Spatial Distribution Of Weeds Research Articles

Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery

Weed invasion of crop fields, such as maize, is a major threat leading to yield reductions or crop right-offs for smallholder farming, especially in developing countries. A synoptic view and timeous detection of weed invasions can save the crop. The sustainable development goals (SDGs) have identified food security as a major focus point. The objectives of this study are to: (1) assess the precision of mapping maize-weed infestations using multi-temporal, unmanned aerial vehicle (UAV), and PlanetScope data by utilizing machine learning algorithms, and (2) determine the optimal timing during the maize growing season for effective weed detection. UAV and PlanetScope satellite imagery were used to map weeds using machine learning algorithms—random forest (RF) and support vector machine (SVM). The input features included spectral bands, color space channels, and various vegetation indices derived from the datasets. Furthermore, principal component analysis (PCA) was used to produce principal components (PCs) that served as inputs for the classification. In this study, eight experiments are conducted, four experiments each for UAV and PlanetScope datasets spanning four months. Experiment 1 utilized all bands with the RF classifier, experiment 2 used all bands with SVM, experiment 3 employed PCs with RF, and experiment 4 utilized PCs with SVM. The results reveal that PlanetScope achieves accuracies below 49% in all four experiments. The best overall performance was observed for experiment 1 using the UAV based on the highest mean accuracy score (>0.88), which included the overall accuracy, precision, recall, F1 score, and cross-validation scores. The findings highlight the critical role of spectral information, color spaces, and vegetation indices in accurately identifying weeds during the mid-to-late stages of maize crop growth, with the higher spatial resolution of UAV exhibiting a higher precision in the classification accuracy than the PlanetScope imagery. The most optimal stage for weed detection was found to be during the reproductive stage of the crop cycle based on the best F1 scores being indicated for the maize and weeds class. This study provides pivotal information about the spatial distribution of weeds in maize fields and this information is essential for sustainable weed management in agricultural activities.

Early-Season Mapping of Johnsongrass (Sorghum halepense), Common Cocklebur (Xanthium strumarium) and Velvetleaf (Abutilon theophrasti) in Corn Fields Using Airborne Hyperspectral Imagery

Accurate information on the spatial distribution of weeds is the key to effective site-specific weed management and the efficient and sustainable use of weed control measures. This work focuses on the early detection of johnsongrass, common cocklebur and velvetleaf present in a corn field using high resolution airborne hyperspectral imagery acquired when corn plants were in a four to six leaf growth stage. Following the appropriate radiometric and geometric corrections, two supervised classification techniques, such as spectral angle mapper (SAM) and spectral mixture analysis (SMA) were applied. Two different procedures were compared for endmember selections: field spectral measurements and automatic methods to identify pure pixels in the image. Maps for both, overall weeds and for each of the three weed species, were obtained with the different classification methods and endmember sources. The best results were achieved by defining the endmembers through spectral information collected with a field spectroradiometer. Overall accuracies ranged between 60% and 80% using SAM for maps that do not differentiate the weed species while it decreased to 52% when the three weed species were individually classified. In this case, the SMA classification technique clearly improved the SAM results. The proposed methodology shows it to be a promising prospect to be applicable to low cost images acquired by the new generation of hyperspectral sensors onboard unmanned aerial vehicles (UAVs).

Opportunities from Unmanned Aerial Vehicles to Identify Differences in Weed Spatial Distribution between Conventional and Conservation Agriculture

Weeds are one of the major issues in agricultural production and they are present in most agricultural systems. Due to the heterogeneity of weed distribution, understanding spatial patterns is paramount for precision farming and improving sustainability in crop management. Nevertheless, limited information is currently available about the differences between conventional agricultural (CV) weed spatial patterns and weed spatial patterns in conservation agricultural systems (CA); moreover, opportunities to use unmanned aerial vehicles (UAV) and recognition algorithms to monitor these differences are still being explored and tested. In this work, the opportunity to use UAVs to detect changes in spatial distribution over time between CA and CV fields was assessed for data acquisition. Acquired data were processed using maximum likelihood classification to discriminate between weeds and surrounding elements; then, a similarity assessment was performed using the ‘equal to’ function of the raster calculator. The results show important differences in spatial distribution over time between CA and CV fields. In the CA field 56.18% of the area was infested in both years when the field margin effect was included, and 22.53% when this effect was excluded; on the other hand, in the CV field only 11.50% of the area was infested in both years. The results illustrate that there are important differences in the spatial distribution of weeds between CA and CV fields; such differences can be easily detected using UAVs and identification algorithms combined.

