Mapping Ridolfia segetum patches in sunflower crop using remote sensing

Ridolfia segetum is a frequent umbelliferous weed in sunflower crops in the Mediterranean basin. Field and remote sensing research was conducted in 2003 and 2004 over two naturally infested fields to determine the potential of multispectral imagery for discrimination and mapping of R. segetum patches in sunflower crops. The efficiency of the four wavebands blue (B), green (G), red (R) and near‐infrared (NIR), selected vegetation indices and the spectral angle mapper (SAM) classification method were studied using aerial photographs taken in the late vegetative (mid‐May), flowering (mid‐June) and senescence (mid‐July) crop growth stages. Discrimination efficiency of R. segetum patches in sunflower crops is consistently affected by their phenological stages, in this order: flowering > senescence > vegetative. In both fields, R. segetum patches were efficiently discriminated in mid‐June, corresponding to the flowering phase, by using the waveband G, the ratio R/B or SAM with overall accuracies ranging from 85% to 98%. The application of the median‐filtering algorithm to any of the classified images improved the accuracy. Our results suggest that mapping R. segetum weed patches in sunflower to implement site‐specific weed management techniques is feasible with aerial photography when images are taken from 8 to 10 weeks before harvesting.     

Competition between Ridolfia segetum and sunflower

Studies on competition between Ridolfia segetum Maris, and sunflower (Helianthemum annuus L.) were conducted at eight locations in southern Spain in 1990 and 1991. in order to define competition models and to estimate from these economic thresholds as affected by crop inputs and potential yields. Competition losses in sunflower crops ranged from 19% to 56% of weed–free yields. There were slightly better correlations between percentage sunflower reduction and weed density than with weed dry weight, (?0.66 and ?0.59, respectively). The weed competitive index, or sunflower crop dry weight reduction per unit dry weight of R. segetum, was 1.09. The percentage yield losses due to weed density (NPR_t) were fitted to multiple linear, quadratic, exponential and hyperbolic models. The hyperbolic equation, %RSY=100 (1+1/b*NPR_t)^?1, where b=0.14 and is the R. segetum competitive ability index, had the lowest error sum of squares (SSE), and gave the best biological explanation for the competition response. Early emergence (before mid–March) made weeds about 1.5 times more competitive than late emergence. The economic threshold to offset the cost of a shallow post–emergence tillage, assuming 70% control efficiency, ranged from about 2.5 plants m ^?2 for low–yielding crops(1200kgha^?1) to less than one plant m^?2 for higher–yielding crops (2800 kg ha^?1).     

Multispectral classification of grass weeds and wheat (Triticum durum) using linear and nonparametric functional discriminant analysis and neural networks

Field studies were conducted to determine the potential of multispectral classification of late‐season grass weeds in wheat. Several classification techniques have been used to discriminate differences in reflectance between wheat and Avena sterilis, Phalaris brachystachys, Lolium rigidum and Polypogon monspeliensis in the 400–900 nm spectrum, and to evaluate the accuracy of performance for a spectral signature classification into the plant species or group to which it belongs. Fisher’s linear discriminant analysis, nonparametric functional discriminant analysis and several neural networks have been applied, either with a preliminary principal component analysis (PCA) or not and in different scenarios. Fisher’s linear discriminant analysis, feedforward neural networks and one‐layer neural network, all showed classification percentages between 90% and 100% with PCA. Generally, a preliminary computation of the most relevant principal components considerably improves the correct classification percentage. These results are promising because A. sterilis and L. rigidum, two of the most problematic, clearly patchy and expensive‐to‐control weeds in wheat, could be successfully discriminated from wheat in the 400–900 nm range. Our results suggest that mapping grass weed patches in wheat could be feasible with analysis of real‐time and high‐resolution satellite imagery acquired in mid‐May under these conditions.     

