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Article

The Influence of Planting Speed of a Maize Vacuum Planter on Plant Spacing Variability and Ear Parameters

by
Igor Petrović
1,
Filip Vučajnk
2,
Stanislav Trdan
2,
Rajko Bernik
2 and
Matej Vidrih
2,*
1
Laboratory for Aeronautics AEROL, Faculty of Mechanical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
2
Department of Agronomy, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, Slovenia
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(2), 462; https://doi.org/10.3390/agronomy15020462
Submission received: 16 December 2024 / Revised: 3 February 2025 / Accepted: 11 February 2025 / Published: 13 February 2025
(This article belongs to the Section Precision and Digital Agriculture)
Browse Figures
Versions Notes

Abstract

:
Planting speed has an important impact on plant spacing variability and also grain yield. In a two-year study, the effects of planting speeds of 6, 9, and 12 km/h on maize plant spacing and, consequently, ear parameters were investigated. We wanted to determine whether increasing the planting speed increases the plant spacing parameters and what effects this has on ear parameters and grain yield. In both experimental years, no differences between the three planting speeds were found in terms of mean plant spacing, plant density, the multiple index, and the miss index. However, the standard deviation of reference spacings and precision increased with the increase in planting speed from 6 to 12 km/h. In 2022, the differences between plant spacings measured using UAV photogrammetry and manual measurements were smaller (<1 cm) than in 2023. The plant spacing data obtained from 3D point clouds show a strong correlation (r = 0.97) with the manual measurements for all three planting speeds. The proposed method is suitable for measuring plant spacing in maize. In 2022, no differences appeared in grain yield and ear parameters between the planting speeds; however, in 2023, the grain yield and kernel mass per ear were greater at planting speeds of 6 and 9 km/h than at a planting speed of 12 km/h in 2023. Individual ear analysis in 2023 showed an increase of 0.73 g in kernel mass per plant with a 1 cm increase in plant spacing, resulting in a 58 kg/ha yield increase.

1. Introduction

Maize (Zea mays L.), also known as corn, is the world’s leading cereal crop in terms of production [1]. A significant reduction in the average yield of maize can occur when the planting parameters are not properly adjusted, such as the planting speed, the air pressure and plant distance mechanism of the planter, the selection mechanism of the seeding plate, etc. Not only weather events, such as extreme rainfall or drought during planting, but also the seeding rate/seed spacing, affect yields [2]. Cortez et al. (2020) [2] also pointed out that planting is one of the most important management practices associated with maize yield and must be carried out with high quality and precision. Various factors, such as the seed metering unit, furrow opener, tractor forward speed, seed quality, and soil conditions, influence seed distribution in the soil. However, producers have trended toward higher plant spacing uniformity and faster planting speeds [3].
Kuş, 2021 [3], compared the influence of three planting speeds (4.0, 5.4, and 7.9 km/h) on planter performance and found that the lowest planting speed of 4.0 km/h resulted in the lowest miss, multiple, and precision indexes. In addition, the planting speed can affect the ability of a planter to uniformly separate and place individual seeds in a furrow. Nadin et al. (2019) [4] reported that increasing the driving speed of the planter reduces the uniformity of the longitudinal distribution of the plants as well as the planting quality. The planting speeds in their trials were 4, 6, 8, and 10 km/h, and a vacuum maize planter was utilized. An increase in planting speeds on one side linearly reduced the percentage of acceptable spacings and plant density; on the other hand, it linearly increased the mean distance between plants, the coefficient of variation in the distance between plants, the percentage of double and miss spacings, and the precision index [4]. Planting speeds over 7.5 km/h can cause variations in plant spacings due to the trajectory of the seed (from its liberation from the seeding plate until reaching the soil) because of the rolling and ricocheting of the seed when coming into contact with the soil [4].
In addition, Staggenborg et al. (2004) [5] reported that maize yield decreased with increasing planting speed at one site but not at the others. To overcome the problems in planting, a monitoring system was developed to improve the economy and efficiency of planting [6]. The differences between plant density and the distribution of plants in the row are crucial factors in understanding the plant-to-plant yield variability of maize. The differences in grain yield within a maize field may be due to variability in plant spacing [7,8,9]. Uniform seed distribution in the row can decrease competition between plants for existing moisture, light, and nutrients. The quality of planting is a decisive factor for maize yield [10,11]. Double seed spacings (less than or equal to half of the theoretical spacing) enacted by the planter increased yield by 6%, while empty seed spacings (greater than 1.5 times the theoretical spacing) decreased yield by 7% [12]. A standard deviation of plant spacing of more than 6 cm decreased maize yield [13]. The increase in planter speed from 1.8 to 3.1 m s−1 resulted in a yield loss of 78 kg per hectare [14]. Increasing spatial variation among plants in the row reduced maize yields in some experiments [15,16,17,18,19,20], while others reported no effect on yield [21,22,23,24]. Egli, 2022 [25], reported that yield loss from non-uniform spacings can also be reduced by increasing plant density above the minimum level needed to maximize yield. The higher plant density allows the dominant plants (larger spacing and more area and solar radiation per plant) in the population to compensate for the loss of kernels and yield of the dominated plants (smaller spacing and less area and solar radiation per plant) [25].
In our research, plant spacing measurements in maize were evaluated using unmanned aerial vehicles (UAVs). This enables a large collection of data in agricultural fields without the traditionally required manual labor [26,27]. Images captured by the UAVs are post-processed using photogrammetry, either automatically or manually [28,29,30,31,32]. Aerial photogrammetry can be used for remote measurements in various fields, e.g., to assess wheat biomass and harvest index [33] and to estimate vine canopy dimensions [34,35]; in addition, it is well suited for measuring plant spacing in maize [36,37], being used to estimate the LAI of maize [38], together with canopy height [39] and green canopy cover [40]. Kostić et al. (2024) [41] showed that UAV-captured images are valuable in analyzing the uniformity of maize plant spacing with different plant recognition algorithms.
Due to different reports of the influence of planting speed on plant spacing and yields, the field research was carried out using three planting speeds. The aim of our research was to determine the influence of the planting speed of a modern vacuum planter on plant spacing accuracy, grain yield, and ear parameters and compare manual and UAV measurements of plant spacing.

