences in geo-referencing accuracy on the interval of altitudes studied. Similarly, the increase of the number of GCPs did not imply a relevant increase of geo-referencing accuracy. Therefore, the most important parameter to consider when choosing the flying altitude is the ortho-image spatial resolution required rather than the geo-referencing accuracy. Regarding the crop mis-alignment, the results showed that the overall errors were less than twice the spatial resolution, which did not break the crop line continuity at the studied spatial resolutions (pixels from 7.4 to 24.7 mm for 30, 60 and 100 m flying altitudes respectively) on the studied crop (early wheat). The results lead to the conclusion that a UAV flying at a range of 30 to 100 m altitude and using a moderate number of GCPs is able to generate ultra-high spatial resolution ortho-imagesortho-images with the geo-referencing accuracy required to map small weeds in wheat at a very early phenological stage. This is an ambitious agronomic objective that is being studied in a wide research program whose global aim is to create broad-leaved and grass weed maps in wheat crops for an effective ESSWM. 相似文献
5.
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. 相似文献
6.
An on-farm approach to quantify yield variation and to derive decision rules for site-specific weed management 总被引：2，自引：1，他引：1
C. Ritter D. Dicke M. Weis H. Oebel H. P. Piepho A. Büchse R. Gerhards 《Precision Agriculture》2008,9(3):133-146
Grain yield often varies within agricultural fields as a result of the variation in soil characteristics, competition from
weeds, management practices and their causal interactions. To implement appropriate management decisions, yield variability
needs to be explained and quantified. A new experimental design was established and tested in a field experiment to detect
yield variation in relation to the variation in soil quality, the heterogeneity of weed distribution and weed control within
a field. Weed seedling distribution and density, apparent soil electrical conductivity (ECa) and grain yield were recorded and mapped in a 3.5 ha winter wheat field during 2005 and 2006. A linear mixed model with
an anisotropic spatial correlation structure was used to estimate the effect of soil characteristics, weed competition and
herbicide treatment on crop yield. The results showed that all properties had a strong effect on grain yield. By adding herbicide
costs and current grain price into the model, thresholds of weed density were derived for site-specific weed control. This
experimental approach enables the variation of yield within agricultural fields to be explained, and an understanding of the
effects on yield of the factors that affect it and their causal interactions to be gained. The approach can be applied to
improve decision algorithms for the patch spraying of weeds. 相似文献
7.
Henning Nordmeyer 《Precision Agriculture》2006,7(3):219-231
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. 相似文献
8.
L. J. Wiles 《Precision Agriculture》2009,10(3):277-290
Site-specific weed management can include both limiting herbicide application to areas of the field where weed pressure is
above the economic threshold (patch spraying) and varying the choice of herbicide for most cost-effective weed control of
local populations. The benefits of patch spraying with several, postemergence herbicides in irrigated corn were evaluated
in simulation studies using weed counts from 16 fields. Patch spraying with one, two or the number of herbicides that maximized
net return for a field was simulated. With patch spraying of one herbicide, the average area of a field left untreated is
34.5%. Net return increases by $3.09 ha−1 compared to a uniform application without decreasing crop yield. Additional herbicides increase the average benefits with
just 4% more of the field treated. With two herbicides, the increase in net return is almost tripled and herbicide use is
reduced nearly 10-fold compared to patch spraying with one herbicide, and weed control is better than the uniform application
in 10 fields. Using more than two herbicides for patch spraying further reduces weed escapes, but herbicide use is greater
than a uniform application in 10 fields. Growers might be more willing to adopt patch spraying if more than one herbicide
is used in a field. 相似文献
9.