Assessment of nozzle control strategies in weed spot spraying to reduce herbicide use and avoid under- or over-application

Spot spraying is a method that can meet the objectives of reduced herbicide by maintaining a high level of weed control efficiency without lowering crop yield and harvest quality. Individual nozzle control systems show great potential for herbicide reduction. Nevertheless, this method of application also entails the risk of under-application on weed surfaces because of the lack of spray overlap and irregular dosing. Thus, nozzle control strategies need to be refined and assessed, regarding herbicide reduction and the ability to apply the prescribed dose on the target surfaces. Six control strategies were considered by activating complementary adjacent nozzles or increasing the flowrate of specific nozzles. Theoretical analyses and simulations were carried out to compare these strategies using three indicators. Considering a simplified description of the weed spatial distribution, a herbicide reduction indicator was expressed analytically for each strategy as a function of the weed coverage rate and patch width. For each strategy, numerical simulations were also carried out to compute under- and over-application indicators considering six weed coverage rates, eight patch widths and six different spray patterns. Graphs and charts were developed to provide convenient tools to help select the best technical approach for reducing chemical applied whilst keeping the prescribed dose on targets. In particular, a good compromise could consist of setting the weed detection width (associated with each nozzle) at twice the nozzle spacing and using triangular spray patterns combined with double spray overlaps. • Six nozzle control strategies are compared for herbicide spot spraying. • Herbicide reduction is modelled analytically by weed patch and spraying features. • Herbicide misapplications are assessed using numerical simulations. • Setting the detection width at twice the nozzle spacing is a good compromise. • Triangular spray patterns combined with double overlaps are preferred.

Структура сорного компонента и его пространственное распределение в полях зернопропашного севооборота

The article examines the species composition and spatial distribution of weeds, depending on the methods of processing crops of grain-tilled crop rotation. The uneven distribution of weeds over the area of the field requires the development of methods to identify weeds and their spatial location, which will allow the use of differentiated use of herbicides in precision farming systems.

Simulation-aided study of herbicide patch spraying: Influence of spraying features and weed spatial distributions

Patch spraying is a technological way to reduce herbicide amounts required to control weeds by triggering spraying only where weeds lie. Nevertheless, the adoption rate of this technology by farmers is still low and there is a lack of efficient tools to assess patch herbicide applications depending on spraying features and weed spatial distributions. To avoid unrealistic in-field experiments, a virtual patch sprayer is designed and computer simulations are carried out to quantify herbicide reduction possibilities and assess the ability to apply the right rate on weed patches to ensure herbicide efficiency. The spray application simulator combines the theoretical description of the sprayer using various boom section widths (from 0.5 to 24 m) with experimental data describing various nozzle spray patterns obtained from three commercial nozzles. Virtual weed infestation maps are designed using random locations of weed patches with various spatial aggregation degrees modelled by different weed coverage rates and elliptical patch sizes. Simulation results are analysed through two performance indicators: i) the herbicide reduction ratio obtained with patch spraying compared to a uniform broadcast application (on the entire field area), ii) the proportion of weed patch area on which the application rate is higher than 85% of the prescribed application rate. Computer simulations enable to estimate a simplified relationship to quantify the reduction of herbicide use as a function of the weed coverage rate, the size of weed patches and the boom section width. This demonstrates that computer simulations provide practical tools to estimate the sprayer spatial resolution required to reach a given herbicide reduction target according to the weed spatial distribution. Simulations also demonstrate that the use of narrow section widths equipped with traditional nozzles leads to a significant proportion of weed areas exposed to herbicide under-application in the case of small patches.