2. Materials and Methods

2.1. Field Trial

The field trial was carried out in Srednji Globodol, Slovenia (45°51′06.3″ N 15°02′52.6″ E), on medium–heavy soil in the growing season of 2022 and 2023. The soil texture class was silty loam, and the soil contained 3.4% organic matter. The experimental design consisted of randomized blocks with three repetitions. Three treatments corresponding to three planting speeds of 6, 9, and 12 km/h were tested in the trial. The single plot unit was 4.5 m wide and 16.7 m long and included 6 plant rows with 0.75 m of inter-row width. The remaining 2 blocks were located next to the block shown in Figure 1.
The cumulative rainfall in July and August 2022 was 212 mm less than in 2023 and 92 mm less than the 1991–2020 average. September 2023 was very wet, with 126 mm more rain than the long-term average. June 2023 was dry, with 44 mm less precipitation than the 1991–2020 average, while the cumulative precipitation in July and August 2023 was 120 mm above the long-term average. In both trial years, the average air temperature from May to October was higher than the 1991–2020 average, with the greatest difference being 3.2 °C in June 2023 and 3.7 °C in October 2023 (Table 1).
Within the experimental field, both in the years 2022 and 2023, a survey area of 20 m × 16 m within block 1 (Figure 1) was selected for the digital reconstruction of the maize field. For this purpose, the RGB images captured by the UAV were used and processed with commercial photogrammetric software to reconstruct a 3D point cloud. A series of ground control points (GCPs) and manual tie points (MTPs) were used for better accuracy and alignment of the point cloud. The misplacement of the point clouds generated from the images obtained from the UAV was expected from previous experience [34] since different camera angles and heights were used in the flight missions, along with changing wind and illumination.

2.2. Vacuum Planter

A hybrid maize breed (P9537) from Corteva (Corteva, Inc., Indianapolis, IN, USA) was planted using a 6-row vacuum maize planter Amazone ED 4500-2 Special (Amazonen-Werke, Hasbergen, Germany) and a Fendt 516 Vario tractor (AGCO-Fendt, Marktoberdorf, Germany) with 121 kW of nominal power. The seeds were calibrated, and the seed size was 4191 seeds/kg. The row width was 75 cm. The planter is equipped with markers, a fertilizer tank, and an ISOBUS connection, which enables the machine to be controlled. It works on the principle of sucked air, i.e., the seeds are drawn into the holes by negative pressure. The division of the seeds takes place mechanically via a scraper, which, in turn, enables a low height and the free fall of the seeds. The adjusted theoretical plant spacing was 16.7 cm, which corresponds to the planting density of 80,000 plants per hectare. The planter seeding rate was adjusted according to the set plant spacing and planting speed. The planting speed was set according to the tractor cruise control and controlled via the tractor radar. The tractor speed did not vary in the same run. The trial plot was plowed in the fall with a four-share reversible plow and cultivated with a rotary harrow in a single pass shortly before planting. Planting took place on 30 April 2022 and 1 May 2023.