Discrimination of Ambrosia artemisiifolia and Artemisia vulgaris by hyperspectral image analysis during the growing season

Ambrosia artemisiifolia (ragweed) is an invasive plant in Europe. An optical detection system for effective monitoring requires differences in the spectral reflectance properties compared with other plant species. A. artemisiifolia often occurs together with Artemisia vulgaris (mugwort). Both plant species are of the Asteraceae family and they are almost indistinguishable in appearance. With the help of hyperspectral image analysis, a method was developed to determine characteristic wavelengths for their classification. High‐resolution hyperspectral images (400–1000 nm) were generated indoors. The factors measured were two weed species, two tissue classes (leaf and stem) and three growth stages (rosette growth, inflorescence emergence and fruit development). Only the stems of A. artemisiifolia in the fruit development stage showed different reflectance behaviour compared with its leaves and with the stems and leaves of A. vulgaris. At wavelengths ranging from 550 to 650 nm, the reflectance increased, and then at wavelengths up to 680 nm, the reflectance decreased. The other tissue classes showed constantly decreasing spectral reflectance from 550 to 680 nm. In the two other early growth stages, the reflectance of all four tissue classes decreased similarly. Thus, using two wavelengths of 550 and 650 nm, classification between A. artemisiifolia and A. vulgaris at fruit development was achieved. The findings could be a first step to develop an optical outdoor detection system to identify hot spots of A. artemisiifolia.     

Early assessment of the yield loss in rice due to the brown planthopper using a hyperspectral remote sensing method

The brown planthopper (BPH) is an important pest in rice. Rice losses due to BPH's damage are often more than 10% of yield. Assessments on loss rates from BPH are now basically dependent on experiential indices. However, early assessments on rice yield losses using hyperspectral data are still rare. In this study, reflectance from rice canopy was measured in net cages after different densities of BPH release. Results showed that reflectance in the near-infrared region (750–1000 nm) from milk grain stage, and in 400–531 nm and 567–705 nm from mature grain stage was closely related to BPH density. These spectral indices: red-edge parameters (λ_r, Dλ_r, Sλ_r), ratio of the maximal reflectance in green (490–560 nm) to minimal reflectance (640–740 nm) in red (R_GREEN/R_RED), ratio of the near-infrared peak (R_NIR) to R_RED, normalized difference vegetation index between R_NIR and R_RED and soil-adjusted vegetation index (SAVI) from both milk and mature grain stages, indicated BPH densities well. As expected, rate of loss in rice grain was higher with increasing BPH density. SAVI, Dλ_r, Sλ_r and reflectance at 760 nm (R₇₆₀) from milk grain stage were significantly correlated with rates of loss in panicle, and 1000-grain weight and multiple-linear regression models for detecting loss rate were established. The hyperspectral reflectance from rice at milk grain stage can be used to assess rice yield losses due to BPH and improve management policies.     

Simulating the effects of weed spatial pattern and resolution of mapping and spraying on economics of site-specific management

The economic benefits of using site‐specific weed management (SSWM) are related to the proportion of the field that is weed‐infested, the number of weed patches and the spatial resolution of sampling and spraying technologies. In this paper we simulate different combinations of these factors using parameter values obtained for Avena sterilis ssp. ludoviciana growing in Spanish winter barley crops. The profitability of SSWM systems increased as the proportion of the field infested by this weed decreased and when patch distribution was more concentrated. Under most of the conditions tested, positive net returns for SSWM were obtained when the weed‐infested area was smaller than 30%. The highest net return occurred using a 12 m × 12 m mapping and spraying resolution. The critical parameter that determined the economic viability of patch mapping and spraying resolution was the technology costs. The site specific strategy was economically superior to the standard strategy (overall herbicide application) in most cases. However, the differential between the two strategies decreased when the number of patches and the resolution of mapping and spraying increased, such that the highest net returns were obtained with a single patch covering 14% of the field and using a 12‐m mapping and spraying resolution; whereas the worst net returns were obtained for all patch numbers when 64% of the field was infested and a 3‐m mapping and spraying resolution was used.     

