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Article

UAV Multispectral Imaging for Multi-Year Assessment of Crop Rotation Effects on Winter Rye

by
Mindaugas Dorelis
*,
Viktorija Vaštakaitė-Kairienė
and
Vaclovas Bogužas
Agriculture Academy, Vytautas Magnus University, Studentu Str. 11, LT-53361 Akademija, Lithuania
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(21), 11491; https://doi.org/10.3390/app152111491
Submission received: 7 October 2025 / Revised: 26 October 2025 / Accepted: 26 October 2025 / Published: 28 October 2025
(This article belongs to the Special Issue Effects of the Soil Environment on Plant Growth)
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Versions Notes

Abstract

Crop rotation is a cornerstone of sustainable agronomy, whereas continuous monoculture can degrade soil fertility and crop vigor. A three-year field experiment (2023–2025) in Lithuania compared winter rye grown in a long-term field experiment of continuous monoculture (with and without fertilizer/herbicide inputs) with five diversified rotation treatments that included manure, forage, or cover crop phases. Unmanned aerial vehicle (UAV) multispectral imaging was used to monitor crop health via the Normalized Difference Vegetation Index (NDVI, an indicator of plant vigor). NDVI measurements at three key developmental stages (flowering to ripening, BBCH 61–89) showed that diversified rotations consistently achieved higher NDVI than monoculture, indicating more robust crop growth. Notably, the most intensive and row-crop rotations had the highest canopy vigor, whereas continuous monocultures had the lowest. An anomalous weather year (2024) temporarily reduced NDVI differences, but rotation benefits re-emerged in 2025. Overall, UAV-based NDVI effectively captured rotation-induced differences in rye canopy vigor, highlighting the agronomic advantages of diversified cropping systems and the value of UAV remote sensing for crop monitoring.

1. Introduction

Crop rotation is a foundational practice for sustainable agriculture in temperate climates, recognized for maintaining soil fertility and breaking pest cycles. In contrast to continuous monoculture, diversified rotations improve long-term soil productivity, stability, and agroecosystem resilience [1,2,3]. Rotating different crop functional groups (cereals, root crops, legumes, and forages) helps ensure more efficient use of resources and reduces the need for external chemical inputs, a priority emphasized by European Union agricultural policy [1]. Recent research in Northern Europe confirms that long-term crop rotations can increase crop yields, suppress weeds, and enhance overall agroecosystem resilience under intensive farming conditions [2,3,4]. For example, a continental analysis found that more diverse rotations increased grain yields by ~28% on average and significantly reduced yield losses during drought years (by 14–90%) [3]. These benefits are increasingly important in the context of climate change, as diversified rotations buffer against extreme weather and help sustain production on vulnerable soils [2,3,4].
In temperate regions like Lithuania, the ecological services provided by rotation are especially critical. The climate features heavy autumn rains, spring snowmelt, and generally high precipitation, which on bare or continuously tilled soil leads to erosion and nutrient leaching. Incorporating cover crops and perennial forages into rotations protects the soil during these vulnerable periods. For instance, the inclusion of winter-hardy crops can reduce soil erosion and nitrogen leaching losses while increasing soil organic carbon storage [2,5]. For instance, including winter-hardy cover crops or grasses provides year-round cover that dramatically curtails water erosion on sloping land. Long-term field trials in Lithuania have shown that perennial grass phases in a rotation can virtually eliminate runoff-driven soil loss—reducing annual erosion by over 75% compared to rotations dominated by row crops like potato [6]. Thus, rotational systems with a high share of forage or cover crops contribute to soil conservation and landscape stability in hilly temperate landscapes [6]. Furthermore, by increasing organic matter inputs and enhancing soil structure, diversified rotations also promote carbon sequestration and water retention in soils [1,4,5,7,8].
Winter rye (Secale cereale L.) plays a key agroecological role in such rotations, particularly under Northern European climatic and agronomic conditions, where long, cold winters and high precipitation during autumn and spring increase the risks of soil erosion and nutrient leaching. In these regions, winter-hardy cover crops like rye are essential for maintaining soil cover and nutrient retention throughout the non-growing season [2,9,10]. By keeping the soil covered and roots active during the fallow season, rye cover crops significantly reduce soil loss compared to leaving fields bare. In long-term rotation trials, adding rye as a cover crop was noted to have “positive effects in reducing erosion and improving soil quality”, underscoring its role in soil conservation [2,11].
Another key ecosystem service of winter rye is its strong nutrient scavenging capacity, particularly for nitrogen. Rye’s deep and fibrous root system captures residual nitrate from the soil in late fall and early spring, storing it in biomass and preventing leaching into groundwater [10]. Studies have consistently shown that cover crops like rye can dramatically curtail nitrate leaching over winter—globally, cover cropping has reduced nitrate losses by around 50–70% compared to bare fallow fields [12]. In simulation and field trials, winter rye cover crops have been found to reduce nitrate-N leaching by sequestering soil nitrogen during the fallow period [13]. This nitrogen catch-crop effect is especially valuable in regions like Northern and Eastern Europe, helping retain nutrients in the agroecosystem and improving fertilizer use efficiency for subsequent crops. However, like most crops, rye suffers yield declines under continuous monocropping, underscoring the need for rotation to realize its full productive potential [2].
A traditional rotation in the Baltic region involving winter rye is often paired with other complementary crops such as potato (Solanum tuberosum L.), oats (Avena sativa L.), and a mixed perennial ley of red clover (Trifolium pratense L.) and timothy grass (Phleum pratense L.). This multi-year rotation represents a balance of cereals, root crops, legumes, and forages chosen to exploit their synergistic benefits. The leguminous clover in the grassland is particularly valuable for biological nitrogen fixation; it enriches the soil with nitrogen and contributes to the nutrition of subsequent crops [4]. Legume cover crops like clover have been shown to improve soil nitrogen availability for following cereal crops, often allowing reductions in synthetic fertilizer use [4]. The clover-timothy mixture, typically maintained for a year or more, also adds substantial organic matter to the soil and improves soil structure [6]. Meanwhile, the inclusion of oats or other spring cereals provides a disease break and an additional cover period. For example, introducing a winter rye cover crop after potato harvest significantly suppresses soil-borne diseases like Rhizoctonia and common scab in the next potato crop [11]. Larkin et al. [11] found that a winter rye cover reduced potato black scurf and scab incidence by roughly 10% across rotations, and when used in combination with a diversified rotation (e.g., including a Brassica break crop), disease severity dropped by over 30% relative to continuous potato cropping. The same study noted improved soil microbial activity and a richer soil microbial community where rye cover crops were used, alongside a modest increase in tuber yields. These findings illustrate that winter rye cover cropping not only protects the soil but also fosters a soil environment that can boost crop health and yield. Likewise, long-term rotation experiments in Lithuania have reported higher winter rye grain yields and thousand-kernel weights in rotated fields compared to rye monocultures, attributable to lower weed pressure and better soil conditions [7]. Overall, integrating winter rye with potatoes, oats, and a clover-timothy ley exemplifies the agroecological intensification of cropping systems: such rotations leverage biological nutrient inputs, natural pest suppression, and soil conservation [1].
While NDVI is widely used to monitor crop health and vigor, its potential to quantify the effects of crop rotation remains underexplored. Most previous studies have focused on short-term observations, employed coarse-resolution satellite data, or lacked systematic comparisons across rotation systems. These limitations reduce the ability to detect fine-scale, rotation-induced variability in canopy development. Moreover, NDVI saturation in dense canopies and variable correlation with final yield across different crops introduce further uncertainty. Crucially, many studies have not integrated NDVI with ground-truth indicators such as yield or soil quality, making it difficult to translate spectral metrics into agronomic insights. This study addresses these gaps by employing high-resolution unmanned aerial vehicle (UAV)-based NDVI monitoring over a multi-year period, capturing canopy dynamics at key phenological stages in a controlled, long-term crop rotation experiment.
Monitoring the performance of these complex rotations and quantifying their ecosystem services has been greatly enhanced by advances in remote sensing technologies. Drone-based multispectral imaging has emerged as a powerful tool for agricultural monitoring in recent years. UAVs equipped with sensors can capture frequent, high-resolution images of crop fields, enabling detailed tracking of crop growth, vigor, and ground cover over time. One of the most widely used metrics derived from such imagery is the Normalized Difference Vegetation Index (NDVI). NDVI is a spectral index calculated from red and near-infrared reflectance, and it correlates strongly with key indicators of crop health such as green canopy cover, leaf area index (LAI), and leaf chlorophyll or nitrogen content [14]. In practical terms, NDVI provides a quantitative measure of vegetative “greenness” or vigor, which has been shown to reflect crop phenology, biomass, and stress levels. NDVI was chosen as our core index of crop vitality because it integrates multiple aspects of canopy health (leaf chlorophyll, biomass, and cover) into a single metric. Higher NDVI corresponds to denser, more chlorophyll-rich foliage—traits directly driven by soil resource availability (nutrients, organic matter, and moisture) [15]. In other words, because plant productivity is strongly tied to soil fertility, NDVI serves as a proxy for soil quality: fields with higher NDVI tend to have richer soil organic carbon and nutrient stocks [16,17]. This mechanistic link means that rotation effects on NDVI can be interpreted through their impacts on soil health (organic matter accumulation, nutrient cycling), which in turn affect rye vigor. Because NDVI responds to changes in plant density and condition, it is widely used to monitor crop development and diagnose issues in real time. Drone-based NDVI mapping offers several advantages for rotation studies: it is non-destructive, covers entire field areas (capturing spatial variability), and can be repeated at critical growth stages to compare crop performance under different rotation treatments.
Recent precision agriculture studies demonstrate the value of UAV-derived NDVI for assessing crop management impacts. For example, a recent precision agriculture study demonstrated that drone-derived NDVI could accurately capture differences in wheat canopy growth under varying nitrogen regimes and even predict final yields with high correlation [14]. In the context of crop rotations, such remote sensing tools allow researchers to observe how different crops and management practices influence vegetation dynamics throughout the season. Notably, NDVI has been successfully used to evaluate cover crops—remote sensing can estimate cover crop biomass production and fractional ground cover with good accuracy [9]. These measures are not just agronomically important but also serve as proxies for ecosystem services. For instance, a high NDVI in winter cover crop fields is indicative of substantial biomass and groundcover, which translates to greater erosion protection and nutrient uptake from the soil. In one study, satellite and proximal NDVI data were used to estimate cover crop biomass and ground cover, providing an “accurate assessment of environmental benefits” such as reduced nitrogen loss and improved soil health due to the cover crops [18]. By using NDVI and other vegetation indices, it becomes feasible to quantify the otherwise intangible benefits of rotations (like soil cover or vigor due to enhanced soil fertility) in a standardized way [19]. This approach aligns with ongoing efforts to integrate precision remote sensing with agroecosystem evaluation, effectively linking plot-level crop performance with broader ecosystem service outcomes [9]. However, it remains unclear how well these rotation-induced benefits can be quantitatively tracked over multiple seasons and verified in the field using high-resolution NDVI over multiple seasons—a gap this study seeks to address.
In this study, we investigate winter rye performance under continuous monoculture versus diversified rye-based rotations in a long-term field experiment, using multi-year drone NDVI monitoring alongside conventional agronomic measurements. The experimental context (Lithuania, 2023–2025) provides a unique opportunity to quantify how rotation diversity impacts crop growth, yield, and environmental indicators over several seasons. The novel contributions of this work include (1) the demonstration of UAV multispectral imaging for long-term monitoring of crop rotation effects on winter rye, (2) a comparative analysis of continuous rye monoculture (with and without fertilizer/herbicide inputs) versus multiple diversified rotation schemes, and (3) new insights into how rotation diversity enhances crop vigor and sustainability—information that can inform precision agriculture practices and sustainable agronomy in temperate cropping systems.