Consumer-grade UAV utilized for detecting and analyzing late-season weed spatial distribution patterns in commercial onion fields

Studying weed spatial distribution patterns and implementing precise herbicide applications requires accurate weed mapping. In this study, a simple unmanned aerial vehicle (UAV) was utilized to survey 11 dry onion (Allium cepa L.) commercial fields to examine late-season weed classification and investigate weeds spatial pattern. In addition, orthomosaics were resampled to a coarser spatial resolution to simulate and examine the accuracy of weed mapping at different altitudes. Overall, 176 weed maps were generated and evaluated. Pixel and object-based image analyses were assessed, employing two supervised classification algorithms: Maximum Likelihood (ML) and Support Vector Machine (SVM). Classification processes resulted in highly accurate weed maps across all spatial resolutions tested. Weed maps contributed to three insights regarding the late-season weed spatial pattern in onion fields: 1) weed coverage varied significantly between fields, ranging from 1 to 79%; 2) weed coverage was similar within and between crop rows; and 3) weed pattern was patchy in all fields. The last finding, combined with the ability to map weeds using a low cost, off-the-shelf UAV, constitutes an important step in developing precise weed control management in onion fields.

Weed Decision Threshold as a Key Factor for Herbicide Reductions in Site-Specific Weed Management

The objective of this research was to explore the influence that weed decision threshold (DT; expressed as plants m−2), weed spatial distribution patterns, and spatial resolution of sampling have on potential reduction in herbicide use under site-specific weed management. As a case study, a small plot located in a typical corn field in central Spain was used, constructing very precise distribution maps of the major weeds present. These initial maps were used to generate herbicide prescription maps for each weed species based on different DTs and sampling resolutions. The simulation of herbicide prescription maps consisted of on/off spraying decisions based on information from two different approaches for weed detection: ground-based vs. aerial sensors. In general, simulations based on ground sensors resulted in higher herbicide savings than those based on aerial sensors. The extent of herbicide reductions derived from patch spraying was directly related to the density and the spatial distribution of each weed species. Herbicide savings were potentially high (up to 66%) with relatively sparse patchy weed species (e.g., johnsongrass) but were only moderate (10 to 20%) with abundant and regularly distributed weed species (e.g., velvetleaf). However, DT has proven to be a key factor, with higher DTs resulting in reductions in herbicide use for all the weed species and all sampling procedures and resolutions. Moreover, increasing DT from 6 to 12 plants m−2resulted in additional herbicide savings of up to 50% in the simulations for johnsongrass and up to 28% savings in the simulations for common cocklebur. Nonetheless, since DT determines the accuracy of patch spraying, the consequences of using higher DTs could be leaving areas unsprayed, which could adversely affect crop yields and future weed infestations, including herbicide-resistant weeds. Considering that the relationship between DT and accuracy of herbicide application depends on weed spatial pattern, this work has demonstrated the possibility of using higher DT values in weeds with a clear patchy distribution compared with weeds distributed regularly.

Evaluation of UAV imagery for mapping Silybum marianum weed patches

ABSTRACTSilybum marianum (L.) Gaertn weed has the tendency to grow in patches. In order to perform site-specific weed management, determining the spatial distribution of weeds is important for its eradication. Remote sensing has been used to perform species discrimination and it offers promising techniques for operational weed mapping. In the present study, the feasibility of high-resolution imaging for S. marianum detection and mapping is reported. A multispectral camera (green–red–near-infrared) mounted on a fixed wing unmanned aerial vehicle (UAV) was used for the acquisition of high-resolution images with pixel size of 0.1 m. The maximum likelihood (ML) classifier was used to classify the S. marianum among other weed species present in a field, with Avena sterilisL. being predominant. As input to the classifier, the three spectral bands and the texture were used. The scale of the mapping was varied by degrading the image resolution to evaluate classification performance, with 1 m resolution providing the highest classification accuracy. The classification rates obtained using ML reached an overall accuracy of 87.04% with a K-hat statistic of 74%. The results prove the feasibility of operational S. marianum mapping using UAV and multispectral imaging.

