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Article

Assessing Weed Canopy Cover in No-Till and Conventional Tillage Plots in Winter Wheat Production Using Drone Data

by
Judith N. Oppong
*,
Clement E. Akumu
and
Sam Dennis
Department of Agricultural Sciences and Engineering, College of Agriculture, Tennessee State University, Nashville, TN 37209, USA
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(11), 2706; https://doi.org/10.3390/agronomy14112706
Submission received: 26 September 2024 / Revised: 13 November 2024 / Accepted: 15 November 2024 / Published: 16 November 2024
(This article belongs to the Special Issue Weed Ecology, Evolution and Management)
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Abstract

:
Weed canopy cover assessment, particularly using drone-acquired data, plays a vital role in precision agriculture by providing accurate, timely, and spatially detailed information, enhancing weed management decision-making in response to environmental and management variables. Despite the significance of this approach, few studies have investigated weed canopy cover through drone-based imagery. This study aimed to fill this gap by evaluating the effects of conventional tillage (CT) and no-till (NT) practices on weed canopy cover in a winter wheat field over two growing seasons. Results indicated that in the 2022–2023 season, weed populations were similar between tillage systems, with a high mean weed cover of 1.448 cm2 ± 0.241 in CT plots. In contrast, during the 2023–2024 season, NT plots exhibited a substantially higher mean weed cover (1.784 cm2 ± 0.167), with a significant overall variation (p < 0.05) in weed distribution between CT and NT plots. These differences suggest that, while CT practices initially mask weed emergence by burying seeds and disrupting root systems, NT practices encourage greater weed establishment over time by leaving seeds near the soil surface. These findings provide valuable insights for optimizing weed management practices, emphasizing the importance of comprehensive approaches to improve weed control and overall crop productivity.