Comparison of crop and weed height,for potential differentiation of weed patches at harvest

Weeds and weed control are major production costs in global agriculture, with increasing challenges associated with herbicide‐based management because of concerns with chemical residue and herbicide resistance. Non‐chemical weed management may address these challenges but requires the ability to differentiate weeds from crops. Harvest is an ideal opportunity for the differentiation of weeds that grow taller than the crop, however, the ability to differentiate late‐season weeds from the crop is unknown. Weed mapping enables farmers to locate weed patches, evaluate the success of previous weed management strategies, and assist with planning for future herbicide applications. The aim of this study was to determine whether weed patches could be differentiated from the crop plants, based on height differences. Field surveys were carried out before crop harvest in 2018 and 2019, where a total of 86 and 105 weedy patches were manually assessed respectively. The results of this study demonstrated that across the 191 assessed weedy patches, in 97% of patches with Avena fatua (wild oat) plants, 86% with Raphanus raphanistrum (wild radish) plants and 92% with Sonchus oleraceus L. (sow thistles) plants it was possible to distinguish the weeds taller than the 95% of the crop plants. Future work should be dedicated to the assessment of the ability of remote sensing methods such as Light Detection and Ranging to detect and map late‐season weed species based on the results from this study on crop and weed height differences.     

Fusion of sensor data for the detection and differentiation of plant diseases in cucumber

The development of plant diseases is associated with biophysical and biochemical changes in host plants. Various sensor methods have been used and assessed as alternative diagnostic tools under greenhouse conditions. Changes in photosynthetic activity, spectral reflectance and transpiration rate of diseased leaves, inoculated with Cucumber mosaic virus (CMV), Cucumber green mottle mosaic virus (CGMMV), and the powdery mildew fungus Sphaerotheca fuliginea were assessed by the use of non‐invasive sensors during disease development. Spatiotemporal changes in leaf temperature related to transpiration were visualized by digital infrared thermography. The maximum temperature difference within a leaf was an appropriate parameter to differentiate between healthy and diseased plants. The photosynthetic activity of healthy and diseased cucumber plants varied as measured by chlorophyll fluorescence and compared to the actual chlorophyll content. Hyperspectral imaging data were analysed using spectral vegetation indices. The results from this study confirm that each pathogen has a characteristic influence on the physiology and vitality of cucumber plants, which can be measured by a combination of non‐invasive sensors. Whereas thermography and chlorophyll fluorescence are unspecific indicators for plant diseases, hyperspectral imaging offers the potential for an identification of plant diseases. In a sensor data fusion approach, an early detection of each pathogen was possible by discriminant analysis. Although it still needs to be validated under real conditions, the combination of information from different sensors seems to be a promising tool.     

Field evaluation of allelopathic plant extracts alongside herbicides on weed management indices and weed–crop regression analysis in maize

Synthetic herbicides are posing problems owing to the development of weed resistance and emerging debate on their associated health hazards and ecological threats. Allelopathic manipulations are evolving as applicable substitutes for weed management in agroecosystems. In order to assess the efficacy of potential allelopathic water extracts from different plant species, field experiments were conducted during 2010 and 2011. Sorghum bicolor L., Helianthus annuus L., Brassica napus L., Oryza sativa L., Zea mays L. and Morus alba L. aqueous extracts in different combinations alone or along with a reduced dose of herbicide were evaluated for weed suppression in a maize crop. A weedy check and two herbicidal treatments, S‐metolachlor + atrazine (pre‐emergence) and atrazine alone (early postemergence), were included for comparison. Sorghum, brassica or sunflower tank‐mixed with 25% of the recommended dose of atrazine significantly suppressed the total weed density and dry biomass, along with a concomitant decrease in the crop resistance indices and treatment efficacy indices over the control. The weed density and dry biomass that were recorded at 60 days after sowing showed a strong negative correlation, while the leaf area index, crop growth rate, dry matter accumulation and net assimilation rate predicted a strong positive correlation, with the stover and grain yield of maize.     

Evaluating the accuracy of mapping weeds in fallow fields using airborne digital imaging: Panicumeffusum in oilseed rape stubble

The potential accuracy of using airborne multispectral imaging to map weed patches rapidly in a fallow field has been evaluated. An image of a field of oilseed rape ( Brassica napus L.) stubble interspersed with Panicum effusum R. Br. was acquired using a four-camera airborne digital imaging system; recording in the infrared, red, green and blue wavebands. The image was converted into georectified weed maps using supervised and unsupervised classification procedures. Comparison of the airborne-derived maps with an accurate weed map compiled from a detailed ground survey demonstrated that weed:non-weed classification and mapping accuracies of better than 87% are possible. The limitations of assessing the accuracy of classified imagery using ground-truth data of similar spatial resolution are discussed.     