2. Materials and Methods

2.1. Experiment Design and Agricultural Practices

A long-term crop-rotation experiment was initiated in 1967 by Prof. A. Stancevičius at the Vytautas Magnus University Experimental Station (54°53′ N, 23°50′ E) and has continued to the present [2].
The experimental site soil formed from basal moraine or subglacial deposits overlain by limnoglacial sediments. The soil of the experimental site was Endocalcari-Epihypogleyic Cambisol (sicco) (CMg-p-w-can) [20]. The water regime is controlled by subsurface drainage, and the microrelief was leveled.
The experiment consists of 58 crop-rotation plot combinations, each with a harvested area of 19 × 9.60 m, arranged in three replications. In total, 15 crop species are cultivated annually within the rotation system. The research was performed on winter rye (Secale cereale L.) ‘Matador’ (180 kg ha−1). The study was conducted in winter rye monoculture without NPK fertilizers and herbicides, and monoculture with fertilizers and herbicides, and intensive, three-course, field rotations with and without raw crops, and rotation for green manure (Table 1).
At the beginning of spring vegetation of winter rye, the plots were fertilized with 200 kg ha−1 of ammonium nitrate and additionally with 250 kg ha−1 after two weeks. Rye crop stands were protected from lodging and were sprayed with the growth regulators Cycocel 750 SL1 at 1.2 L ha−1 (i.e., chlormequat chloride 750 g L−1) and Stabilan 750 SL (i.e., chlormequat chloride 750 g L−1). In spring, winter rye was sprayed with the herbicide Arelon flussig at 1.2 L ha−1 (i.e., isoproturon 50 g L−1); with fungicides INPUT 460 EC (i.e., prothioconazole 160 g L−1, spiroxsamine 300 g L−1) 1.0 L ha−1; and with Fandango (i.e., prothioconazole 100 g L−1, fluoxastrobin 100 g L−1) 1.0 L ha−1.
Crop rotations vary not only in crop sequence but also in the amount and type of organic matter applied (crop residues, root litter, and cattle manure) (Table 2). In all rotations, straw was retained and incorporated into the soil as organic fertilizer. Cattle manure at a rate of 55 t ha−1 was applied to winter rye within the row-crop and three-course rotations. In intensive crop rotation manure was applied once every 6 years after winter rye for the following potato. Organic amendments were incorporated by plowing to a depth of 20–25 cm.

2.2. Meteorological Conditions

The meteorological data were obtained from the Kaunas Meteorological Station, operated by the Lithuanian Hydrometeorological Service under the Ministry of Environment, located at 54.888° N, 24.043° E (the station is part of VMU’s Agriculture Academy campus).
During the three growing seasons (September–August of 2022–2023, 2023–2024, and 2024–2025), the experimental site experienced variable weather conditions as detailed in Table 3. In 2022–2023, monthly precipitation ranged from 14.3 mm in May 2023 to 96.2 mm in August 2023. Total rainfall in this first season was moderate, with notably higher accumulation in winter and late summer (e.g., 66.5 mm in January 2023 and 96.2 mm in August 2023). Mean monthly air temperatures during 2022–2023 varied between −2.5 °C in December 2022 and 20.2 °C in August 2023, reflecting the expected cold winter and warm summer pattern.
In the 2023–2024 season, rainfall was more irregular, with two pronounced peaks: 99.1 mm in October 2023 and 109.4 mm in July 2024. In contrast, the driest month of this season was September 2023, with only 11.6 mm of precipitation. Monthly air temperatures in 2023–2024 ranged from a low of −3.85 °C in January 2024 to a high of 20.5 °C in July 2024. Notably, September 2023 was relatively warm (mean 17.1 °C), followed by a much cooler October 2023 (mean 8.4 °C), indicative of a sharp autumn temperature drop.
Similarly, the 2024–2025 growing season saw substantial summer rainfall, peaking at 118.0 mm in July 2025. June 2025 also had high precipitation (82.3 mm), whereas April 2025 was comparatively dry (19.9 mm). Average monthly temperatures during 2024–2025 ranged from −2.33 °C in February 2025 to 19.23 °C in July 2025. Overall, among the three seasons, 2023–2024 had the highest total rainfall (607.9 mm) while 2022–2023 was the driest (481.4 mm), with 2024–2025 intermediate at around 510 mm.
The long-term average temperature and the sum of precipitation rate were published in previous studies [2,7].

2.3. UAV Data Acquisition

Multispectral imagery was acquired using an XAG M500 (XAG Co., Ltd., Guangzhou, China) multirotor UAV operating in RTK mode. The system was integrated with the XAG Agricultural Fixed RTK Base Station (XAG Co., Ltd., Guangzhou, China), providing centimeter-level spatial accuracy through multi-constellation signal tracking, including BDS (B1I/B2I/B3I/B1C/B2a), GPS (L1/L2/L5), GLONASS (L1/L2), Galileo (E1/E5a/E5b), and QZSS (L1/L2/L5). This configuration enabled a horizontal positioning accuracy of ±10 mm + 1 ppm (RMS) and a vertical accuracy of ±15 mm + 1 ppm (RMS). Flights were carried out in autonomous mission mode at a consistent speed of 8 m s−1 during image acquisition to ensure uniform coverage and minimize motion blur. An XAG M500 multirotor drone was equipped with a 20 MP multispectral gimbal camera (XAG Co., Ltd., Guangzhou, China) to collect aerial imagery. The camera features a mechanical shutter (up to 1/2000 s) and a stabilized 3-axis gimbal, with onboard automatic lens distortion correction for sharp, undistorted captures. This UAV platform provided high-resolution multispectral images suitable for detailed vegetation index mapping.
Flight parameters were standardized as follows:
  • Flight timing: To control lighting variability and optimize spectral data quality, flights were conducted between 12:00 and 14:00 local time under cloud-free conditions. This time window corresponds to peak solar elevation in Kaunas, Lithuania, during the primary monitoring months. Typical midday solar angles in this region are high: In May and June, the solar elevation is approximately 38° at 12:00 and peaks around 41° by 14:00. June, near the summer solstice, presents the highest angles of the year. In July, although the solar angle begins to decline slightly post-solstice, it remains elevated at 38–41° during the midday period. These stable high-angle conditions reduced shadow artifacts, enhanced image uniformity, and improved the accuracy and consistency of NDVI calculations across all data collection dates.
  • Altitude and resolution: The drone maintained an altitude of about 45 m above ground level, resulting in an approximate ground sampling distance (GSD) of 5.243 cm per pixel.
  • Overlap: Adjacent images were captured with 70% forward lap and 70% side lap, ensuring sufficient overlap for photogrammetric processing and complete coverage of each field at high resolution.
The measurements were done at three different winter rye phenological (growth) stages according to Biologische Bundesanstalt (Federal Biological Research Centre), Bundessortenamt (Federal Plant Variety Office), and Chemical industry (BBCH): BBCH 61–69; BBCH 71–79; BBCH 83–89.