1. Introduction

Wheat is a major staple food in most parts of the world. The rising demand for food security among the global population compels farmers to continually strive for wheat cultivars that maximize crop yields to meet this growing need [1]. However, weeds remain a major problem in winter wheat production in most parts of the world [2]. The occurrence of certain weed varieties depends largely on some soil and environmental conditions, such as climate, tillage effects, and environmental factors [3,4]. These conditions allow the recurring appearance of weeds on many wheat fields. The science of effective weed management is crucial to attaining optimum winter wheat yield. This is because weeds compete with crop species for resources needed for proper growth, such as light, nutrients, and space [5]. Weed infestations can significantly lower crop yield while also increasing food costs for consumers.
Due to some traits like effective seed dispersal and durable and viable seed quality, common broadleaved weeds emerge biennially and annually [6]. According to Pala et al. [7], broadleaved weeds like mustard (Alliaria petolata), wild oat (Avena fatua), and corn buttercup (Ranunculus arvensis) are often difficult to manage because they have quicker regeneration rates and are highly resistant to certain herbicides. In most wheat fields, weed allelopathy is a peculiar occurrence where the germination and survival of winter wheat are influenced by one or more biochemical effects of weeds [8]. The results of 11 isolated field studies showed an average of 25% wheat yield losses due to weed allelopathy across multiple wheat-growing areas in the world [9]. However, Pál and Zsombik [10] asserted that some weeds, like common vetch (Vicia sativa L.), helped improve soil quality by serving as nitrogen fixing agents and sources of organic matter. This is because of the symbiotic relationship it has with some soil organisms.
Soil tillage has been adopted to prepare seed beds before sowing and is typically characterized by some degree of disturbance and loosening of the soil [11]. Tillage practices describe soil and farmland management activities between different cropping cycles [12]. No-till (NT) farming reduces soil erosion by maintaining surface cover, which enhances both physical and chemical properties of the soil [13]. It also enhances soil structure, increases water retention, and promotes the buildup of organic matter [14,15], leading to healthier soils. In contrast, conventional tillage (CT) disrupts soil layers [16], which can lead to increased erosion, compaction, loss of soil organic carbon [16,17], reduced organic matter content, and loss of soil biota [18]. A farmer’s choice of tillage method mostly depends on factors such as soil type, crop rotation, climate, economic considerations, weed and pest control, and long-term sustainability goals [19].
Furthermore, conventional tillage (CT) and no tillage (NT) practices have shown variations in weed abundance and crop yield in many wheat fields [20]. Tillage methods like NT and minimum tillage are said to significantly increase crop yield and reduce weed diversity by improving soil temperature and moisture content [21], while the CT system can increase or decrease weed density and crop yield [22]. According to Cordeau et al. [23], the presence of crop residues on the topsoil through the NT system could delay weed emergence and prevent weed–crop competition. Other researchers also found that NT systems, when combined with crop rotation and cover cropping, significantly reduced the overall weed seedbank compared to CT [24,25]. However, the absence of tillage requires the integration of alternative weed control measures, such as herbicides and cultural practices, to prevent weed pressure from escalating [26,27]. On the other hand, CT tends to redistribute weed seeds both vertically and horizontally in the soil profile [28], though frequent soil disturbance through tillage, as indicated by Hossain and Begum [29], could increase the likelihood of weed seed dormancy being broken, leading to more prolific weed infestations in future growing seasons.
Advances in precision agriculture have increasingly incorporated remote sensing technologies, including drone-based monitoring systems, which enable high-resolution, real-time observation of weed dynamics across agricultural landscapes [30]. Drones equipped with multispectral and hyperspectral sensors can capture detailed spectral information that differentiates weed species based on their unique spectral signatures, allowing researchers to identify and quantify weed presence at different stages of crop growth [30,31]. This capability is critical for developing adaptive weed management strategies, as it permits monitoring not only of weed cover but also of shifts in weed populations over time [32,33]. By mapping weed distribution and density at a field scale, drone-based imagery provides actionable data that informs precision applications of herbicides, which can reduce overall herbicide use and lower input costs [34,35,36]. Additionally, the integration of drone data supports the development of sustainable agricultural systems that balance weed control with environmental stewardship [37].
As weed management methods play crucial roles in influencing the emergence, diversity, and density of weed populations within crop production systems, the strategies employed, such as tillage practices and herbicide application [3,38], could directly affect the soil environment and weed seed bank dynamics [9]. These methods can also alter the timing and rate of weed emergence [39], potentially favoring or suppressing certain weed species, thus impacting overall weed diversity. Thus, this study aimed at using drone-acquired imagery to assess the impact of conventional tillage (CT) and no-till (NT) practices on weed canopy cover in a winter wheat field over two growing seasons. Central to this goal was understanding the effect of no-till and conventional tillage practices on weed canopy cover. Unlike previous studies, this research highlights seasonal variations in weed response to tillage practices and environmental conditions, specifically noting that NT practices led to significant increased weed cover. The reminder of this paper is structured as follows: a detailed materials and methods section describing the use of drone-acquired data for evaluating weed cover. The results section presents findings on the differences in weed cover between conventional tillage and no-till plots over two seasons, while the discussion interprets these results. Finally, the concluding section summarizes the study’s contributions, emphasizing the potential for targeted weed management strategies informed by drone data to enhance sustainability in agriculture.

2. Materials and Methods

2.1. Study Area

This study was conducted at Tennessee State University’s urban agricultural field in Davidson County (Figure 1). The specific area used for the research is situated at approximately 36.176° N latitude and 86.827° W longitude, near the Cumberland River. This southeastern U.S. location experiences cold winters and warm summers, with an average annual high temperature of about 70° F (21.1 °C) and an average annual low temperature of approximately 49° F (9.4 °C). The area receives an average annual precipitation of around 47.2 inches. The soil in this region, according to the Web Soil Survey (by the United States Department of Agriculture–Natural Resource Conservation Service), is primarily Byler silt loam (ByB), a moderately acidic soil derived from weathered limestone materials.

2.2. Methodology

The methodology used in the study involved several key procedures (Figure 2) to assess weed canopy cover in a winter wheat (Triticum aestivum) field. Initially, an unknown (vns) winter wheat variety was cultivated and monitored through its growing stages. A drone was employed to capture aerial images of the wheat field, providing a multispectral dataset at four (4) growth stages (tillering, jointing, booting, and mature stages) of the winter wheat. The captured images then underwent preprocessing to enhance their quality, followed by image processing techniques to prepare them for analysis. Subsequently, supervised image classification was performed at each growth stage to determine the extent of weed canopy cover. Statistical analysis was performed to test for the difference in weed canopy cover in no-till versus conventional tillage plots.