Sampling for weed spatial distribution mapping need not be adaptive

Weeds are species of interest for ecologists because they are competitors of the crop for resources but they also play an important role in maintaining biodiversity in agroecosystems. To study their spatial distribution at the field scale, only sampled observations are available due to the cost of sampling. Weeds sampling strategies are static. However, in the domain of spatial sampling, adaptive strategies have also been developed with, for some of them, an important on-line or off-line computational cost. In this article we provide answers to the following question: Are the current adaptive sampling methods efficient enough to motivate a wider use in practice when sampling a weed species at a field scale? We provide a comparison of the behaviour of 8 static strategies and 3 adaptive ones on four criteria: density class estimation, map restoration, spatial aggregation estimation, and sampling duration. From two weeds data sets, we estimated six contrasted Markov Random Field (MRF) models of weed density class spatial distribution and a model for sampling duration. The MRF models were then used to compare the strategies on a large set of simulated maps. Our main finding was that there is no clear gain in using adaptive sampling strategies rather than static ones for the three first criteria, and adaptive strategies were associated to longer sampling duration. This conclusion points out that for weed mapping, it is more important to build a good model of spatial distribution, than to propose complex adaptive sampling strategies.

Interseeding a Cover Crop as a Weed Management Tool is More Compatible with Soybean than with Maize in Organic Farming Systems

Weeds are the most serious threat to crop production in organic farming systems. Information on the spatial distribution of weeds is important for effective weed management. The objective of this study was to evaluate the effect of the ground cover of the main crops (soybean and maize) and cover crop on the spatial distribution of weeds for two row sections, ‘within the row (IR)’ and ‘between the rows (BR)’. The cover crop was interseeded in BR 3 – 4 weeks after sowing the main crops in two years, and weed density and vegetation cover ratio (VCR, an index of ground cover) of the main crops and cover crop were measured. There was a significant difference in the spatial distribution of weeds between the two main crops. In IR, weed density was higher in maize than in soybean, while in BR, it was higher in soybean. This means that weed suppression in IR was more important for maize, while the suppression in BR was more important for soybean. The negative relationship between VCR of the main crop plus cover crop and weed density in each row section suggests that the difference in ground cover was one of the reasons for the difference in weed density between the two main crops. The cover crop markedly increased the VCR in BR, but only slightly increased VCR in IR in both main crops. These results suggest that the cover crop was more compatible with soybean than with maize, because the high weed density in IR of maize could not be decreased sufficiently by the slight increase of VCR in BR by the cover crop.

A Technique for High-Accuracy Ground-Based Continuous Weed Mapping at Field Scale

<abstract><title><italic>Abstract.</italic></title> Site-specific weed management was proven beneficial on both economic and environmental aspects. Little scientific literature exists about weed spatial distribution in corn fields because manual data acquisition is tedious and time-consuming. Airborne and satellite imagery resolution is too low to provide sound data to answer important questions about weed spatial distribution during the critical weed control period (weed seedlings). This article describes a technique to acquire high-resolution ground-based imagery data to quantify weed cover after crop emergence. The technique is based on a mobile platform traveling at a slow walking speed, controlling ambient light, and triggering a camera at fixed intervals. Vegetation is segmented from non-vegetation pixels using principal component analysis, and weed vegetation is distinguished from crop vegetation by location. Automatic image segmentation resulted in more than 99% detection accuracy, and crop-weed distinction resulted in 0.37% error. The technique enabled the creation of 19 one-hectare maps of weed cover with more than 3000 points per map. These high-definition weed maps can be used to analyze the spatial distribution of weeds and the cost-effectiveness of site-specific weed management.