利用光谱特征评价莲草直胸跳甲对喜旱莲子草的控制效果

为建立快速有效的喜旱莲子草生物防治效果评价技术体系,采用基于成像光谱遥感技术的多波段光谱辐射仪,研究了利用冠层光谱特征值评价天敌昆虫莲草直胸跳甲Agasicles hygrophila对喜旱莲子草Alternanthera philoxeroides控制效果的方法。结果表明,莲草直胸跳甲取食引起喜旱莲子草冠层光谱变化,在可见光区绿光波段（中心波长约560 nm）和近红外波段（中心波段810 nm）,喜旱莲子草冠层光谱反射率随莲草直胸跳甲密度的增加逐渐下降,而在红光波段（中心波长约660 nm）呈上升趋势。莲草直胸跳甲取食还导致喜旱莲子草植被指数的变化。在近红外区760～810 nm波段莲草直胸跳甲的控制效果与喜旱莲子草冠层光谱反射率呈极显著负相关,相关系数为-0.92,故可将760～810 nm波长范围作为监测莲草直胸跳甲对喜旱莲子草控制效果的敏感波段。     

Longevity,dormancy and germination of Cyanus segetum

Cyanus segetum is an iconic, colourful weed in arable fields that provides ecological and societal services. To understand better both the infestation dynamics of C. segetum as an abundant, harmful weed and maintain sustainable populations where it provides beneficial services, we compared information on seed dormancy, seed longevity and germination conditions in two populations. Persistence of seeds buried in the soil was low, with <10% viable after 3 years. Periodic dormancy cycling was observed over the 4 years in the soil, with a maximum of dormant seeds in the spring and a minimum in the autumn; however, 20% of the seeds were non‐dormant all the time. Seeds of C. segetum were positive photosensitive, but light requirement varied among populations. Base water potential for germination was ?1 MPa. Base temperature ranged from 1 to 2°C. Optimum temperature for germination was about 10 to 15°C, but the mean thermal time varied greatly between populations, from 80 to 134 day °C. Photoperiod and temperature combinations had no effect on germination percentage, but both reduced the germination rate. Burial deeper than 2 cm greatly reduced germination and seedling emergence strongly decreased at depths >0.5 cm. No seeds buried deeper than 8 cm emerged. Low seed longevity and a wide range of germination conditions could partly explain the rapid disappearance of C. segetum populations after herbicide application began in western Europe. However, yearly sowing in restoration areas does not seem to be essential.     

Olive processing wastes for weed control

The herbicidal effect of olive processing wastes (OPW) on some weed species in wheat, maize and sunflower was investigated in the Aegean region of Turkey. In trials with maize and sunflower, OPW was applied as an air‐dried solid form at 3 and 4.5 kg m^?2. It provided an effectiveness level on Portulaca oleracea of 63–98%. In trials with wheat, OPW was applied as solid and liquid forms, each at two different doses, namely 4.5 and 6 kg m^?2 (solid), and 5 and 10 L m^?2 (liquid). Solid OPW provided a reduction in total weed coverage of 75% and 81% at doses of 4.5 and 6 kg m^?2, respectively. The weed coverage reduction by liquid OPW was 39% and 62% with 5 and 10 L m^?2, respectively. Apart from 12–26% reduction of the number of germinating seeds, OPW showed no toxic effects on maize and sunflower. Wheat was affected in the initial stages but no adverse effect was detected at harvest. It can be concluded that the herbicidal effect of OPW may be considered as an alternative to chemical weed control in some important summer crops (maize and sunflower) and for most of the weeds in winter wheat.     