2.3. Measurement of Plant Spacing

The plant spacings were measured in the BBCH 13 growth phase (3 unfolded leaves) on 24 May 2022, and the BBCH 15 growth phase (5 unfolded leaves) on 9 June 2023. Initially, manual plant spacing measurements were carried out using a hand metal tape meter with a 1 mm division. The measurements were obtained within a 16.7 m long plot unit (100× theoretical plant spacing) between the stakes in both trial years 2022 and 2023. After the manual measurements, the UAV flight was carried out, and the plant spacing measurements were calculated in post-processing.
The UAV images of the maize plants in the row were taken on the same day as the manual measurements. A DJI Phantom 4 Pro (SZ DJI Technology Co., Ltd., Shenzhen, China) with a camera resolution of 5472 × 3078 pixels was used in 2022, and a DJI Mavic 3M (SZ DJI Technology Co., Ltd., Shenzhen, China) with an RGB camera resolution of 5472 × 3468 pixels was used in 2023 to collect the data for photogrammetry. Different UAVs were used, as the Phantom was not available in 2023. Due to the similar resolution, the same flight parameters were used to obtain a similar ground sampling distance (GSD). At the beginning of the measurements, 6 stationary ground control points (GCPs) and 4 manual tie points were set to align and position the points in the post-processed point cloud. The size of the GCPs and MTPs was 0.20 m × 0.29 m. The absolute coordinates of the GCPs were measured using a dual-frequency RTK differential GNSS, with the stationary base station module positioned close to the experimental field to achieve an accuracy of ±0.02 m, and the rover was used to measure the relative position of the GCPs.
The flight missions of the Phantom were planned using the application Pix4Dcapture (Pix4D SA, Prilly, Switzerland). To provide a high-quality dataset for the 3D point cloud, 3 flight missions over the experimental field on the same day were planned in order to take pictures from different camera angles. The missions were planned using a double-grid flight over the survey area. All 3 missions were performed at the lowest altitude of 10 m above the starting point, which was at the same altitude as the experimental field, resulting in an average GSD of 0.27 cm/pixel. The frontal overlap of the images was set to 90%, and the lateral overlap to 81%. The camera angles were changed for each mission and were 80°, 65°, and 50° (measured from the horizon). The total flight time of the missions was 53 min, and 2360 images were taken. Due to the unavailability of the Phantom, the Mavic 3M was used for the flight missions in 2023. The flight missions over the experimental field were planned with the DJI Pilot 2 app (SZ DJI Technology Co., Ltd., Shenzhen, China) using the mapping mission type and an RGB and multispectral camera. The flight altitude for 3 flight missions was set at 12 m above the ground, resulting in an average GSD of 0.33 cm/pixel. All missions had an 80% frontal and 80% lateral overlap. The camera angles were changed for each mission and were 90°, 80°, and 74°. The total flight time of the missions was 36 min, and 923 RGB images were captured.
The georeferenced images obtained from the UAV mission were imported into Pix4Dmapper version 4.8.4 (Pix4D SA, Prilly, Switzerland). In addition to the GCPs with measured coordinates and MTPs shown in Figure 1, additional MTPs were added to better align the point clouds from different flight missions that did not match during automatic post-processing. After recalibrating the cameras from the consecutive missions, a single matching point cloud of a survey area for each year was created with the full resolution of all the images, which were taken from different camera angles.
In the generated point clouds in Pix4Dmapper, the centers of the maize plants to be measured were determined. The beginning of the row was marked with a stake, which was first identified and manually marked in the point cloud. Afterward, the next individual plant in the direction toward the stake at the end of the row was marked. By marking a plant that was captured in several images from different angles, a new point was added to the point cloud with better positional accuracy. Oblique images taken from different camera angles provided the possibility of correctly marking the center of the maize stalk, regardless of the growth stage. This was repeated for all measured points within the point cloud. The point coordinates determined through manual marks of the plant center point were then exported, and the distances were calculated as the projection of the length of the line connecting adjacent plants. The row length where the plant spacings were measured was 16.7 m. For plant distance determination from the UAV images, which were post-processed into the point clouds, the first 10 plants from the start of the row and the last 10 plants at the end of the row were taken. These measurements (n = 360) were carried out only in block 1 in three repetitions (3rd row, 4th row, and 5th row) for each planting speed (Figure 1) since data processing was very time-consuming. Both measurements (manual and UAV-based) were carried out for both trial years 2022 and 2023. A plastic stake was placed at the beginning and end of the row, as shown in Figure 1. This was a reference point from which the distances between the maize plants were measured manually using a hand-held measuring tape and digitally using the point cloud. Both plant spacing measurements (manual and digital) were taken from the same plants for later comparison.
The mean distance, plant density, miss index, multiple index, feed index, minimum and maximum distance, standard deviation for all distances (SDall), coefficient of variation (CV), precision, and standard deviation for the reference distances (SDref) were calculated from the manual plant distance measurements according to [43]. The miss index, multiple index, and feed index are calculated using the theoretical plant spacing of 16.7 cm. This is the targeted distance between plants, assuming no skips, no multiples, and no variability between spacings. The multiple index is the percentage of all spacings that are less than or equal to half the theoretical spacing of 16.7 cm. The miss index is the percentage of all spacings that are greater than 1.5 times the theoretical spacing of 16.7 cm. The feed index is the percentage of all spacings that are more than half but no more than 1.5 times the theoretical spacing of 16.7 cm. SDall is the standard deviation calculated from all plant spacings, including the miss and multiple indexes. SDref is the standard deviation calculated from plant spacings that are more than half but no more than 1.5 times the theoretical spacing. Precision is a measure of variability in plant spacing range, from 0.5 to 1.5 times the theoretical distance; it is calculated by dividing SDref and the theoretical spacing of 16.7 cm and is similar to the coefficient of variation for the spacings between 0.5 and 1.5 times the theoretical plant spacing of 16.7 cm. It is a measure of variability in plant spacing after removing the variability due to the miss index and multiple index. These 10 metrics were derived from the manually measured distances between plants.