Inferring weed spatial distribution from multi-type data

An accurate weed management in a context of sustainable agriculture relies on the knowledge about spatial weed distribution within fields. To improve the representation of patchy spatial distributions of weeds, several sampling strategies are used and lead to various weed measurements (abundance, count, patch boundaries). Here, we propose a hierarchical Bayesian model which includes such multi-type data and which allows the interpolation of weed spatial distributions (using a MCMC algorithm). The weed pattern is modeled with a log Gaussian Cox process and the various weed measurements are modeled with different observation processes. The application of the method to simulated data shows the advantage of combining several types of data (instead of using only one type of data). The method is also applied to infer the weed spatial distribution for real data.

Variabilidade espacial e temporal de plantas daninhas em Latossolo Vermelho argiloso sob semeadura direta

The knowledge of weed spatial distribution allows specific site management that leads to herbicide savings and environmental quality protection. This work aimed to evaluate the spatial distribution of weeds in a clay Oxisol in Campinas, Sao Paulo State, Brazil during the period between 2003 and 2008. The sampled area measured 3.42 ha and featured 302 sampling points, in a 10 x 10 meter regular grid. Based on high and very high values of coefficient of variation and on its asymmetry, it was shown that inferences of mean values did not represent the sampled population of weeds. Also, the semivariograms showed a spatial pattern of data for weeds, even if they were separated according to its leaf morphology. The clustered weed distribution were well designed at the kriged maps and also observed according to distribution index. With the geostatistical approach, it was possible to clear mapping site-specific managing zones with 5 to 10 fold differences in weed count, in order to confirm that this technique can be used as ancillary tool in agricultural practices.

Variabilidade espacial de plantas daninhas em dois sistemas de manejo de solo

Estudos sobre a distribuição espacial das plantas daninhas são importantes no contexto agronômico e ambiental. O objetivo deste trabalho foi descrever a distribuição espacial de plantas daninhas em dois sistemas de manejo de solo. O estudo foi realizado em um Latossolo Vermelho distroférrico, em Campo Mourão, PR, utilizado em sistema de plantio direto (PD) e preparo convencional do solo (PC). Estabeleceu-se uma malha referenciada em coordenadas x e y, totalizando 128 pontos amostrais. Avaliou-se a biomassa da parte aérea das plantas, a densidade de plantas, a densidade de Commelina benghalensis e a densidade de Bidens pilosa. Os dados foram submetidos à análise exploratória e à análise variográfica. A variabilidade espacial foi estudada através da geoestatística, mediante a análise de semivariogramas, interpolação por krigagem e construção de mapas de isolinhas. O alcance das variáveis biomassa e densidade de B. pilosa é maior no PD que no PC; para a variável densidade de plantas, o alcance é maior no PC. A distribuição espacial descrita por técnicas geoestatísticas, mostra seu potencial no mapeamento de plantas daninhas o que permite estratégias de manejo localizado e o estabelecimento de critérios para experimentos futuros.

Identifying associations among sterile oat (Avena sterilis) infestation level, landscape characteristics, and crop yields

Information on weed spatial distribution could improve weed management decisions. Herbicide use could be reduced if applied only to field zones with an infestation level higher than a specific economic threshold. In this study, we surveyed 31 winter barley fields in different regions of Spain to describe the spatial distribution of sterile oat and to analyze the relationship between sterile oat infestation level and landscape and crop yield attributes. Elevation and slope angle, crop yield and slope aspect were the main factors in order of importance in explaining the distribution of sterile oat. In general, greater infestation levels were observed in flat lowland and concave landscapes, with a low crop yield, and on northern exposures (when slope ≥ 2%). We could define field zones with a higher risk (&gt; 6 times probability) of having sterile oat problems. High-probability zones, defined by topographic attributes alone, occupied 24% of the total area and contained 46% of the high infestation levels, whereas zones defined by topographic and crop yield attributes constituted 14% of the total area and contained 31% of the infestation.