2.4. Image Processing and NDVI Computation

All captured multispectral images were processed using Pix4Dfields software Version 2.8.4 (Pix4D SA, Prilly, Switzerland) to generate georeferenced orthomosaics and per-band reflectance maps. The software aligns and stitches the overlapping images into a seamless orthomosaic, applying camera calibrations and blending for uniform exposure. This produced a high-resolution composite map of each field, as well as calibrated reflectance layers for the individual spectral bands (including red and near-infrared).
From the reflectance maps, a Normalized Difference Vegetation Index (NDVI) layer was computed to assess crop vigor. For each pixel, NDVI was calculated using the standard formula:
N D V I = N I R − R e d N I R + R e d ,
where NIR and Red are the reflectance values in the near-infrared and red bands, respectively [21]. The NDVI values range from −1 to +1, with higher positive values indicating denser and healthier vegetation cover. These NDVI maps provided a quantitative measure of relative vegetation vigor across the fields, identifying spatial variations in crop health for further analysis.

2.5. Statistical Analysis

Statistical analyses were conducted using XLSTAT 2025 (Addinsoft, Paris, France). Normality of the residuals was verified using the Shapiro–Wilk test (p > 0.05). Homogeneity of variances was assessed using Levene’s test. When assumptions were met, NDVI data were analyzed using a mixed-effects model with “crop rotation” as a fixed factor and “year” as a random effect to account for interannual variability. For each growth stage (BBCH 61–69, 71–79, and 83–89), treatment means (n = 3) were compared using one-way ANOVA followed by Tukey’s Honest Significant Difference (HSD) post hoc test at p < 0.05 [22].

3. Results

3.1. NDVI Indexes of Winter Rye at BBCH 61–69

In this study, interannual differences refer to variation among the three growing seasons (2023, 2024, 2025), whereas differences in the reproductive period denote contrasts among BBCH 61–69 (flowering), 71–79 (grain filling), and 83–89 (ripening).
At flowering (BBCH 61–69), between-year effects were generally small within most rotations; however, MONOFH increased in 2025 relative to 2023–2024 (Figure 1). Over the years, INT and FWR consistently exhibited the highest NDVI, while MONO and MONOFH were the lowest. NDVI values showed distinct differences among crop rotations; NDVI values in 2024 were visibly lower than those in 2023 and 2025, although year-to-year variations within most treatments were not statistically significant. In MONO, NDVI remained statistically similar across years. MONOFH also showed no differences between 2023 and 2024, but values were significantly higher in 2025. INT, FWR, TC, FR, and SI exhibited no significant year effects.
Between treatments, INT and FWR recorded the highest NDVI in 2023, significantly exceeding MONO and MONOFH, but not differing from TC, FR, or SI. In 2024, INT, FWR, FR, and SI maintained significantly higher NDVI than MONO, while remaining statistically like TC and MONOFH. By 2025, INT, TC, and FWR again showed the highest NDVI values, significantly higher than MONOFH and MONO, but not differing from FR and SI.
Overall, INT and FWR consistently supported the highest canopy vigor across all years, whereas MONO and MONOFH exhibited the lowest NDVI, particularly in MONOFH during 2025.
The spatial distribution of NDVI within each treatment, illustrating within-field variability across years, is presented in Supplementary Figures S1, S4 and S7. These figures show that diversified rotations (INT and FWR) consistently exhibited more uniform NDVI spatial patterns compared with monocultures (MONO and MONOFH), indicating more homogeneous crop growth.

3.2. NDVI Indexes of Winter Rye at BBCH 71–79

At grain filling (BBCH 71–79), INT/TC/FWR ranked highest in 2023; in 2024, the treatment separation narrowed, with MONO significantly lower than several diversified rotations, and in 2025 INT and FWR again outperformed MONO and MONOFH (TC/FR/SI intermediate) (Figure 2). Between treatments, INT, TC, and FWR reached the highest NDVI in 2023, significantly exceeding MONOFH, while MONO remained at an intermediate level. In 2024, the significantly lower NDVI was MONO compared to MONOFH, INT, FWR, FR, and SI; however, it did not significantly differ from TC. By 2025, INT and FWR again exhibited the highest NDVI values, significantly outperforming MONO and MONOFH, while TC, FR, and SI occupied intermediate positions. The unfertilized MONO may have maintained a slightly higher NDVI signal than MONOFH due to greater overall groundcover, potentially including weed presence, which can contribute to NDVI measurements.
Overall, INT and FWR consistently supported the greatest canopy vigor across years, whereas MONOFH exhibited the weakest performance, particularly in 2023 and 2025.
Supplementary Figures S2, S5 and S8 provide spatial NDVI maps for these growth stages. As visible in these figures, areas of reduced vigor were concentrated in MONO and MONOFH plots, while INT and FWR plots maintained high NDVI uniformity across years.

3.3. NDVI Indexes of Winter Rye at BBCH 83–89

At ripening (BBCH 83–89), no significant differences in NDVI were observed among treatments in 2023 (Figure 3). In 2024, FR and MONOFH exhibited significantly higher NDVI values compared to TC, FWR, and INT, with INT showing the lowest values; however, the differences between INT, TC, and FWR were not statistically significant. FR and MONOFH did not differ significantly from MONO and SI. In 2025, MONO recorded the highest NDVI, although the differences between MONO, TC, and FWR were not significant. Nevertheless, MONO showed significantly higher NDVI values than MONOFH, INT, FR, and SI. No significant differences were found among TC, FWR, and the lower-performing treatments (MONOFH, INT, FR, and SI).
Over the years, NDVI values for MONO followed an increasing trend, being lowest in 2024, moderate in 2023, and highest in 2025. In MONOFH, NDVI was lowest in 2024, while values in 2025 were slightly higher than those in 2023. The INT treatment showed a marked decline in 2024, followed by a strong recovery in 2025, exceeding the levels recorded in 2023. For TC and FWR, NDVI reached the lowest values in 2024 and the highest in 2025. In FR, NDVI remained moderate in 2024 and increased in 2025 compared to 2023. Similarly, SI exhibited its lowest NDVI in 2024 and its highest in 2025.
Spatial NDVI patterns during the ripening stage (Supplementary Figures S3, S6 and S9) further confirm these trends, showing higher canopy uniformity and greater NDVI stability under diversified rotations compared to monocultures, particularly evident in 2025.
Year-wise trends showed the lowest values for many treatments in 2024 and a recovery in 2025. These patterns align with the documented meteorological context: irregular rainfall with peaks in late 2023 and mid-2024 likely constrained development and reduced between-treatment contrasts in 2024, while a warmer early spring and ample summer rainfall in 2025 favored the re-emergence of rotation advantages.