Assessment of Brown Planthopper, (Nilaparvata lugens) [Stål], damage in rice using hyperspectral remote sensing

Hyperspectral remote sensing was used to detect stress on potted rice plants caused by the Brown Planthopper (BPH), Nilaparvata lugens (Stål). BPH damage influenced reflectance of rice plants compared to uninfested plants in the visible and near-infrared regions of the electromagnetic spectrum. Correlations between plant reflectance and BPH damage, when plotted against wavelengths, enabled us to identify four sensitive wavelengths, at 1986, 665, 1792 and 500 nm, in relation to BPH stress on rice plants. Based on rice plant reflectance corresponding to the sensitive wavelengths, three hyperspectral indices were developed. The BPH damage showed a positive association with normalized pigment chlorophyll index, and a negative relationship with normalized difference vegetation index and soil adjusted vegetation index. Using rice plant reflectance corresponding to the sensitive wavelengths, a multiple-linear regression model was developed and validated, which would facilitate assessment of BPH damage based on rice plant reflectance, thereby ensuring prompt forewarning to stakeholders.     

Spatial and temporal patterns of Lolium rigidum–Avena sterilis mixed populations in a cereal field

Through a detailed case study of a two‐species (Lolium rigidum and Avena sterilis) weed community at contrasting scales, this paper examined factors that affect weed distribution across space and time in a commercial wheat field in north‐east Spain. A. sterilis showed relatively stable spatial distribution and spatial structure of its population over time at large scale, with well‐defined patches, although weed density rose quickly. L. rigidum showed poorly defined patches that were not stable across time. Interaction between species could explain to some degree the spatial distribution at large scale: a negative relationship was detected between the spatial structures of both weed populations. At fine scale, both species showed a clear interaction effect from primary dispersal (more important in A. sterilis) and secondary dispersal from combine harvesting (more important in L. rigidum).     

Spatial stability of Avena sterilis ssp. ludoviciana populations under annual applications of low rates of imazamethabenz

Long‐term experiments were conducted in two winter barley fields in central Spain to determine the spatial stability of Avena sterilis ssp. ludoviciana populations under annual applications of low rates of imazamethabenz herbicide. Weed density was sampled every year (over 5 years in the first field and over 3 years in the second) on the same grid locations prior to herbicide application. Although weed patches were stable in their location, weed density decreased in most of the years. In the first field, the populations decreased exponentially over the 5‐year period. The rates of population decline were dependent on the initial density of the population, being higher for the central core of the patches and lower for the low‐density areas. Under the conditions present in this experiment, it was possible to reduce heavy weed patches (up to 1200 seedlings m^?2) down to relatively safe levels (18 seedlings m^?2) in a period of 3 years using a density‐specific control programme, applying low rates of herbicides when weed densities were below a given level (1000 seedlings m^?2). However, under adverse environmental conditions, half rates of the herbicide failed to control the weed populations adequately. The stability of the location of patches of A. sterilis ssp. ludoviciana suggest that weed seedling distributions mapped in one year are good predictors of future seedling distributions. However, the actual densities established each year will depend on the control level achieved the previous year and the climatic conditions present during the establishment period.     

韭菜中毒死蜱残留量与高光谱特征参数的相关性建模

本研究目的在于分析农药残留量(pesticide residue,PR)与高光谱中响应特征参数之间的关系,并利用筛选的光谱特征参数建立反演毒死蜱残留量的有效模型。首先采用ASD Fieldspec高光谱仪测得韭菜样本的光谱,通过气相色谱-质谱联用(GC-MS)法测得毒死蜱残留量(PR)值;分析样本光谱反射率值及其一阶微分值与毒死蜱残留量的相关性,计算33个高光谱特征参数与毒死蜱残留量的相关性;根据相关系数高低选择敏感的光谱特征参数;最后采用最佳相关系数下的光谱特征参数对毒死蜱残留量进行建模反演。相关性分析结果显示:近红外波段789~867 nm范围内一阶微分光谱值与PR值呈正相关,1 860 nm处一阶微分光谱值(first-order differential 1 860 nm,FD1860)与PR值紧密相关;在33个高光谱特征参数中,近红外一阶微分总和(the sum of first-order differential near infrared,SDnir)与PR值呈良好的正相关关系。基于此,文章以供试样本的FD1860和SDnir观测值为自变量,分别建立了3个预测毒死蜱残留量的模型,即线性、二次多项式及指数模型,并采用交叉验证测试方法检验了模型的合理性。对实验所得决定系数R2和预测均方根误差(RMSE)的评价结果表明,以SDnir为自变量构建的模型稳定性强,其二次多项式模型是最佳反演毒死蜱残留量的有效模型。因此,样本的高光谱特征参数SDnir的变化幅度直接反映了韭菜样本中毒死蜱残留量的变化,表明运用蔬菜的高光谱特征参数反演蔬菜中农药残留量的方法是可行的。     