2.4. Grain Yield and Ear Analysis

When the maize cobs were fully mature, we hand-picked all the cobs in the same treated rows, where we measured the distance between plants in the row. These were dried in a dryer at 40 °C to achieve grain moisture below 14%. The percentage of moisture in the grain was measured using a Pfeuffer HE 50 (Pffeufer, Kitzingen, Germany) moisture meter. From each individual treatment, 10 ears were randomly selected and subjected to manual ear analysis in 2022. The ear analysis contained kernel mass per ear, the cob mass, the kernel number per ear, and the mass of 1 kernel. From the data on grain mass and grain moisture, the yield at 14% grain moisture was calculated. In 2023, all harvested ears from each treatment were analyzed to obtain more accurate data on kernel mass per ear and plant spacing.

2.5. Statistics

Statistical analysis was carried out according to the procedure for random blocks. The analysis of variance and Duncan’s test for multiple comparisons (p < 0.05) were calculated. STATGRAPHICS® Centurion (v. 17) software (Statpoint Technologies, Inc., Warrenton, VA, USA) was used for data analysis. If the results from the statistical analysis showed significant differences between the treatment data, the results in the tables were presented with different letters. Since three treatments (6, 9, and 12 km/h) were analyzed, 3 different letters (a, b, and c) were used. The relationships between the different variables were represented using linear regression models. An analysis of variance was performed for the regression model. The equations for the fitted models are presented, as well as the coefficient of determination (R2), the correlation coefficient (r), and the p-value.

3. Results

3.1. Plant Spacing

Planting speed did not have a significant effect on mean distance, plant density, multiple index, miss index, feed index, and minimum distance (Table 2). According to Schrödl, 1993 [44], and Schuchmann, 2020 [45], the multiple index is high when between 5 and 7.5%, which was the case in our study at a planting speed of 12 km/h in 2023. Otherwise, the multiple index was between 1.7 and 4.5%, which is considered low to acceptable. A similar situation was observed for the miss index, which reached high values (>5%) only at a planting speed of 12 km/h in both trial years. In 2022, the differences occurred in the maximum distance between planting speeds. At the planting speed of 12 km/h, the maximum distance was higher than at planting speeds of 6 and 9 km/h.
No significant differences were found in the standard deviation for all spacings and in the coefficient of variation between the different planting speeds (Table 3). To exclude the effect of outlying distances, SDref ( 0.5 theoretical distance to 1.5   theoretical distance) and precision were calculated, as they are based on the theoretical distance between plants and only measure the degradation of planter performance within the target range. In both experimental years, the standard deviation of reference spacing and precision were significantly affected by planting speed. The highest precision value was achieved at 12 km/h in both trial years but was still below the practical upper limit of 29% [43].
The relationship between precision and planting speed was calculated using a linear regression model (Figure 2).
Equation (1) of the fitted model is
Precision = 5.5 + 1.47 × planting speed
R2 = 84.8%; r = 0.92; p < 0.001
There is a statistically significant relationship between precision and planting speed at the 95.0% confidence level. The R-squared statistic indicates that the fitted model explains 84.8% of the variability in precision. The correlation coefficient is 0.92, indicating a relatively strong relationship between the variables. If the speed increases by 1 km/h, the precision increases by 1.47%.

3.2. Comparison of Hand and UAV Measurements of Plant Spacing

The absolute mean difference in plant spacing between the hand and UAV measurements was between 0.5 and 1.3 cm, and planting speed had no significant effect on it. A non-significant influence of planting speed was also evident in the absolute maximum and minimum differences (Table 4). All differences between the hand and UAV measurements of plant spacings are relatively small.
A linear regression model was developed to compare the hand and UAV measurements of plant spacing (Figure 3).
Equation (2) of the fitted model is
UAV plant spacing = 1.95 + 0.90 × manual plant spacing
R2 = 93.8%; r = 0.97; p < 0.001
There is a statistically significant relationship between UAV plant spacing and manual plant spacing at the 95.0% confidence level. The model, as fitted, explains 93.8% of the variability of UAV plant spacing. The correlation coefficient is 0.97, indicating a relatively strong relationship between the variables.