2.2.1. Growing of Winter Wheat

Winter wheat production was carried out within two different growing seasons (2022–2023 and 2023–2024). The growing field was divided into four (4) blocks and subdivided into forty (40) 6 m by 6 m plots. The forty plots were then randomly divided into no-till and conventional tillage plots (Figure 3). The conventional tillage plots were prepared using a bush hog tiller. A selective herbicide (2 percent Roundup®) (Alligare, LLC, Opelika, AL, USA, 36801)was used to control initial weeds on the field two weeks prior to planting of the winter wheat. The planting of the winter wheat seeds was made with a no-till planter on 19 October 2022, for the first season, and 14 November 2023, for the second season. The row spacing for the wheat was 20 cm (0.20 m). A fertilizer high in nitrogen (about 45 kg) was applied in the wheat plots at the tillering stage. There was no irrigation of the plots before or after planting of the wheat. The winter wheat was cut down on 10 June 2023 and 25 June 2024 for seasons one and two, respectively.

2.2.2. Drone Data Acquisition

An Inspire-2 drone, equipped with an Altum multispectral camera, was used to capture images of the cultivated field during four key stages of winter wheat growth: tillering, jointing, sbooting, and maturity. These stages were chosen because weeds were competitive with the winter wheat. The drone was operated at an altitude of 15 m above ground level and a speed of 3 m per second, with an overlap rate of 80–90%. The flight path was west-to-east (Figure 4). During the drone flight, the images were taken every two seconds and covered spectral bands including blue (450 to 520 nm), green (520 to 590 nm), red (630 to 690 nm), red-edge (690 to 730 nm), near-infrared (NIR) (770 to 890 nm), and longwave infrared thermal (LWIR) (10,600 to 11,200 nm). The drone images had a spatial resolution of about 1 cm. The images captured were geotagged, and radiometric calibration was carried out using a camera and sun radiation. Furthermore, the images were orthomosaiced in Pix4D mapper (version 4.8.0).

2.2.3. Weed Canopy Cover Mapping

Supervised deep learning classification was carried out to map and weed canopy cover for the four (4) growth stages of winter wheat. This was performed using the U-net pixel classification algorithm in ArcGIS Pro (version 3.3.1). The U-net classifier was used because of its high performance, reliability, and overall accuracy from an experimental study with the data. The classifier works within an encoder–decoder workflow [40], which allows it to max-pool and reconstruct segmented layers within the input image. The classification was performed using a training dataset derived from the input Red, Green, and Blue (RGB) image. The training dataset was acquired from digitized polygons (about one thousand polygons) for each growth stage. The distinct floral colors of the weed species within the drone images and digital images taken of the field guided the digitization process. The training dataset was divided into 80% model training and 20% validation. The common broadleaved weed species mapped at the different growing stages and growing seasons were henbit (Lamium amplexicaule), speedwell (Veronica spp.), mayweed (Matricaria discoidea), hairy buttercup (Ranunculus sardous), common vetch (Vicia sativa), and white clover (Trifolium repens L.).

2.2.4. Statistical Assessment of Tillage Systems

The classified images were converted into vector files to help determine the area coverage of the weeds in each plot. These values were then used to perform an independent-sample Mann–Whitney U-test (MWUT) in SPSS (IBM SPSS, version 22) to determine whether the distribution of weed cover over the years differed significantly between conventional tillage and no-till plots. The Mann–Whitney U-test was chosen because it compares two independent groups, where the observation in one group does not affect the other. The test evaluates the hypothesis that the distribution of weed cover is equal across the two tillage systems against the alternative hypothesis that the distribution differs between them.