Grapevine leafroll disease (GLD) is a virus disease that quickly propagates through vineyards under appropriate weather conditions and can reduce grape production worldwide by nearly 60 %. Therefore, the accurate diagnosis and reliable evaluation of GLD distribution, particularly at the early stage of GLD infection, is important to prevent the spread of this disease. This study applied the ant colony clustering algorithm (ACCA) to detect GLD spectral anomalies on 4 GLD-infected vineyards according to multi-spectral imagery for precision disease management. GLD was classified into three stages: GLD1, GLD2 and GLD3 according to its infection severity. An 11-index feature vector and its stacked image were generated to enhance the spectral differences and spectral discrimination between diseased and healthy grapevines. ACCA was then designed to solve the fuzziness of the multi-spectral image for GLD-infected grapevines and successfully identify GLD from healthy grapevines. Finally, a field survey with 49 samples and pixel purity index technology were applied to validate the effectiveness, efficiency and accuracy of ACCA. Field results indicated that an early stage of the GLD infection (GLD1) could be successfully discriminated from GLD2-, GLD3- and non-infected grapevines. The classification accuracies of non-, GLD1-, GLD2- and GLD3-infected grapevines were 94.4, 75, 84.6 and 83.3 %, respectively. Hence, the method based on an 11-index image and ACCA may significantly detect GLD at an early stage from healthy grapevines for precision disease management at the field level. 相似文献
10.
除草剂对燕麦田杂草的防效及其对燕麦产量的影响 总被引：5，自引：2，他引：3
通过田间试验研究了13种除草剂对燕麦田杂草控制及产量的影响.结果表明:10种苗期茎叶处理除草剂中,皮燕麦和裸燕麦田用药后30d,株防效和鲜重防效均以40%二甲.辛酰溴最高,分别为97.5%、99.0%和90.5%、98.0%;3种土壤处理除草剂以仲丁灵效果最佳;麦草畏对燕麦有轻微药害.除草剂对燕麦产量有显著影响,不同除草剂效应不同,皮燕麦、裸燕麦对除草剂的反应也不尽相同;裸燕麦在相同除草剂处理下产量增幅小于皮燕麦;除草剂的防效是影响燕麦产量的最主要因素,其次为小穗数、穗粒数、株高、穗长、千粒质量. 相似文献
11.
基于无人机多光谱遥感图像的玉米田间杂草识别 总被引：5，自引：0，他引：5
【目的】为了精确高效识别玉米田间杂草,减少除草剂施用,提高玉米种植管理精准性。【方法】通过六旋翼无人机搭载多光谱相机获取玉米田块多光谱图像。为分离图像中植被与非植被像元,计算了7种植被指数,采用最大类间方差法提取植被指数图像中非植被区域,制作掩膜文件并对多光谱图像掩膜。通过主成分分析对多光谱图像进行变换,保留信息量最多的前3个主成分波段。将试验区域分为训练区域和验证区域,在训练区域中分别选取了675处玉米和525处杂草样本对监督分类模型进行训练,在验证区域选取了240处玉米样本及160处杂草样本评价模型分类精度。将7种植被指数、3个主成分波段的24个纹理特征及经过滤波的10个反射率,共计41项特征作为样本特征参数。利用支持向量机-特征递归消除算法（support vector machines-feature recursive elimination,SVM-RFE）和Relief算法从41项特征中各筛选14项特征构成特征子集,采用支持向量机、K-最近邻、Cart决策树、随机森林和人工神经网络对特征子集进行监督分类。【结果】支持向量机与随机森林对全部特征及2个特征子集分类效果较好,支持向量机总体精度为89.13%—91.94%,Kappa>0.79,随机森林总体精度为89.27%—90.95%,Kappa>0.79。【结论】SVM-RFE算法对数据降维效果优于Relief算法,支持向量机（SVM）模型对区域冠层尺度下玉米与杂草的分类效果最好。 相似文献
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基于视觉注意模型的苗期油菜田间杂草检测 总被引：3，自引：0，他引：3
提出了基于视觉注意模型的苗期油菜/杂草图像检测方法。针对苗期油菜大田环境,获取油菜/杂草RGB原始图像。根据原始图像颜色分布特点改进Itti模型,生成系列特征显著图,结合区域生长算法分割出感兴趣区域。针对该区域提取形状和纹理特征参数作为支持向量机输入量,判别出所有油菜区域,最后融合原始图像和油菜区域获取最终株间杂草区域。结果表明:与局部迭代阈值法和最大类间方差法相比,本研究提出的图像分割方法更优,正确分割目标概率、错误分割目标概率及漏分割目标概率分别为92.46%、3.26%及7.54%;针对形状、纹理、综合特征及精选特征四类特征参数集,径向基-支持向量机的识别率分别为96.00%、94.29%、100.00%及96.00%。 相似文献
16.