4. Discussion

Advances in UAV-based remote sensing have provided powerful tools for non-invasive crop monitoring in precision agriculture. Drone-mounted multispectral cameras enable rapid, high-resolution mapping of NDVI across fields, offering frequent insights into crop growth and vigor at key phenological stages [23,24]. This approach has been successfully applied in other cereals—for instance, drone-derived NDVI data have guided site-specific nitrogen management in maize and accurately predicted final yields [23,24]. In our study, repeated UAV NDVI measurements allowed quantitative comparison of winter rye canopy development under different rotation systems over three seasons. NDVI is a well-established proxy for green biomass and crop health, correlating strongly with leaf area index and yield in cereals [13,23]. Thus, the observed NDVI patterns provide a robust indication of how rotation diversity influenced rye growth and performance under Lithuanian conditions.
Across all three growth stages (BBCH 61–69, 71–79, and 83–89) and study years (2023–2025), clear differences in canopy vigor were evident between the rotation treatments. The diversified rotations—particularly the intensive rotation (INT) and the field-with-row-crops rotation (FWR)—consistently exhibited the highest NDVI values, reflecting dense and vigorous rye canopies. In contrast, the continuous rye monoculture without fertilizers and herbicides (MONO) and its high-input variant (MONOFH) showed the lowest NDVI values throughout the experiment. For example, at the flowering stage (BBCH 61–69) in 2023, the INT and FWR plots attained significantly greater NDVI than both MONO and MONOFH, highlighting the immediate benefits of rotation even in the first year. This superiority of INT and FWR was maintained at later growth stages and in subsequent years: by 2025, these rotations again recorded the highest NDVI readings, whereas the two continuous rye systems lagged markedly.
Rotations of intermediate diversity, such as the three-course rotation (TC), the field-without-row-crops rotation (FR), and the green-manure/sideration rotation (SI), generally produced moderate NDVI values between these extremes. Notably, even the fertilized or herbicide-treated continuous rye (MONOFH) could not match the NDVI of the best diversified rotations despite the additional inputs—by the third year, its canopy greenness had deteriorated considerably. This suggests that while chemical inputs provided some short-term relief, they could not compensate for the absence of rotation-derived benefits such as improved soil health and pest suppression [1,2]. Diversified sequences (with legumes, cover crops, or manures) build soil organic matter and nutrient pools over time, improving soil structure and moisture retention. In contrast, continuous rye tends to exhaust specific nutrients and degrade soil carbon, which limits canopy vigor [25]. The unfertilized monoculture (MONO) fared worse of all, underscoring the well-known penalty of continuous cropping on crop vigor and productivity [2]. Long-term studies similarly report that monoculture systems suffer reduced biomass and yield compared to rotations, even when extra fertilizers are applied [2]. Diversified rotations evidently foster a more robust rye canopy, whereas continuous rye (especially without external inputs) struggles to maintain equivalent growth.
Interannual trends in NDVI further reveal how rotation advantages are modulated by yearly weather conditions. Within most given rotations, mean NDVI did not differ greatly across 2023, 2024, and 2025, indicating a degree of yield stability from year to year. However, the year 2024 stood out as an anomaly in which differences between rotation treatments nearly disappeared. During the grain-filling stage (BBCH 71–79) of 2024, all treatments—from continuous rye to diverse rotations—exhibited statistically similar NDVI values, with no clear ranking. This convergence coincided with extreme weather stress. An abnormally wet autumn in 2023 likely caused waterlogging and nutrient leaching during the establishment of the 2024 rye crop, followed by a spring drought during heading and grain filling. These back-to-back stresses curtailed overall crop development, effectively “leveling the playing field” so that even rotations normally associated with vigorous growth could not fully express their potential. However, it is worth noting that MONO and MONOFH exhibited relatively higher NDVI than the more diverse rotations in 2024. This may be attributed to environmental anomalies during that season: an unusually wet autumn (99.1 mm in October 2023) likely hindered crop establishment due to waterlogging, while the following spring drought (13.5 mm in March) suppressed soil biological activity and delayed nutrient release from organic residues in rotation plots. In contrast, the simpler monoculture systems, particularly those receiving fertilizers (MONOFH), were less dependent on soil microbial dynamics and may have responded more rapidly to available nutrients and subsoil moisture, resulting in stronger late-season canopy development as captured by NDVI. By contrast, growing conditions rebounded in 2025, allowing rotation benefits to re-emerge. Warmer early spring temperatures and improved moisture enabled rotations with higher soil organic matter and better structure to recover canopy vigor more rapidly. The INT and FWR plots, having superior soil fertility and structure, capitalized on these favorable conditions—by 2025, their NDVI advantage reappeared strongly. Additionally, heavy rainfall in summer 2025 may have favored these rotations, which possess better drainage and root systems, while the continuous rye plots likely suffered from waterlogging and lodging due to weaker soil structure. This demonstrates that while extreme weather conditions can temporarily mask the benefits of rotation, diversified cropping systems have the capacity to recover and reassert their advantages once favorable conditions return, whereas continuous monocultures remain more vulnerable to stress.
These patterns are consistent with other European research showing that diversified crop rotations increase cereal vigor, yield, and resilience relative to monocultures [1,2]. For example, a 50-year Lithuanian field trial reported that winter rye yields were about 22% lower under continuous monoculture than when rye followed a two-year perennial grass ley with manure application [2,7]. Even intensive fertilizer and herbicide use in rye monoculture could not match the yields or canopy vigor achieved in rotations with cover crops and organic amendments [1,2]. Our NDVI results mirror those long-term findings: while chemical inputs in MONOFH temporarily improved canopy color, the structural and biological benefits of diverse rotations (INT, FWR) ultimately produced stronger and more stable NDVI.
Remote sensing assessments in other cereal systems reinforce these findings. NDVI has been widely used as a proxy for biomass and vigor in maize and wheat, correlating well with final yields [26,27,28,29]. NDVI-based monitoring has also proven effective in evaluating cover crop biomass and ground cover, both of which translate to improved soil protection and nitrogen retention [30,31,32,33,34]. In our study, rotations that included fertility-building crops (SI with winter rapeseed, FWR/FR with grass-clover ley) showed higher rye NDVI than those without such components, reflecting improved soil nitrogen and structure for the succeeding crop. The link between legume cover crops, enhanced soil nitrogen, and cereal canopy greenness is well documented [8,35]. Consequently, our results support the principle that NDVI effectively captures the cumulative agronomic advantages of rotation diversity—improved nutrient availability, greater organic inputs, and better soil health—which manifest as stronger canopy vigor and spectral reflectance.
One key mechanism behind the observed NDVI differences is improved nutrient cycling and soil fertility in diversified rotations. Rotations with organic matter inputs and legumes (INT, FWR, FR, SI) enhance soil organic carbon, nutrient availability, and biological activity, which in turn sustain more vigorous crop growth [7,36]. Continuous rye systems, by contrast, tend to suffer from nutrient depletion and pathogen buildup, leading to reduced growth despite fertilizer additions [5,37,38]. Rotations including legumes or high-residue crops add organic nitrogen and biomass to the soil each season, gradually building fertility and improving aggregation. This accumulated organic matter enhances nutrient cycling and root growth, which manifests as greener, more vigorous canopies (higher NDVI) [25].
Another important benefit of rotation diversification is the natural suppression of crop pests (including diseases and weeds). Studies have shown that including cover crops or Brassica species in rotations can reduce soil-borne disease pressure and increase soil microbial activity [39]. Similarly, allelopathic and competitive properties of rye itself can suppress weeds, but this benefit diminishes under long-term monocropping as weed communities adapt [39,40,41]. Weed density and seed banks are significantly lower under rotations than under monoculture, as shown in Lithuanian long-term studies [2]. Hence, crop diversity contributes to both direct soil fertility gains and indirect pest control, reinforcing canopy vigor.
Soil structure and water dynamics also explain the NDVI trends. Long-term rotation systems increase soil porosity and aggregation, improving water retention during drought and drainage under excess rainfall [42,43,44,45]. This helps explain why rotations like INT and FWR maintained relatively stable NDVI through the spring drought of 2024 and recovered quickly in 2025. In contrast, the compacted, lower-organic soils of continuous rye systems limited root penetration and moisture storage, making them more vulnerable to climatic stress [46,47]. Similar relationships between soil structure and NDVI have been reported in other long-term rotation trials across Europe [48]. The cumulative evidence confirms that rotations integrating organic matter, legumes, and cover crops not only improve soil health but also enhance canopy development, measurable through NDVI.
Finnish multi-year field data (Peltonen-Sainio et al., 2019) [49] used Sentinel-2 NDVI to quantify pre-crop values on thousands of fields. This analysis showed that break crops—especially grain legumes, rapeseed, sugar beet, etc.—gave higher NDVI (and inferred yields) in following cereals compared to continuous monoculture [49]. These findings directly align with the Lithuanian study: diversified rotations with legumes or cover crops elevated winter cereal NDVI relative to monoculture. Mattila and Girz [50] reported the opposite effect under some conditions. In side-by-side split-plot experiments over four years, undersown cover crops in cereals reduced the main crop’s performance by about 5% on average. NDVI maps of autumn and early summer clearly revealed crop-cover competition (especially on wetter patches). Thus, in these trials, adding cover crops did not boost the cereal’s NDVI; on the contrary, it caused modest vigor loss in spring. This suggests a context-dependent tradeoff: cover crops can improve long-term soil health but may transiently suppress early-season NDVI/growth of the following crop under moisture stress. Overall, it was observed that cover cropping slightly contradicts the expectation of higher NDVI in the first year (though the effect was small and spatially variable). Reuter et al. [51] used UAV multispectral imaging on organic cereal-grassland fields. They divided clover-grass stands into management zones by NDVI and biomass, and then measured the following crop (summer spelt and then winter wheat) yields. High-NDVI zones (with more legume biomass) yielded significantly more than low-NDVI zones. In other words, plots with greener (higher-NDVI) legume cover produced healthier, higher-yielding successor crops. This supports the Lithuanian results: including a green manure cover enhanced subsequent cereal vigor as seen in NDVI maps. The authors conclude that UAV-NDVI can effectively delineate field zones in diversified rotations to guide management.
NDVI differences (around 0.1) are in line with magnitudes reported in other studies under comparable management contrasts. Using UAVs, Papadopoulos et al. [52] found NDVI differed by around 0.10 between enhanced efficiency vs. conventional nitrogen treatments in a cereal crop. Similarly, cover-cropped fields have shown slightly lower early-season NDVI (on the order of 0.05–0.1) compared to no-cover fields, as detected by high-resolution drone imagery [53]. Even with ground sensors, rotations can induce NDVI gaps: e.g., Govaerts and Verhulst [54] observed maize after wheat had around 0.05–0.1 lower NDVI than maize monoculture at early growth stages. Such findings affirm that an NDVI increase of approximately 0.1, like we observed under diversified rotations, signals a notable improvement in crop vigor. Indeed, a 0.1 NDVI rise has been correlated with about 0.8–1.3 t ha−1 higher cereal yield in pan-European analyses [55]. Thus, the NDVI advantages of rotations in our study are quantitatively consistent with previously reported benefits of rotation, cover cropping, and optimal fertilization on canopy development.
The first limitation is the relatively short duration of this study’s observations. Our UAV-based NDVI monitoring covered only three growing seasons, which may not capture the full variability of climate impacts on crop rotations. Another challenge is the reliance on NDVI alone as a proxy for crop performance. We did not directly measure winter rye grain yields, quality, or soil health indicators alongside the NDVI data, making it difficult to quantify the practical significance of the observed canopy differences. For instance, it remains unclear how much of a yield increase would correspond to the approximately 0.1 higher NDVI index observed in the INT and FWR rotations compared to MONO.
A further limitation is the site-specific scope of our experiment. This study was conducted at a single location in Lithuania with a fixed set of rotation treatments, so the results may not readily generalize to different regions or farming systems. Different climates, soil types, or crop sequences might yield different outcomes, and we did not assess whether the rotation effects detectable by NDVI at the plot scale would also be observable at larger farm or landscape scales.
Finally, there are technical constraints to the remote sensing approach used. We relied solely on the NDVI index for drone-based monitoring, which can saturate in very dense canopies and potentially mask subtle differences once crop biomass is high. Other spectral indices (e.g., red-edge NDVI) or imaging modalities (such as thermal and hyperspectral sensors) were not employed, meaning that finer gradations of crop condition (for example, early nutrient stress signs) may have gone undetected.
One important direction for future research is to extend the observation period beyond three years. Including additional growing seasons—especially years with extreme weather—would help confirm whether the observed rotation benefits hold under a wider range of conditions. Longer-term monitoring might also reveal gradual improvements (e.g., accumulating soil health benefits) that further enhance NDVI over time.
Future studies should also link UAV-derived NDVI measurements with direct agronomic performance measurements. For example, determining how much additional grain yield corresponds to approximately a 0.1 increase in NDVI (as observed in the INT/FWR rotations versus MONO) would quantify the practical significance of the spectral differences. Similarly, tracking changes in soil organic carbon, nutrient levels, and biological activity under each rotation would help explain the NDVI differences and validate links between rotation-induced soil improvements and crop vigor. Another valuable extension would be to test these crop rotation effects across different regions and scales. Comparative trials in other environments could determine whether diverse rotations consistently boost NDVI and yield beyond the conditions of our study. Exploring additional rotation types (or cover crop species) may identify which diversification strategies maximize canopy development. In addition, scaling up the monitoring (e.g., using satellite-based NDVI data) can establish whether the rotation benefits observed at the plot level are detectable at farm or landscape scales.
Finally, future research should integrate more advanced remote sensing techniques with precision crop management. Indices less prone to saturation (such as red-edge NDVI or an enhanced vegetation index) and other sensor modalities (thermal or hyperspectral imaging) could be used to capture accurate indicators of crop status (e.g., mineral nutrient deficiency or stress) that basic NDVI might miss. Moreover, NDVI monitoring can be coupled with site-specific agronomic practices (fertilization or irrigation) for each crop rotation to test the resilience of rotation benefits under adaptive management.
Our findings are consistent with reports that UAV imaging can detect even minor variations in crop vigor across different treatments [14,52,56]. NDVI provides spatially detailed, non-destructive information that complements traditional yield and soil measurements [57,58].
Integrating such remote sensing tools with long-term rotation research and precision management will further advance the development of sustainable cropping systems.