3.3. Grain Yield and Ear Parameters

The impact of planting speed on the grain yield and ear parameters—which are crucial for maize production—was analyzed (Table 5). In the year 2022, planting speed did not significantly affect grain yield. One year later, the grain yield at a planting speed of 12 km/h was significantly lower than at planting speeds of 6 and 9 km/h. In 2022, planting speed did not influence kernel mass per ear. In 2023, however, a higher kernel mass per ear was observed at planting speeds of 6 and 9 km/h compared to 12 km/h. In 2023, the cob mass was higher at a planting speed of 6 km/h than at a planting speed of 12 km/h. In both trial years, no significant differences were found in kernel number per ear between three planting speeds. In 2023, with the increase in planting speed, the mass of 1 kernel decreased, whereas in 2022, no differences appeared between planting speeds for this parameter.
The plant spacings were divided into three ranges: from 0 to 8.3 cm (doubles), from 8.3 to 25 cm (singles), and from 25 to 41.7 cm (miss) to clearly present the influence of planting speed on kernel mass per ear. In the plant spacing range from 8.3 to 25 cm, the kernel mass per ear was lower at a planting speed of 12 km/h compared to 6 and 9 km/h (Table 6). In the other two plant spacing ranges (0–8.3 cm and 25–41.7 cm), no significant effect of planting speed on kernel mass per ear was noticed.
The influence of plant spacing range on the kernel mass per ear is presented by calculating the kernel mass per plant regardless of planting speed. At the plant spacing range of 0–8.3 cm, kernel mass per plant was lower when compared to plant spacing ranges of 8.3–25 cm and 25–57 cm (Table 7). The highest kernel mass per plant was at the plant spacing range of 25–57 cm when compared to the other two plant spacing ranges. Kernel mass per plant at a plant spacing range of 8.3–25 cm represented 100%, and at plant spacing ranges of 0–8.3 cm and 25–57 cm, it represented 94.4% and 105.8%.
The relationship between kernel (grain) mass per plant and plant spacing was calculated using a linear regression model (Figure 4).
Equation (3) of the fitted model is
Kernel mass per plant (g) = 122.7 + 0.73 × plant spacing (cm)
R2 = 0.87%; r = 0.093; p = 0.0056
There is a statistically significant relationship between kernel mass per plant (g) and plant spacing (cm) at the 95.0% confidence level. The fitted model explains 0.87% of the variability in kernel mass per plant. The correlation coefficient equals 0.093, indicating a relatively weak relationship between the variables. At 95% confidence, it is expected that at a plant spacing of 5 cm (double space), the average kernel mass per plant will be in the interval from 119.3 to 133.4 g. At a plant spacing of 17 cm (target spacing), the average kernel mass per plant will be in the interval between 132.1 and 138.2 g, and at a plant spacing of 37 cm (empty space), the average kernel mass per plant will be between 139.1 and 160.5 g at 95% confidence.