Weed flora distribution in Greek cotton fields and its possible influence by herbicides

A weed survey methodology was used for 2 years in three provinces in Greece to determine the abundance and spatial distribution of weeds in cotton (Gossypium hirsutum L.) fields. Based on a stratified random sampling procedure, the most frequently occurring weeds were counted in 150 cotton fields. The field surveys were conducted late in the growing season; hence, the weed populations consisted of species that had been present during the critical competitive period for the crop and may have contributed to yield losses.Solanum nigrum was the most abundant weed in the surveyed fields of the southern province, followed byCyperus rotundus, Convolvulus arvensis, Xanthium strumarium, Chrozophora tinctoria andCynodon dactylon, in descending order. The ranked weed flora in the fields of the northern province was differentiated, suggesting the geographical distribution of weed species. The weedsDatura stramonium andS. nigrum were recorded in high abundance and followed byAmaranthus spp.,Abutilon theophrasti, Portulaca oleracea, Chenopodium album andXanthium spinosum, in descending order. Although the use of preplant incorporated herbicides is the dominant practice in cotton cultivation, certain weeds continue to spread in increasing densities.

Sampling to make maps for site-specific weed management

Growers need affordable methods to sample weed populations to reduce herbicide use with site-specific weed management. Sampling programs and methods of developing sampling programs for integrated pest management are not sufficient for site-specific weed management because more and different information is needed to make treatment maps than simply estimate average pest density. Sampling plans for site-specific weed management must provide information to map the weeds in the field but should be developed for the objective of prescribing spatially variable management. Weed scientists will be most successful at designing plans for site-specific weed management if they focus on this objective throughout the process of designing a sampling plan. They must also learn more about the spatial distribution and dynamics of weed populations and use that knowledge to identify cost-effective plans, recommend methods to make maps as well as collect data, and find ways to evaluate maps that reflect management to be prescribed from the map. Foremost, sampling must be thought of as an ongoing process over time that uses many types of information rather than a single event of collecting one type of information. Specifically, scientists will need to identify common characteristics rather than just differences of the spatial distribution of weeds among fields and species, recognize that map accuracy may be a poor indicator of the value of a sampling plan, and develop methods to use growers' knowledge of the distribution of weeds and past spatially variable management within a field for both making a map and recommending a sampling plan. The value of proposed methods for sampling and mapping must also be demonstrated or adoption of site-specific weed management might be limited to growers who enjoy using sophisticated technology.

Multi-species weed spatial variability and site-specific management maps in cultivated sunflower

Abstract Geostatistical techniques were used to describe and map weed spatial distribution in two sunflower fields in Cabello and Monclova, southern Spain. Data from the study were used to design intermittent spraying strategies. Weed species, overall infestation severity (IS) index, and spatial distribution varied considerably between the two sites. Weed species displayed differences in spatial dependence regardless of IS. The IS mapping of each single weed and of the overall infestation was achieved by kriging, and site-specific application maps were then drawn based on the multi-species weed map and the estimated economic threshold (ET). Herbicide treatment was assumed to be needed for an overall IS score of 2 or 3, and the infested “area exceeding the economic threshold” was determined. The overall weed-infested area varied considerably between locations. About 99 and 38% of the total area was moderately infested (IS ≥ 2) at Monclova and Cabello, respectively. Therefore, if a given herbicide were appl...

A risk‐qualified approach to calculate locally varying herbicide application rates

Weed competition can decrease crop yield and profit. Herbicides are applied to reduce weed populations, minimize crop loss and maximize profit. Traditional practice is to apply herbicides at a uniform rate over an entire field. Complete knowledge of the weed distribution and appropriate instrumentation on the spraying equipment would allow the farm manager to apply the ‘correct’ locally varying herbicide application rate. The locally variable rate would be greater in areas of high weed density and less where there are few weeds. A locally varying treatment would have both economic and environmental advantages. A major challenge facing farm managers is the unavoidable uncertainty in the spatial distribution of weeds in any particular field. This uncertainty in weed distribution influences the optimal locally varying herbicide rate. A mathematical model is presented to calculate the optimal herbicide application rate using geostatistical models of uncertainty in weed density combined with principles from decision making. Weed data from a 34‐ha field near Saskatoon, Saskatchewan, Canada, illustrate the application of these tools. Weed control was achieved with a significant reduction in total herbicide use.