3. Results

The 2022–2023 classified maps provided valuable insights into the spatial distribution of wheat and weed species across the field at the different growth stages of winter wheat. The identified broadleaved species (henbit, speedwell, and mayweed) were more dominant in the no-till (NT) plots at the tillering stage (Figure 5), compared to the conventional tillage (CT) plots. The weed distribution at this stage was skewed towards the eastern part of the field. At the jointing stage, winter wheat was more dominant than the identified broadleaved weed species (Figure 6). Hairy buttercup was the most dominant weed species in both NT and CT plots at the booting stage (Figure 7), though common vetch covered some plots at the northern part of the field. There was more winter wheat at the mature stage than there were broadleaved weeds, though traces of common vetch could be seen (Figure 8).
The classified maps from the 2023–2024 growing season showed more weed emergence in both the no-till and conventional tillage plots than seen from the previous year. At the tillering growth stage (Figure 9), henbit was more dominant in NT plots than in CT plots. Traces of mayweed and speedwell were found in all the plots. Common vetch and hairy buttercup were more present in no-till plots than they were in conventional tillage plots at the jointing growth stage (Figure 10). Though hairy buttercup was more present in NT plots at the booting stage, the dominance of common vetch increased in both no-till and conventional tillage plots (Figure 11). Unlike the first year, weed species, especially common vetch and hairy buttercup, were present and traceable at the mature growth stage in most NT and CT plots (Figure 12).
During the 2022–2023 season, the mean weed cover was similar between the two treatments, with conventional tillage plots showing a mean cover of 1.448 cm2 ± 0.241 and no-till plots exhibiting a comparable mean cover of 1.420 cm2 ± 0.261. However, a shift in weed cover dynamics was observed in the 2023–2024 growing period. In conventional tillage plots, weed cover significantly decreased to 0.579 cm2 ± 0.059, while it increased in no-till plots to 1.784 cm2 ± 0.017. Across both seasons, the overall mean weed cover for conventional tillage plots was 1.013 cm2 ± 0.128, whereas no-till plots had a higher overall mean weed cover of 1.604 cm2 ± 0.155 (Figure 13).
The Mann–Whitney U-test results indicated the differences in weed distribution across the tillage systems for the years 2022–2023 and 2023–2024, as well as for the combined data. For the year 2022–2023, there was no significant difference in weed distribution across the tillage systems (sig > 0.05). In contrast, the 2024 results showed a significant difference (sig < 0.001). With the combined data from both years, the significance was below the 0.05 level, indicating that, overall, there was a significant difference in weed distribution across the two tillage systems.
The weather data (Table 1) during the growth period of the winter wheat indicated that in 2023, the temperatures gradually increased from 8.2 °C in January to 21.1 °C in May, while rainfall remained relatively low, ranging from 2.5 to 4.1 mm, with slight increases in April and May. In contrast, the 2024 data showed a cooler start with January (2.6 °C), followed by a steady increase to 22.3 °C by May, accompanied by increased rainfall that started at 4.3 mm in January and reached 6.4 mm by May. This could have created a more favorable growing season for crops and weeds in 2024 compared to 2023.