4. Discussion

Increased planting speed did not influence mean distance, plant density, multiple index, miss index, or feed index, which contradicts the findings of Staggenborg et al. (2004) [5], who reported worse plant arrangement in rows with increasing planting speed. The same author reported that special fingers on the planter prevented the grains from bouncing in the row, reducing the standard deviation and improving the arrangement of the plants in the row. One possible reason for a high multiple index, especially at a planting speed of 12 km/h, is that the seed selection mechanism may not have been able to remove excess seeds on the seeding plate, resulting in a higher multiple index. On the other hand, it is possible that some seeds did not stick to the seeding plate, resulting in larger distances between seeds in the row and a higher miss index at the 12 km/h planting speed. All of this could be related to the size and shape of the maize seed. In addition, the seeds can roll along the furrow, which can increase the SDall. The feed index is a measure of how often the plant spacings were close to the theoretical distance of 16.7 cm. Schuchmann, 2020 [45], found a feed index of higher than 93% for planting speeds of 6, 9, and 12 km/h, but the planter type was different from that in our study. In our study, the feed index did not exceed 90% at a planting speed of 12 km/h.
The SDall was higher than 4.2 cm for all three planting speeds, which, according to [45], is too high and is strongly influenced by some very large spacings [43], which was also confirmed in our study. Our results show that planting speed had no influence on the SDall and coefficient of variation. Nielsen, 2001 [17], reported that the practical limit for improving grain yield is an SDall of less than 5.1 cm, which was the case at 6 and 9 km/h in our study. The standard deviation for all distances (SDall) is not the most important information, as it depends on the squared deviation from the mean and is strongly influenced by large distances between plants. That is why we only analyzed distances in the targeted area (0.5 to 1.5 times the theoretical distance) and calculated SDref and precision. It was found that as planting speed increased, the SDref and precision also increased, which shows the importance of planting speed and its influence on plant spacing variability within the row. Staggenborg et al. (2004) [5] also reported increasing variability in plant spacing with increasing planting speed. The SDref was below 2.5 cm in both experimental years at the 6 km/h planting speed, which is very good according to [45]. When considering all plant spacings, particularly large distances between plants had a large effect on SDall and CV. This is also evident in the minimum and maximum distances between plants, with no differences between the different planting speeds. In addition, a completely positive and strong correlation (r = 0.92) was found between precision and planting speed, showing that an increase in planting speed of 1 km/h increases precision by 1.47%. At planting speeds of 6 and 9 km/h, the SDref did not exceed 4 cm, which is satisfactory, as mentioned in [44,45]. The permissible limit for SDref is 4 cm, and for precision, it is 29% [43,44]. By taking this into account, the planting speed of 12 km/h was the highest possible using this planter. There is very little scope for an even higher planting speed above 12 km/h using this planter with regard to the permissible level for SDref (4 cm), which was already exceeded in 2023.
The UAV measurements of planting spacings were taken at growth stage BBCH 15 (five unfolded leaves) in 2023 and at growth stage BBCH 13 (three unfolded leaves) in 2022. This is the reason for the larger absolute mean difference between the UAV and manual measurements in 2023 when compared to 2022, as a higher number of leaves means more obstacles in the 3D point cloud model, and it is more difficult to determine the correct plant distance. In our opinion, it is better to perform measurements based on photogrammetry at earlier growth stages from BBCH 11 to BBCH 13, while the leaves at later growth stages can interrupt the measurements. The same reason as above is also relevant for the absolute maximum difference between the UAV and manual measurements. Li et al. (2022) [39] reported on photogrammetry-based 3D imaging approaches and techniques and pointed out that the depth and quality of the point cloud, which is influenced by the detection area, determines the accuracy of the measurement of the crop size, such as height, width, canopy volume, leaf area index, etc. Similarly, Zhang et al. (2018) [37] found that UAV images taken later in the growing season are problematic because plant leaves overlap and the exact individual plant centers cannot be determined, which causes errors.
A very strong correlation between the UAV and manual measurements was obtained (r = 0.97), indicating the ability of UAV-based photogrammetry to calculate plant distance. Zhang et al. (2018) [37] calculated plant distance in maize using a UAV to capture images with an RGB camera. They found that the accuracy of measuring the distance between maize plants depended on the height above the ground at which the UAV images were captured. With the exception of the scenario with a flight altitude of 1 m, a relative error of about 10% was automatically achieved based on some predefined parameters (the distance between maize rows and the height of maize plants). The manual determination of the center of the maize plants is more time-consuming, but accuracy can be significantly improved; this gives importance to our study and provides a good starting point for further research.
The images taken using the UAV and the photogrammetric 3D point cloud allow the data to be rechecked and possible corrections to the manual measurements to be made. The different viewports and zoom levels enable an accurate view of the details if the flight mission was set up correctly. Here, the difference between the accuracy in 2022 and 2023 could be influenced not only by the later growth stage but also by the smaller differences in camera angles in 2023 (90°, 80°, and 74°) compared to those in 2022 (80°, 65°, and 50°). The larger errors resulting from camera angles closer to the nadir view also support the results of Dai et al. (2023) [46] and Che et al. (2020) [38]. A lower oblique view (i.e., a camera angle of 50°) provides a better view and determination of the plant centers but requires a longer flight path. Although the manual determination of the plant centers in the point cloud improves accuracy, it is very time-consuming, and it is impossible to use this on all plants in the field. However, the accuracy of the method used in this study would allow the results to be used as validation for a convolution-based deep learning algorithm that could automatically mark the centers of the plants after they have been segmented from the images and measured using the photogrammetric methods. The use of the automatic algorithms would be limited at certain growing stages, e.g., where the leaves of the adjacent plants would overleap.
In 2022, planting speed did not influence the ear parameters and grain yield, which were very low (5867 to 6527 kg/ha) due to low precipitation in July and August, resulting in poor grain filling. In that year, less rain (more than 200 mm) fell than in 2023, when the grain yield was almost twice as high and was influenced by planting speed. In addition, it was 2.1 °C, 1.0 °C, and 1.2 °C warmer in June, July, and August 2022, respectively, than in the same months in 2023. It is possible that the 2022 maize yield data weaken the relationship since the limitation in rainfall causes other interactions to be totally overshadowed by this factor, which is the main one. The higher grain yield at a planting speed of 6 and 9 km/h compared to the 12 km/h planting speed in 2023 could be due to the better weather conditions in that year, which allowed the plant spacing parameters influenced by planting speed to become decisive. Firstly, the plant density at a planting speed of 12 km/h was 2000 plants/ha lower in 2023 than in the previous year, which could have an impact on the lower grain yield, even if it was not significant. Overall, the combination of lower plant density with plant spacing variability in the targeted area and weather conditions were the main factors for the lower yield at the 12 km/h planting speed compared to 6 km/h and 9 km/h in 2023. The lower grain yield in 2023 at a planting speed of 12 km/h was also expressed as a lower kernel mass per ear than at a planting speed of 6 and 9 km/h, which was expected. In general, almost all ear parameters were higher in 2023 than in 2022, which can be attributed to the better weather conditions in 2023 during the growing season. In contrast to our results, Thompson, 2013 [47] reported increased kernel mass per ear with reduced plant spacing variability in the year with stress growing conditions. However, in our study, an increase in kernel mass per ear was found at the planting speeds of 6 and 9 km/h compared to 12 km/h in 2023, a year with good growing conditions. Nielsen, 1995 [14], reported a decrease in grain yield of about 156 kg/ha for every 2.5 cm increase in the standard deviation of plant spacing. Similarly, La Barge and Thomison, 2001 [48], found yield losses ranging between 69 and 183 kg/ha for each kilometer per hour increase due to increased plant spacing variations. At the plant spacing range of 25–57 cm, the kernel mass per plant was the highest (105.8%), which corresponds with the results of Doerge et al. (2015) [10]. It seems that a decrease in kernel mass per plant from doubles is about the same percentage increase in kernel mass per plant from skips. Thus, the overall effect on the kernel mass per plant of doubles and skips is nullified. Generally, two plants growing together as doubles generate (together) more kernel mass for the same area as one plant, which is considered a skip, even though one plant is missing. This is extremely dependent on the growth habit of the maize hybrid used. There are some that are more prolific and can compensate more for longer distances when plants are missing, but in the field of maize crop physiology, plants planted together have totally suboptimal behavior compared to individual plants, as they compete for resources among themselves, thus canceling out each other’s yield. In that manner, our results only partially correspond to the results of Doerge et al. (2015) [10], who stated that the miss index (skips) caused the greatest yield loss while the multiple index (doubles) slightly increased grain yield. A regression model of the relationship between plant spacing and kernel mass per plant shows that for each cm of increase in plant spacings, the kernel mass per plant will increase by 0.73 g, which is very little. This means an increase in grain yield of only 58 kg/ha at a plant density of 80,000 plants per hectare. According to this regression model, plants in the distance range of <½ of the referenced distance (doubles) show lower yield potential compared to plants in the distance range >1 ½ of the referenced distance (skips), but these distances alternate within the row, which can negate this effect.
Many plant spacing studies used a highly artificial grouping of plantings to achieve certain levels of plant spacing variability, while others used overplanting and thinning, which exhibits less favorable crop architecture and lower grain yield potential. Our trial was conducted using a vacuum planter regularly employed in real conditions, without the aforementioned procedures, which adds significance to our study.