5. Conclusions

In a three-year field trial (2023–2025), UAV-based NDVI monitoring clearly distinguished winter rye canopy vigor under different crop rotation treatments. Diversified rotations consistently produced higher NDVI values than continuous monocultures, indicating more robust crop growth under rotation. In particular, the intensive (INT) and field-with-row-crops (FWR) rotations showed the highest canopy NDVI, whereas continuous winter rye monoculture without NPK fertilizers and herbicides (MONO) and its high-input variant (MONOFH) had the lowest NDVI—underscoring the limitations of monocropping even when supplemented with fertilizers and herbicides. Notably, an anomalous weather year in 2024 narrowed the NDVI differences across all treatments, temporarily masking the rotation effects. By 2025, however, the typical pattern re-emerged: INT and FWR again achieved the highest NDVI (significantly exceeding the monoculture treatments), reaffirming that rotational diversity promotes sustained crop greenness and health. Overall, these results confirm the agronomic benefits of diversified crop rotations and demonstrate that UAV-derived NDVI is an effective, non-invasive tool for capturing rotation-induced differences in canopy vigor across variable seasonal conditions. The higher NDVI in INT and FWR rotations likely arose from their enhanced soil conditions: organic amendments (manure, cover crop residues) in those rotations build soil organic matter and nutrient pools, which in turn support greater leaf area and chlorophyll (higher NDVI) in the rye. Monoculture treatments, by contrast, appear to have diminished soil fertility (even with fertilization) and thus produced weaker canopy growth (lower NDVI).
These findings highlight the value of long-term UAV monitoring beyond this initial three-year trial to capture interannual climate variability and confirm the stability of rotation benefits over time. Indeed, our results suggest that rotation-enhanced NDVI reflects improved agroecosystem processes. Rotational systems with cover crops and manure input enhance soil structure, organic carbon, and nutrient cycling, directly boosting plant nutrient uptake and growth (and hence NDVI). In contrast, continuous monoculture without such practices can degrade soil quality and plant vigor, explaining the persistently lower NDVI in MONO and MONOFH. Understanding these ecological mechanisms highlights why diversified management (increasing soil organic matter and fertility) is significant for sustaining crop resilience and productivity.
Future research should directly correlate UAV-derived NDVI with winter rye grain yields and soil health metrics, validating NDVI as a reliable proxy for crop performance and revealing the soil fertility improvements behind higher canopy vigor. To generalize the study’s insights, comparable long-term rotation trials across diverse climates and soil conditions are needed to test whether the observed NDVI benefits of diversified rotations hold true beyond this single location. Additionally, integrating NDVI with other vegetation indices (e.g., red-edge NDVI) and coupling these remote sensing tools with precision agriculture interventions (such as NDVI-guided fertilization) could further refine crop management and maximize the practical benefits of rotation-enhanced canopy health.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/app152111491/s1, Figure S1: Spatial variability of winter rye (BBCH 61–69) in 2023; Figure S2: Spatial variability of winter rye (BBCH 71–79) in 2023; Figure S3: Spatial variability of winter rye (BBCH 83–89) in 2023; Figure S4: Spatial variability of winter rye (BBCH 61–69) in 2024; Figure S5: Spatial variability of winter rye (BBCH 71–79) in 2024; Figure S6: Spatial variability of winter rye (BBCH 83–89) in 2024; Figure S7: Spatial variability of winter rye (BBCH 61–69) in 2025; Figure S8: Spatial variability of winter rye (BBCH 71–79) in 2025; Figure S9: Spatial variability of winter rye (BBCH 83–89) in 2025.