4. Discussion

In this paper, the effects of conventional tillage (CT) and no-till (NT) practices on the canopy cover of weeds in a winter wheat field over a period of two growing seasons using drone-acquired data were evaluated. During the 2022–2023 season, speedwell, common vetch, and mayweed were more prevalent in NT plots, while hairy buttercup was dominant in both NT and CT plots. In the 2023–2024 season, weed canopy cover increased in NT plots at all stages compared to CT plots, with a notable rise in the emergence of common vetch and hairy buttercup. These differences in weed cover across tillage systems suggest that weed growth and emergence responded to variations in soil and environmental conditions. The presence of mayweed, a highly competitive weed species [41], and hairy buttercup, a toxic weed species [42], could pose threats to the growth and development of crops, while the increased presence of common vetch and clover (nitrogen fixing agents) could be beneficial to the fertility of the soil [10].
The key difference between conventional tillage and no-till practices likely played a relevant role in weed emergence and canopy cover dynamics. In conventional tillage, the process of soil disturbance buries weed seeds deeper into the soil, reducing their chances of germinating and emerging on the surface [14]. Additionally, tillage can physically disrupt weed root systems, making it harder for them to establish and compete with crops, which helps explain the reduced weed cover in the CT plots [43]. However, despite these practices, in the first growing season, weed canopy cover was similar between the CT and NT plots. This could be attributed to several factors, including the application of herbicides, which may have minimized the impact of tillage on weed suppression. In contrast, no-till practices leave the soil largely undisturbed, allowing weed seeds to remain closer to the surface where they can more easily germinate and establish [44,45]. This may lead to greater weed emergence and canopy cover, as seen with the NT plots in this study. The increasing presence of common vetch and clover can largely be attributed to their adaptation to low nitrogen levels in the soil, making low nitrogen availability a major factor in their proliferation [4,46].
Additionally, environmental factors such as weather, climate, and soil conditions could have contributed to the variation in weed canopy. Kumar et al. [47] emphasized that key climate variables like sunlight, temperature, and moisture levels can influence the growth patterns of weeds, as well as their competitive interactions with crops. In 2023, moderate temperatures combined with relatively low rainfall during early spring (Table 1) likely contributed to slower, more controlled weed emergence, as noted in the fields. These conditions may have also limited weed germination and early growth. However, in 2024, the season began with cooler temperatures but quickly transitioned to a warmer, wetter climate as spring progressed. This shift likely created more favorable conditions for weed growth, particularly in no-till plots, where the absence of soil disturbance can promote the establishment of certain weed species. The increased moisture and higher temperatures in 2024 likely drove the more aggressive weed emergence and faster growth rates, leading to a greater weed canopy cover.
The results of the Mann–Whitney U-test reveal contrasting effects of the tillage systems employed. In the 2022–2023 season, the lack of a significant difference in weed distribution between conventional tillage and no-till plots could be attributed to several factors. One possible explanation is the adaptability of weed species, enabling them to thrive in both tillage systems despite differences in soil disturbance. In the 2022–2023 season, the lack of a significant difference in weed distribution between conventional tillage and no-till plots could be due to several factors. One explanation is the adaptability of weed species, which may allow them to thrive in both tillage systems despite differences in soil disturbance [48]. Additionally, factors such as soil moisture retention, temperature fluctuations, and weed seed dispersal patterns may have contributed to similar weed levels across the plots [49,50]. However, in the 2023–2024 growing season and for the overall study period, a significant difference in weed distribution was observed, indicating that the tillage method had a more pronounced impact on weeds. This suggests that while weed control strategies such as herbicide application may initially mask differences between tillage systems, the long-term effects of tillage practices on weed populations become more apparent over time. These findings suggest that while short-term effects may show little distinction, the long-term influence of tillage practices on weed dynamics becomes more evident, requiring ongoing adjustments in management to sustain effective weed control.
In summary, the study demonstrated that weed canopy cover varied significantly between conventional tillage and no-till plots over two winter wheat growing seasons. This finding has practical implications for agricultural systems where tillage practices are selected for reasons beyond weed control, such as soil conservation or moisture retention. Overall, this study underlined the dynamic nature of weed–crop interactions and the need for comprehensive management approaches to optimize weed control while considering the environmental and operational impacts of different tillage practices. The study only covers two growing seasons, which may not be sufficient to fully capture the long-term effects of tillage practices on weed dynamics, especially considering that weed populations can fluctuate significantly from year to year due to various environmental factors.

5. Conclusions

In conclusion, this study evaluated the effects of conventional tillage (CT) and no-till (NT) practices on weed canopy cover in a winter wheat field over two growing seasons. The results indicated that while both tillage systems exhibited similar weed canopy cover during the 2022–2023 season, significant differences emerged in the subsequent season, with increased weed growth observed in no-till plots. This shift can be attributed to factors such as the adaptability of weed species, soil moisture retention, and changing environmental conditions, particularly in the context of increased rainfall and warmer temperatures. The study highlighted that specific weed species, such as common vetch and hairy buttercup, demonstrated varying responses to tillage methods, emphasizing the need for ongoing management adjustments to sustain effective weed control over time. Additionally, the findings underscore the significance of soil nutrient availability, particularly nitrogen levels, in influencing weed dynamics. Moving forward, future research will focus on the potential benefits of variable-rate application of fertilizers and herbicides to enhance weed management strategies while simultaneously reducing the environmental footprint of agricultural practices.

Author Contributions

Conceptualization, J.N.O.; methodology, J.N.O.; validation, J.N.O.; formal analysis, J.N.O.; resources, J.N.O. and C.E.A.; data curation, J.N.O.; writing—original draft preparation, J.N.O.; writing—review and editing, C.E.A. and S.D.; visualization, J.N.O.; supervision, C.E.A. and S.D.; project administration, C.E.A.; funding acquisition, C.E.A. All authors have read and agreed to the published version of the manuscript.