5. Conclusions

Our results indicate that the plant spacing variability of the targeted plant spacing increases with the increase in planting speed from 6 km/h to 12 km/h when using a modern vacuum planter. In years with worse weather conditions, such as 2022, the planting speed had no influence on the grain yield or ear parameters. However, in years with good weather conditions in the growing season, especially in the summer months, a lower variability in plant spacing at 6 km/h and 9 km/h and higher plant density can lead to a higher grain yield compared to a higher planting speed of 12 km/h. Comparisons between manual field measurements and those made by manual marking in the point cloud obtained by photogrammetry (digital) show a strong correlation. It is better to apply the digital method before BBCH 13 to avoid problems with overlapping leaves, and while it can also be used at later growth stages, the process is time-consuming. This method is suitable for the validation of plant spacing measurements in the maize field.

Author Contributions

Conceptualization, F.V., I.P. and M.V.; methodology, F.V. and I.P.; software, I.P. and R.B.; validation, S.T. and M.V.; formal analysis, F.V.; investigation, F.V., I.P. and M.V.; resources, I.P., R.B., S.T. and M.V.; data curation, F.V.; writing—original draft preparation, F.V., I.P. and M.V.; writing—review and editing, M.V., I.P. and F.V.; visualization, F.V., M.V. and I.P.; supervision, M.V., I.P. and S.T.; project administration, M.V.; funding acquisition, M.V., I.P. and S.T. All authors have read and agreed to the published version of the manuscript.

Funding

The authors would like to thank Slovenian Research Agency ARIS for their financial contribution to this work. This work was financed in part by the research program Animal Health, Environment and Food Safety, P4-0092, and Mechanics in Engineering, P2-0263.

Data Availability Statement

Point clouds from this study are available on https://doi.org/10.5281/zenodo.14640576 (accessed on 15 December 2024).