Author Contributions

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

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article and Supplementary Materials. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
BBCHBiologische Bundesanstalt (Federal Biological Research Centre), Bundessortenamt (Federal Plant Variety Office), and Chemical industry
NDVINormalized Difference Vegetation Index
MONOWinter rye monoculture
MONOFHWinter rye monoculture with fertilizers and herbicides
INTIntensive crop rotation
TCThree-course rotation
FWRField with row crops
FRField without row crops
SIFor green manure/sideration

References

  1. Butkevičienė, L.M.; Skinulienė, L.; Auželienė, I.; Bogužas, V.; Pupalienė, R.; Steponavičienė, V. The Influence of Long-Term Different Crop Rotations and Monoculture on Weed Prevalence and Weed Seed Content in the Soil. Agronomy 2021, 11, 1367. [Google Scholar] [CrossRef]
  2. Bogužas, V.; Skinulienė, L.; Butkevičienė, L.M.; Steponavičienė, V.; Petrauskas, E.; Maršalkienė, N. The Effect of Monoculture, Crop Rotation Combinations, and Continuous Bare Fallow on Soil CO2 Emissions, Earthworms, and Productivity of Winter Rye after a 50-Year Period. Plants 2022, 11, 431. [Google Scholar] [CrossRef]
  3. Bowles, T.M.; Mooshammer, M.; Socolar, Y.; Calderón, F.; Cavigelli, M.A.; Culman, S.W.; Deen, W.; Drury, C.F.; Garcia y Garcia, A.; Gaudin, A.C.M.; et al. Long-Term Evidence Shows That Crop-Rotation Diversification Increases Agricultural Resilience to Adverse Growing Conditions in North America. One Earth 2020, 2, 284–293. [Google Scholar] [CrossRef]
  4. Gaudin, A.C.M.; Westra, S.; Loucks, C.E.S.; Janovicek, K.; Martin, R.C.; Deen, W. Improving Resilience of Northern Field Crop Systems Using Inter-Seeded Red Clover: A Review. Agronomy 2013, 3, 148–180. [Google Scholar] [CrossRef]
  5. Van Eerd, L.L.; Chahal, I.; Peng, Y.; Awrey, J.C. Influence of Cover Crops at the Four Spheres: A Review of Ecosystem Services, Potential Barriers, and Future Directions for North America. Sci. Total Environ. 2023, 858, 159990. [Google Scholar] [CrossRef]
  6. Jankauskas, B. Modelling of Terrestrial Erosion and Change of Soil Features under Soil Erosion on the Hilly Relief of Lithuania. Int. Arch. Photogramm. Remote Sens. 2000, 33 Pt 7, 615–622. [Google Scholar]
  7. Skinulienė, L.; Marcinkevičienė, A.; Dorelis, M.; Bogužas, V. The Effect of Long-Term Crop Rotations for the Soil Carbon Sequestration Rate Potential and Cereal Yield. Agriculture 2024, 14, 483. [Google Scholar] [CrossRef]
  8. Domnariu, H.; Reardon, C.L.; Manning, V.A.; Gollany, H.T.; Trippe, K.M. Legume Cover Cropping and Nitrogen Fertilization Influence Soil Prokaryotes and Increase Carbon Content in Dryland Wheat Systems. Agric. Ecosyst. Environ. 2024, 367, 108959. [Google Scholar] [CrossRef]
  9. Thieme, A.; Prabhakara, K.; Jennewein, J.; Lamb, B.T.; McCarty, G.W.; Hively, W.D. Intercomparison of Same-Day Remote Sensing Data for Measuring Winter Cover Crop Biophysical Traits. Sensors 2024, 24, 2339. [Google Scholar] [CrossRef] [PubMed]
  10. Rebong, D.; Henriquez Inoa, S.; Moore, V.M.; Reberg-Horton, S.C.; Mirsky, S.; Murphy, J.P.; Leon, R.G. Breeding Allelopathy in Cereal Rye for Weed Suppression. Weed Sci. 2024, 72, 30–40. [Google Scholar] [CrossRef]
  11. Larkin, R.P.; Griffin, T.S.; Honeycutt, C.W. Rotation and Cover Crop Effects on Soilborne Potato Diseases, Tuber Yield, and Soil Microbial Communities. Plant Dis. 2010, 94, 1491–1502. [Google Scholar] [CrossRef]
  12. Nouri, A.; Lukas, S.; Singh, S.; Singh, S.; Machado, S. When Do Cover Crops Reduce Nitrate Leaching? A Global Meta-Analysis. Glob. Change Biol. 2022, 28, 4736–4749. [Google Scholar] [CrossRef]
  13. Tahir, M.; Fernández, F.G.; Ricks, N.; Mulla, D.J. EPIC Model Prediction of Winter Rye Cover Crop Effects on Crop Yield and Nitrate-N Leaching in Minnesota. Agron. J. 2025, 117, e70065. [Google Scholar] [CrossRef]
  14. Venkata Sai Chakradhar Reddy, D.; Sahoo, R.N.; Kondraju, T.; Rejith, R.G.; Ranjan, R.; Bhandari, A.; Moursy, A.; Tripathi, S.C.; Kumar, N. Drone-Based Multispectral Imaging for Precision Monitoring of Crop Growth Variables. Biol. Life Sci. Forum 2025, 41, 10. [Google Scholar] [CrossRef]
  15. Caruso, G.; Tozzini, L.; Rallo, G.; Primicerio, J.; Moriondo, M.; Palai, G.; Gucci, R. Estimating Biophysical and Geometrical Parameters of Grapevine Canopies (‘Sangiovese’) by an Unmanned Aerial Vehicle (UAV) and VIS-NIR Cameras. Vitis J. Grapevine Res. 2017, 56, 63–70. [Google Scholar] [CrossRef]
  16. Dong, Z.; Yao, L.; Bao, Y.; Zhang, J.; Yao, F.; Bai, L.; Zheng, P. Prediction of Soil Organic Carbon Content in Complex Vegetation Areas Based on CNN-LSTM Model. Land 2024, 13, 915. [Google Scholar] [CrossRef]
  17. Beisekenov, N.; Banakinaou, W.; Ajayi, A.D.; Hasegawa, H.; Tadao, A. Remote Sensing-Based Soil Organic Carbon Monitoring Using Advanced Machine Learning Techniques under Conservation Agriculture Systems. Smart Agric. Technol. 2025, 11, 101036. [Google Scholar] [CrossRef]
  18. Swoish, M.; Da Cunha Leme Filho, J.F.; Reiter, M.S.; Campbell, J.B.; Thomason, W.E. Comparing Satellites and Vegetation Indices for Cover Crop Biomass Estimation. Comput. Electron. Agric. 2022, 196, 106900. [Google Scholar] [CrossRef]
  19. Farias, G.D.; Bremm, C.; Bredemeier, C.; de Lima Menezes, J.; Alves, L.A.; Tiecher, T.; de Faccio Carvalho, P.C. Normalized Difference Vegetation Index (NDVI) for Soybean Biomass and Nutrient Uptake Estimation in Response to Production Systems and Fertilization Strategies. Front. Sustain. Food Syst. 2023, 6, 959681. [Google Scholar] [CrossRef]
  20. Buivydaitė, V.V.; Vaičys, M.; Motuzas, A.J. Lithuanian Soil Classification; Lietuvos Mokslas: Vilnius, Lithuania, 2001; p. 139. ISBN 9986795118. [Google Scholar]
  21. Nițu, A.; Florea, C.; Ivanovici, M.; Racoviteanu, A. NDVI and Beyond: Vegetation Indices as Features for Crop Recognition and Segmentation in Hyperspectral Data. Sensors 2025, 25, 3817. [Google Scholar] [CrossRef]
  22. Addinsoft. XLSTAT Statistical Software, Retrieved 26 October 2025; XLSTAT: Paris, France, 2025. Available online: https://www.xlstat.com (accessed on 7 October 2025).
  23. Tamás, A.; Kovács, E.; Horváth, É.; Juhász, C.; Radócz, L.; Rátonyi, T.; Ragán, P. Assessment of NDVI Dynamics of Maize (Zea mays L.) and Its Relation to Grain Yield in a Polyfactorial Experiment Based on Remote Sensing. Agriculture 2023, 13, 689. [Google Scholar] [CrossRef]
  24. Maresma, Á.; Ariza, M.; Martínez, E.; Lloveras, J.; Martínez-Casasnovas, J.A. Analysis of Vegetation Indices to Determine Nitrogen Application and Yield Prediction in Maize (Zea mays L.) from a Standard Uav Service. Remote Sens. 2016, 8, 973. [Google Scholar] [CrossRef]
  25. Al-Musawi, Z.K.; Vona, V.; Kulmány, I.M. Utilizing Different Crop Rotation Systems for Agricultural and Environmental Sustainability: A Review. Agronomy 2025, 15, 1966. [Google Scholar] [CrossRef]
  26. Atanasov, A.I.; Atanasov, A.Z.; Evstatiev, B.I. A Remote Sensing Approach for Biomass Assessment in Winter Wheat Using the NDVI Second Derivative in Terms of NIR. Sustainability 2025, 17, 7299. [Google Scholar] [CrossRef]
  27. Rossi, A.; Tavarini, S.; Tognoni, M.; Angelini, L.G.; Clemente, C.; Caturegli, L. Reliable NDVI Estimation in Wheat Using Low-Cost UAV-Derived RGB Vegetation Indices. Smart Agric. Technol. 2025, 12, 101452. [Google Scholar] [CrossRef]