In this paper, a new method to fuse low resolution multispectral and high resolution RGB images is introduced, in order to detect Gramineae weed in rice fields with plants at 50 days after emergence (DAE).The images are taken from a fixed-wing unmanned aerial vehicle (UAV) at 60 and 70 m altitude. The proposed method combines the texture information given by a high resolution red–green–blue (RGB) image and the reflectance information given by a low resolution multispectral (MS) image, to obtain a fused RGB-MS image with better weed discrimination features. After analyzing the normalized difference vegetation index (NDVI) and normalized green red difference index (NGRDI) for weed detection, it was found that NGRDI presents better features. The fusion method consists of decomposing the RGB image using the intensity, hue and saturation (IHS) transformation, then, a second order Haar wavelet transformation is applied to the intensity layer (I) and the NGRDI image. From this transformation, the low–low (LL) coefficients of the NGRDI image are replaced by the LL coefficients of the I layer. Finally, the fused image is obtained by transforming the new wavelet coefficients to RGB space. To test the method, a one hectare experimental plot with rice plants at 50 DAE with Gramineae weeds was selected. Additionally, to compare the performance of the method, two indices were used, specifically, the M/MGT index which is the percentage of detected weed area, and the MP index which indicates the precision of weed detection. These indices were evaluated in four validation zones using three Neural Networks (NN) detection systems based on three types of images; namely, RGB, RGB + NGRDI, and fused RGB-NGRDI. The best weed detection performance was obtained by the NN with the fused image, with M/MGT index between 80 and 108% and MP between 70 and 85%. 相似文献
17.
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。 相似文献
18.
Development of a target oriented weed control system 总被引：1，自引：0，他引：1
19.
Henrik Skov Midtiby Solvejg K. Mathiassen 《Computers and Electronics in Agriculture》2011,77(1):35-40
An intelligent real-time microspraying weed control system was developed. The system distinguishes between weed and crop plants and a herbicide (glyphosate) is selectively applied to the detected weed plants. The vision system captures 40 RGB images per second, each covering 140 mm by 105 mm with an image resolution of 800 × 600 pixels. From the captured images the forward velocity is estimated and the spraycommands for the microsprayer are calculated. Crop and weed plants are identified in the image, and weed plants are sprayed. Performance of the microsprayer system was evaluated under laboratory conditions simulating field conditions. A combination of maize (Zea mays L.), oilseed rape (Brassica napus L.) and scentless mayweed (Matricaria inodora L.) plants, in growth stage BBCH10, was placed in pots, which were then treated by the microspray system. Maize simulated crop plants, while the other species simulated weeds. The experiment were conducted at a velocity of 0.5 m/s. Two weeks after spraying, the fraction of injured plants was determined visually. None of the crop plants were harmed while 94% of the oilseed rape and 37% of the scentless mayweed plants were significantly limited in their growth. Given the size and shape of the scentless mayweed plants and the microsprayer geometry it was calculated that the microsprayer could only hit 64% of the scentless mayweed plants. The system was able to effectively control weeds larger than 11 mm × 11 mm. 相似文献
20.
Development of an Image Processing System and a Fuzzy Algorithm for Site-Specific Herbicide Applications 总被引：1，自引：0，他引：1
Yang Chun-Chieh Prasher Shiv O. Landry Jacques-André Ramaswamy Hosahalli S. 《Precision Agriculture》2003,4(1):5-18
In precision farming, image analysis techniques can aid farmers in the site-specific application of herbicides, and thus lower the risk of soil and water pollution by reducing the amount of chemicals applied. Using weed maps built with image analysis techniques, farmers can learn about the weed distribution within the crop. In this study, a digital camera was used to take a series of grid-based images covering the soil between rows of corn in a field in southwestern Quebec in May of 1999. Weed coverage was determined from each image using a greenness method in which the red, green, and blue intensities of each pixel were compared. Weed coverage and weed patchiness were estimated based on the percent of greenness area in the images. This information was used to create a weed map. Using weed coverage and weed patchiness as inputs, a fuzzy logic model was developed for use in determining site-specific herbicide application rates. A herbicide application map was then created for further evaluation of herbicide application strategy. Simulations indicated that significant amounts of herbicide could be saved using this approach. 相似文献