Acknowledgments

We are grateful to Primož Berus for his work with agricultural machinery during the trial.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Agronomy 15 00462 g001
Figure 1. UAV survey area within block 1, where both UAV and manual measurements were performed in the year 2023. The stakes, GCPs, and MTPs are visible and were set in block 1 for data comparison. The row numbers are marked, along with the treatment planting speed within the single plot unit.
Figure 1. UAV survey area within block 1, where both UAV and manual measurements were performed in the year 2023. The stakes, GCPs, and MTPs are visible and were set in block 1 for data comparison. The row numbers are marked, along with the treatment planting speed within the single plot unit.
Agronomy 15 00462 g001
Agronomy 15 00462 g002
Figure 2. Linear regression model for the relationship between precision and planting speed (of the vacuum planter), with 95% confidence intervals for the mean prediction.
Figure 2. Linear regression model for the relationship between precision and planting speed (of the vacuum planter), with 95% confidence intervals for the mean prediction.
Agronomy 15 00462 g002
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Figure 3. Linear regression model for the relationship between UAV plant spacing and manual plant spacing, with 95% confidence intervals for the mean prediction.
Figure 3. Linear regression model for the relationship between UAV plant spacing and manual plant spacing, with 95% confidence intervals for the mean prediction.
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Agronomy 15 00462 g004
Figure 4. Linear regression model of the relationship between kernel mass per plant (g) and plant spacing (cm).
Figure 4. Linear regression model of the relationship between kernel mass per plant (g) and plant spacing (cm).
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Table 1. Precipitation and air temperature for 2022 and 2023 compared to the long-term average of 1991–2020 for Novo Mesto, Slovenia [42].
Table 1. Precipitation and air temperature for 2022 and 2023 compared to the long-term average of 1991–2020 for Novo Mesto, Slovenia [42].
MonthPrecipitation (mm)Air Temperature (°C)
202220231991–2020202220231991–2020
April96928310.19.811.1
May12914210318.015.615.6
June1416110522.720.619.5
July651539923.322.321.2
August5417811222.221.020.5
September2624713615.918.315.4
October3311612013.314.510.8
Sum780789758
Table 2. Mean distance (cm), plant density (plants/ha), multiple index (%), miss index (%), feed index (%), and minimum and maximum distance (cm) at three planting speeds of vacuum planter.
Table 2. Mean distance (cm), plant density (plants/ha), multiple index (%), miss index (%), feed index (%), and minimum and maximum distance (cm) at three planting speeds of vacuum planter.
YearPlanting Speed
(km/h)		Mean
Distance
(cm)		Plant
Density (Plants/ha)		Multiple
Index
(%)		Miss
Index
(%)		Feed Index (%)Minimum
(cm)		Maximum
(cm)
2022616.7 a77,333 a2.7 a3.1 a94.2 a4.0 a29.8 a
917.1 a76,267 a1.4 a3.9 a94.7 a4.7 a30.3 a
1217.0 a77,333 a4.5 a5.8 a89.7 a2.3 a40.7 b
2023616.5 a78,933 a4.4 a2.7 a92.9 a1.5 a28.3 a
917.2 a77,667 a2.3 a4.7 a92.9 a4.0 a33.0 a
1217.5 a75,467 a5.8 a5.7 a88.6 a3.0 a41.5 a
Different letters within the same column and year represent significant differences (Duncan’s test, α = 0.05).
Table 3. The standard deviation all (cm), coefficient of variation (%), standard deviation reference (cm), and precision (%) at planting speeds of 6, 9, and 12 km/h.
Table 3. The standard deviation all (cm), coefficient of variation (%), standard deviation reference (cm), and precision (%) at planting speeds of 6, 9, and 12 km/h.
YearPlanting Speed (km/h)SDall
(cm)		CV
(%)		SDref
(cm)		Precision
(%)
202264.4 a26.3 a2.4 a14.4 a
94.4 a25.5 a3.1 b18.0 b
126.1 a35.8 a3.7 c22.2 c
202364.2 a25.5 a2.3 a13.7 a
95.0 a29.3 a3.3 b20.1 b
126.5 a37.4 a4.1 c24.7 c
Different letters within the same column and year represent significant differences (Duncan’s test, α = 0.05).
Table 4. The absolute mean difference (cm), absolute maximal (cm), and absolute minimal (cm) differences between the hand and UAV measurements of plant spacing at planting speeds of 6, 9, and 12 km/h.
Table 4. The absolute mean difference (cm), absolute maximal (cm), and absolute minimal (cm) differences between the hand and UAV measurements of plant spacing at planting speeds of 6, 9, and 12 km/h.
YearPlanting Speed (km/h)Absolute Mean
Difference (cm)		Absolute Maximal Difference (cm)Absolute Minimal Difference (cm)
202260.61.80
90.51.30
120.71.60
202361.33.40.3
91.22.50.1
121.23.40.2
Table 5. Grain yield (kg/ha) and ear parameters at different planting speeds using a vacuum planter.
Table 5. Grain yield (kg/ha) and ear parameters at different planting speeds using a vacuum planter.
YearPlanting Speed (km/h)Grain Yield (kg/ha)Kernel Mass
per Ear (g)		Cob Mass
(g)		Kernel Number
per Ear (-)		Mass of 1 Kernel (mg)
202265867 a110 a11.2 a418 a270 a
96194 a111 a11.6 a395 a281 a
126527 a118 a11.9 a413 a298 a
2023612,597 a168 a19.4 a552 a309 a
911,234 a145 a16.8 ab538 a270 b
128110 b122 b14.2 b533 a230 c
Different letters within the same column and year represent significant differences (Duncan’s test, α = 0.05).
Table 6. Kernel mass per ear by plant spacing range at different planting speeds in 2023.
Table 6. Kernel mass per ear by plant spacing range at different planting speeds in 2023.
Planting Speed (km/h)Kernel Mass per Ear (g)
0–8.3 cm8.3–25 cm25–41.7 cm
6144.7 a156.9 a124.0 a
9100.9 a143.0 a157.3 a
12100.2 a104.4 b112.4 a
Different letters within the same column and year represent significant differences (Duncan’s test, α = 0.05).
Table 7. Kernel mass per plant by plant spacing range in 2023.
Table 7. Kernel mass per plant by plant spacing range in 2023.
Plant Spacing RangeKernel Mass per Plant (g)Relative (%)
0–8.3 cm127.9 a94.4
8.3–25 cm135.5 b100.0
25–57 cm143.3 c105.8
Different letters within the same column and year represent significant differences (Duncan’s test, α = 0.05).
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Petrović, I.; Vučajnk, F.; Trdan, S.; Bernik, R.; Vidrih, M. The Influence of Planting Speed of a Maize Vacuum Planter on Plant Spacing Variability and Ear Parameters. Agronomy 2025, 15, 462. https://doi.org/10.3390/agronomy15020462

AMA Style

Petrović I, Vučajnk F, Trdan S, Bernik R, Vidrih M. The Influence of Planting Speed of a Maize Vacuum Planter on Plant Spacing Variability and Ear Parameters. Agronomy. 2025; 15(2):462. https://doi.org/10.3390/agronomy15020462

Chicago/Turabian Style

Petrović, Igor, Filip Vučajnk, Stanislav Trdan, Rajko Bernik, and Matej Vidrih. 2025. "The Influence of Planting Speed of a Maize Vacuum Planter on Plant Spacing Variability and Ear Parameters" Agronomy 15, no. 2: 462. https://doi.org/10.3390/agronomy15020462

APA Style

Petrović, I., Vučajnk, F., Trdan, S., Bernik, R., & Vidrih, M. (2025). The Influence of Planting Speed of a Maize Vacuum Planter on Plant Spacing Variability and Ear Parameters. Agronomy, 15(2), 462. https://doi.org/10.3390/agronomy15020462

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