  28. Cabrera-Bosquet, L.; Molero, G.; Stellacci, A.; Bort, J.; Nogués, S.; Araus, J. NDVI as a Potential Tool for Predicting Biomass, Plant Nitrogen Content and Growth in Wheat Genotypes Subjected to Different Water and Nitrogen Conditions. Cereal Res. Commun. 2011, 39, 147–159. [Google Scholar] [CrossRef]
  29. Barboza, T.O.C.; Ardigueri, M.; Souza, G.F.C.; Ferraz, M.A.J.; Gaudencio, J.R.F.; dos Santos, A.F. Performance of Vegetation Indices to Estimate Green Biomass Accumulation in Common Bean. AgriEngineering 2023, 5, 840–854. [Google Scholar] [CrossRef]
  30. Jennewein, J.S.; Davis, B.W.; Seehaver-Eagen, S.; Nicolette, J.; Pittman, J.; Hively, W.D.; Goldsmith, A.; Hidalgo, C.; Reberg-Horton, C.; Mirsky, S.B. Multi-Sensor Proximal Remote Sensing for Cover Crop Biomass Estimation at High and Moderate Spatial Resolutions. Smart Agric. Technol. 2025, 12, 101201. [Google Scholar] [CrossRef]
  31. Meng, J.; Du, X.; Wu, B. Generation of High Spatial and Temporal Resolution NDVI and Its Application in Crop Biomass Estimation. Int. J. Digit. Earth 2013, 6, 203–218. [Google Scholar] [CrossRef]
  32. Dal Lago, P.; Vavlas, N.; Kooistra, L.; De Deyn, G.B. Estimation of Nitrogen Uptake, Biomass, and Nitrogen Concentration, in Cover Crop Monocultures and Mixtures from Optical UAV Images. Smart Agric. Technol. 2024, 9, 100608. [Google Scholar] [CrossRef]
  33. Miller, J.O.; Shober, A.L.; Taraila, J. Assessing Relationships of Cover Crop Biomass and Nitrogen Content to Multispectral Imagery. Agron. J. 2024, 116, 1417–1427. [Google Scholar] [CrossRef]
  34. Poudel, A.; Burns, D.; Adhikari, R.; Duron, D.; Hendrix, J.; Gentimis, T.; Tubana, B.; Setiyono, T. Cover Crop Biomass Predictions with Unmanned Aerial Vehicle Remote Sensing and TensorFlow Machine Learning. Drones 2025, 9, 131. [Google Scholar] [CrossRef]
  35. Moreno-Cadena, P.; Salmeron, M.; Canisares, L.P.; Poffenbarger, H.J. Productivity Benefits of Cereal-Legume Cover Crop Mixtures under Variable Soil Nitrogen and Termination Times. Eur. J. Agron. 2024, 155, 127114. [Google Scholar] [CrossRef]
  36. Zani, C.F.; Manning, D.A.C.; Abbott, G.D.; Taylor, J.A.; Cooper, J.; Lopez-Capel, E. Diversified Crop Rotations and Organic Amendments as Strategies for Increasing Soil Carbon Storage and Stabilisation in UK Arable Systems. Front. Environ. Sci. 2023, 11, 26. [Google Scholar] [CrossRef]
  37. Tan, G.; Liu, Y.; Peng, S.; Yin, H.; Meng, D.; Tao, J.; Gu, Y.; Li, J.; Yang, S.; Xiao, N.; et al. Soil Potentials to Resist Continuous Cropping Obstacle: Three Field Cases. Environ. Res. 2021, 200, 111319. [Google Scholar] [CrossRef]
  38. Kartini, N.L.; Saifulloh, M.; Trigunasih, N.M.; Sukmawati, N.M.S.; Mega, I.M. Impact of Long-Term Continuous Cropping on Soil Nutrient Depletion. Ecol. Eng. Environ. Technol. 2024, 25, 18–29. [Google Scholar] [CrossRef]
  39. Liang, X.; Yu, S.; Ju, Y.; Wang, Y.; Yin, D. Integrated Management Practices Foster Soil Health, Productivity, and Agroecosystem Resilience. Agronomy 2025, 15, 1816. [Google Scholar] [CrossRef]
  40. Vajja, N.R.; Meinke, H.; Kropff, M.J.; Anten, N.P.; Whitbread, A.M.; Kumar, U.; Parsons, D. Incorporating Knowledge of Allelopathic Interactions Can Improve Productivity and Sustainability of Crop Rotations in the Semi-Arid Tropics. J. Agric. Food Res. 2025, 22, 102026. [Google Scholar] [CrossRef]
  41. Olofsdotter, M.; Jensen, L.B.; Courtois, B. Improving Crop Competitive Ability Using Allelopathy—An Example from Rice. Plant Breed. 2002, 121, 1–9. [Google Scholar] [CrossRef]
  42. Rabot, E.; Wiesmeier, M.; Schlüter, S.; Vogel, H.J. Soil Structure as an Indicator of Soil Functions: A Review. Geoderma 2018, 314, 122–137. [Google Scholar] [CrossRef]
  43. Horel, Á.; Zsigmond, T.; Farkas, C.; Gelybó, G.; Tóth, E.; Kern, A.; Bakacsi, Z. Climate Change Alters Soil Water Dynamics under Different Land Use Types. Sustainability 2022, 14, 3908. [Google Scholar] [CrossRef]
  44. Bao, G.; Qin, Z.; Bao, Y.; Zhou, Y.; Li, W.; Sanjjav, A. NDVI-Based Long-Term Vegetation Dynamics and Its Response to Climatic Change in the Mongolian Plateau. Remote Sens. 2014, 6, 8337–8358. [Google Scholar] [CrossRef]
  45. Wang, S.; Fu, G. Modelling Soil Moisture Using Climate Data and Normalized Difference Vegetation Index Based on Nine Algorithms in Alpine Grasslands. Front. Environ. Sci. 2023, 11, 1130448. [Google Scholar] [CrossRef]
  46. Horn, R.; Domzzał, H.; Słowińska-Jurkiewicz, A.; van Ouwerkerk, C. Soil Compaction Processes and Their Effects on the Structure of Arable Soils and the Environment. Soil Tillage Res. 1995, 35, 23–36. [Google Scholar] [CrossRef]
  47. Lynch, J.P.; Mooney, S.J.; Strock, C.F.; Schneider, H.M. Future Roots for Future Soils. Plant Cell Environ. 2022, 45, 620–636. [Google Scholar] [CrossRef]
  48. Serrano-Grijalva, L.; Ochoa-Hueso, R.; Veen, G.F.; Repeto-Deudero, I.; Van Rijssel, S.Q.; Koorneef, G.J.; Van der Putten, W.H. Normalized Difference Vegetation Index Analysis Reveals Increase of Biomass Production and Stability during the Conversion from Conventional to Organic Farming. Glob. Change Biol. 2024, 30, e17461. [Google Scholar] [CrossRef]
  49. Peltonen-Sainio, P.; Jauhiainen, L.; Honkavaara, E.; Wittke, S.; Karjalainen, M.; Puttonen, E. Pre-Crop Values from Satellite Images for Various Previous and Subsequent Crop Combinations. Front. Plant Sci. 2019, 10, 462. [Google Scholar] [CrossRef] [PubMed]
  50. Girz, A.I.; Mattila, T.J. Within-Field Variation of Crop Yield Loss from Cover Crops. Agron. J. 2024, 116, 2922–2933. [Google Scholar] [CrossRef]
  51. Reuter, T.; Nahrstedt, K.; Jarmer, T.; Trautz, D. Site-Specific Management Zones for Crop Rotation Based on NDVI Images of Clover-Grass Fields. In Proceedings of the XXII N Workshop 2024, Aarhus, Denmark, 17–21 June 2024. [Google Scholar]
  52. Papadopoulos, G.; Zafeiriou, I.; Georgiou, E.; Oikonomou, A.; Mavroeidis, A.; Stavropoulos, P.; Kakabouki, I.; Fountas, S.; Bilalis, D. Remote Sensing Meets Agronomy: A Three-Year Field Study of Tritordeum’s Response to Enhanced Efficiency Fertilisers. Agronomy 2025, 15, 2244. [Google Scholar] [CrossRef]
  53. Yuan, M.; Burjel, J.C.; Martin, N.F.; Isermann, J.; Goeser, N.; Pittelkow, C.M. Advancing On-Farm Research with UAVs: Cover Crop Effects on Crop Growth and Yield. Agron. J. 2021, 113, 1071–1083. [Google Scholar] [CrossRef]
  54. Govaerts, B.; Verhulst, N. The Normalized Difference Vegetation Index (NDVI) Greenseeker(TM) Handheld Sensor: Toward the Integrated Evaluation of Crop Management Part A: Concepts and Case Studies; CIMMYT: Texcoco, Mexico, 2012. [Google Scholar]
  55. Panek, E.; Gozdowski, D. Relationship between Modis Derived Ndvi and Yield of Cereals for Selected European Countries. Agronomy 2021, 11, 340. [Google Scholar] [CrossRef]
  56. Panek-Chwastyk, E.; Paradowski, K.; Rutkowska, B.; Szulc, W.; Dzierżanowski, I. Advancing Early-Stage Plant Phosphorus Assessment for Winter Rye via Hyperspectral Data: A Model-Based Approach Harnessing Feedforward Neural Networks. Eur. J. Agron. 2025, 169, 127667. [Google Scholar] [CrossRef]
  57. Feng, D.; Yang, H.; Gao, K.; Jin, X.; Li, Z.; Nie, C.; Zhang, G.; Fang, L.; Zhou, L.; Guo, H.; et al. Time-Series NDVI and Greenness Spectral Indices in Mid-to-Late Growth Stages Enhance Maize Yield Estimation. Field Crops Res. 2025, 333, 110069. [Google Scholar] [CrossRef]