Weed seed predation rate in cereals as a function of seed density and patch size,under high predation pressure by rodents

Weed seed predation is an ecosystem service, influencing weed population dynamics. The impact of weed seed predation on weed population dynamics depends on how predators respond to seed patches at the field scale. Seed predation will be most effective if the proportion of seeds predated increases with increasing size and seed density of patches. Density‐dependent rodent seed predation was measured by varying seed density and patch size in four irrigated conventionally managed cereal fields in north eastern Spain. Artificial weed seed patches were created by applying a range of Lolium multiflorum seed densities from 0 to 7500 seeds m^?2 in 225 m² patches (2008) or in patches that varied in size from 1 to 9 m² (2009). Seed predation was estimated using seed cards and seed frames. The granivorous rodents Mus spretus and Apodemus sylvaticus caused high seed predation rates (92%) in three fields, whereas in a fourth field, it was lower (47%). Rodents responded in an inversely density‐dependent manner, but this had little biological meaning as even in patches seeded with the highest density, the input to the soil seedbank was reduced by 88%. For the period of time this experiment lasted, hardly any new seeds would have entered the seedbank.     

Composition of the stubble weed flora and its role for Heterodera schachtii in the year preceding sugar beet production

In Germany, sugar beet is often rotated with 2 years of cereal. Extensive fallow periods between cereal harvest and autumn primary tillage allow for a weed flora to develop. Broad‐leaved weeds could potentially be alternate hosts for the common nematode Heterodera schachtii, one of the most important pests of sugar beet. Between 2009 and 2012, annual weeds developing in cereal stubble fields during July to mid‐October in the season prior to sugar beet were surveyed, including known hosts of H. schachtii. Yearly weather patterns and agronomic practices possibly impacted weed species composition and weed population densities. During September, Chenopodium album, Cirsium arvense, Convolvulus arvensis, Mercurialis annua, Polygonum spp., Solanum nigrum and Sonchus spp. occurred at the highest frequencies. Weed hosts of H. schachtii were present, but densities, frequencies and uniformity were limited. In 2010 and 2011, staining for nematodes in roots revealed juvenile penetration of some weeds but few adult stages. No indication of nematode reproduction of H. schachtii was found on these weed hosts. A fairly stable weed flora was detected on stubble fields that could provide some carry over for weed species. An elevated risk for nematode population density build‐up on these weeds was not found and management of these weeds at the observed densities during the stubble period for nematological reasons appeared unnecessary.     

Evaluating the potential of Unmanned Aerial Systems for mapping weeds at field scales: a case study with Alopecurus myosuroides

Mapping weed densities within crops has conventionally been achieved either by detailed ecological monitoring or by field walking, both of which are time‐consuming and expensive. Recent advances have resulted in increased interest in using Unmanned Aerial Systems (UAS ) to map fields, aiming to reduce labour costs and increase the spatial extent of coverage. However, adoption of this technology ideally requires that mapping can be undertaken automatically and without the need for extensive ground‐truthing. This approach has not been validated at large scale using UAS ‐derived imagery in combination with extensive ground‐truth data. We tested the capability of UAS for mapping a grass weed, Alopecurus myosuroides , in wheat crops. We addressed two questions: (i) can imagery accurately measure densities of weeds within fields and (ii) can aerial imagery of a field be used to estimate the densities of weeds based on statistical models developed in other locations? We recorded aerial imagery from 26 fields using a UAS . Images were generated using both RGB and R_mod (R_mod 670–750 nm) spectral bands. Ground‐truth data on weed densities were collected simultaneously with the aerial imagery. We combined these data to produce statistical models that (i) correlated ground‐truth weed densities with image intensity and (ii) forecast weed densities in other fields. We show that weed densities correlated with image intensity, particularly R_mod image data. However, results were mixed in terms of out of sample prediction from field‐to‐field. We highlight the difficulties with transferring models and we discuss the challenges for automated weed mapping using UAS technology.