Funding

This study is funded by the United States Department of Agriculture (USDA)–National Institute of Food and Agriculture (NIFA) through the Agriculture and Food Research Initiative (AFRI) Small- and Medium-Sized Farms program (grant number 2021-69006-33875).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of the study area with an insert of the study field.
Figure 1. Location of the study area with an insert of the study field.
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Figure 2. Schematic illustration of the methodology used for this study.
Figure 2. Schematic illustration of the methodology used for this study.
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Figure 3. No-till and conventional tillage plot layout for winter wheat production.
Figure 3. No-till and conventional tillage plot layout for winter wheat production.
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Figure 4. West-to-east drone flight path for field image acquisition.
Figure 4. West-to-east drone flight path for field image acquisition.
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Figure 5. Classified weed canopy cover map derived from the 2022–2023 tillering growth stage.
Figure 5. Classified weed canopy cover map derived from the 2022–2023 tillering growth stage.
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Figure 6. Classified weed canopy cover map derived from the 2022–2023 jointing growth stage.
Figure 6. Classified weed canopy cover map derived from the 2022–2023 jointing growth stage.
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Figure 7. Classified weed canopy cover map derived from the 2022–2023 booting growth stage.
Figure 7. Classified weed canopy cover map derived from the 2022–2023 booting growth stage.
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Figure 8. Classified weed canopy cover map derived from the 2022–2023 mature growth stage.
Figure 8. Classified weed canopy cover map derived from the 2022–2023 mature growth stage.
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Figure 9. Classified weed canopy cover map derived from the 2023–2024 tillering growth stage.
Figure 9. Classified weed canopy cover map derived from the 2023–2024 tillering growth stage.
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Figure 10. Classified weed canopy cover map derived from the 2023–2024 jointing growth stage.
Figure 10. Classified weed canopy cover map derived from the 2023–2024 jointing growth stage.
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Figure 11. Classified weed canopy cover map derived from the 2023–2024 booting growth stage.
Figure 11. Classified weed canopy cover map derived from the 2023–2024 booting growth stage.
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Figure 12. Classified weed canopy cover map derived from the 2023–2024 mature growth stage.
Figure 12. Classified weed canopy cover map derived from the 2023–2024 mature growth stage.
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Figure 13. Mean canopy cover of weeds for conventional tillage and no-till over the study period. Error bars = standard error of mean (SE).
Figure 13. Mean canopy cover of weeds for conventional tillage and no-till over the study period. Error bars = standard error of mean (SE).
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Table 1. A summary of the temperature and rainfall data for the months of January to May for the two growing seasons.
Table 1. A summary of the temperature and rainfall data for the months of January to May for the two growing seasons.
YearJanuaryFebruaryMarchAprilMay
Temperature (°C)
20238.210.711.415.921.1
20242.610.213.917.622.3
Rainfall (mm)
20234.12.52.83.83.8
20244.33.33.35.86.4
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MDPI and ACS Style

Oppong, J.N.; Akumu, C.E.; Dennis, S. Assessing Weed Canopy Cover in No-Till and Conventional Tillage Plots in Winter Wheat Production Using Drone Data. Agronomy 2024, 14, 2706. https://doi.org/10.3390/agronomy14112706

AMA Style

Oppong JN, Akumu CE, Dennis S. Assessing Weed Canopy Cover in No-Till and Conventional Tillage Plots in Winter Wheat Production Using Drone Data. Agronomy. 2024; 14(11):2706. https://doi.org/10.3390/agronomy14112706

Chicago/Turabian Style

Oppong, Judith N., Clement E. Akumu, and Sam Dennis. 2024. "Assessing Weed Canopy Cover in No-Till and Conventional Tillage Plots in Winter Wheat Production Using Drone Data" Agronomy 14, no. 11: 2706. https://doi.org/10.3390/agronomy14112706

APA Style

Oppong, J. N., Akumu, C. E., & Dennis, S. (2024). Assessing Weed Canopy Cover in No-Till and Conventional Tillage Plots in Winter Wheat Production Using Drone Data. Agronomy, 14(11), 2706. https://doi.org/10.3390/agronomy14112706

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