  58. Perry, E.; Sheffield, K.; Crawford, D.; Akpa, S.; Clancy, A.; Clark, R. Spatial and Temporal Biomass and Growth for Grain Crops Using NDVI Time Series. Remote Sens. 2022, 14, 3071. [Google Scholar] [CrossRef]
Applsci 15 11491 g001
Figure 1. Normalized Difference Vegetation Index (NDVI) of winter rye at the BBCH 61–69 growth stage under different crop rotations during 2023–2025. Treatments: rye monoculture (MONO); rye monoculture with fertilizers and herbicides (MONOFH); intensive rotation (INT); three-course rotation (TC); field with row crops (FWR); field without row crops (FR); and rotation for green manure/sideration (SI). Different letters above the bars indicate significant differences between treatments according to Tukey’s HSD test (p < 0.05). Error bars represent the standard error of the mean, indicating the precision of the estimated mean across replicates (n = 3).
Figure 1. Normalized Difference Vegetation Index (NDVI) of winter rye at the BBCH 61–69 growth stage under different crop rotations during 2023–2025. Treatments: rye monoculture (MONO); rye monoculture with fertilizers and herbicides (MONOFH); intensive rotation (INT); three-course rotation (TC); field with row crops (FWR); field without row crops (FR); and rotation for green manure/sideration (SI). Different letters above the bars indicate significant differences between treatments according to Tukey’s HSD test (p < 0.05). Error bars represent the standard error of the mean, indicating the precision of the estimated mean across replicates (n = 3).
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Figure 2. Normalized Difference Vegetation Index (NDVI) of winter rye at the BBCH 71–79 growth stage under different crop rotations during 2023–2025. Treatments: rye monoculture (MONO); rye monoculture with fertilizers and herbicides (MONOFH); intensive rotation (INT); three-course rotation (TC); field with row crops (FWR); field without row crops (FR); and rotation for green manure/sideration (SI). Different letters above the bars indicate significant differences between treatments according to Tukey’s HSD test (p < 0.05). Error bars represent the standard error of the mean, indicating the precision of the estimated mean across replicates (n = 3).
Figure 2. Normalized Difference Vegetation Index (NDVI) of winter rye at the BBCH 71–79 growth stage under different crop rotations during 2023–2025. Treatments: rye monoculture (MONO); rye monoculture with fertilizers and herbicides (MONOFH); intensive rotation (INT); three-course rotation (TC); field with row crops (FWR); field without row crops (FR); and rotation for green manure/sideration (SI). Different letters above the bars indicate significant differences between treatments according to Tukey’s HSD test (p < 0.05). Error bars represent the standard error of the mean, indicating the precision of the estimated mean across replicates (n = 3).
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Figure 3. Normalized Difference Vegetation Index (NDVI) of winter rye at the BBCH 83–89 growth stage under different crop rotations during 2023–2025. Treatments: rye monoculture (MONO); rye monoculture with fertilizers and herbicides (MONOFH); intensive rotation (INT); three-course rotation (TC); field with row crops (FWR); field without row crops (FR); and rotation for green manure/sideration (SI). Different letters above the bars indicate significant differences between treatments according to Tukey’s HSD test (p < 0.05). Error bars represent the standard error of the mean, indicating the precision of the estimated mean across replicates (n = 3).
Figure 3. Normalized Difference Vegetation Index (NDVI) of winter rye at the BBCH 83–89 growth stage under different crop rotations during 2023–2025. Treatments: rye monoculture (MONO); rye monoculture with fertilizers and herbicides (MONOFH); intensive rotation (INT); three-course rotation (TC); field with row crops (FWR); field without row crops (FR); and rotation for green manure/sideration (SI). Different letters above the bars indicate significant differences between treatments according to Tukey’s HSD test (p < 0.05). Error bars represent the standard error of the mean, indicating the precision of the estimated mean across replicates (n = 3).
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Table 1. Crop rotations with different pre-crops.
Table 1. Crop rotations with different pre-crops.
Crop RotationPre-Crop
Rye monoculture without NPK fertilizers and herbicides (MONO)Rye monoculture without NPK fertilizers and herbicides
Rye monoculture with fertilizers and herbicides (MONOFH)Rye monoculture with fertilizers and herbicides
Intensive (INT)Potatoes
Three-course (TC)Black fallow
Field with row crops (FWR)Perennial grasses (Trifolium pratense L. + Phleum pratense L.) (second year)
Field without row crops (FR)Perennial grasses (Trifolium pratense L. + Phleum pratense L.) (second year)
For green manure/sideration (SI)Winter rape
Table 2. Sources of organic matter in crop rotations.
Table 2. Sources of organic matter in crop rotations.
Crop RotationsOrganic Matter Input in Rotation
ManureStrawGreen ManurePerennial Grasses
Rye monoculture without NPK fertilizers and herbicides (MONO) +
Rye monoculture with NPK fertilizers and herbicides (MONOFH) +
Intensive (INT)++++
Three-course (TC)++
Field with row crops (FWR)++ +
Field without row crops (FR) + +
For green manure/sideration (SI) ++
Table 3. The average temperature and the sum of monthly precipitation during the winter rye growing season (September–August) in 2022–2025.
Table 3. The average temperature and the sum of monthly precipitation during the winter rye growing season (September–August) in 2022–2025.
Year/MonthSep.Oct.Nov.Dec.Jan.Feb.Mar.Apr.MayJun.Jul.Aug.
The sum of monthly precipitation, mm
2022–2023 (1 Y)2617.730.744.166.527.630.826.714.364.036.896.2
2023–2024 (2 Y)11.699.130.447.027.460.213.563.041.636.8109.440.9
2024–2025 (3 Y)40.227.040.835.023.2027.033.8019.9031.5082.30118.0031.30
The average air temperature, °C
2022–2023 (1 Y)11.110.22.9−2.50.90.12.88.512.617.317.920.2
2023–2024 (2 Y)17.18.42.20.53.852.834.478.9115.6917.820.519.67
2024–2025 (3 Y)17.268.653.741.931.98−2.335.19.3610.1915.6919.2316.6
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Dorelis, M.; Vaštakaitė-Kairienė, V.; Bogužas, V. UAV Multispectral Imaging for Multi-Year Assessment of Crop Rotation Effects on Winter Rye. Appl. Sci. 2025, 15, 11491. https://doi.org/10.3390/app152111491

AMA Style

Dorelis M, Vaštakaitė-Kairienė V, Bogužas V. UAV Multispectral Imaging for Multi-Year Assessment of Crop Rotation Effects on Winter Rye. Applied Sciences. 2025; 15(21):11491. https://doi.org/10.3390/app152111491

Chicago/Turabian Style

Dorelis, Mindaugas, Viktorija Vaštakaitė-Kairienė, and Vaclovas Bogužas. 2025. "UAV Multispectral Imaging for Multi-Year Assessment of Crop Rotation Effects on Winter Rye" Applied Sciences 15, no. 21: 11491. https://doi.org/10.3390/app152111491

APA Style

Dorelis, M., Vaštakaitė-Kairienė, V., & Bogužas, V. (2025). UAV Multispectral Imaging for Multi-Year Assessment of Crop Rotation Effects on Winter Rye. Applied Sciences, 15(21), 11491. https://doi.org/10.3390/app152111491

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