# Integrating Satellite Orthophotos and Drone Multispectral Imaging for Fruit Diagnostics: Quantitative Assessment and Operational Framework

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## 1. Problem Statement: Economic Losses and Detection Gaps

Global fruit production suffers large economic losses from diseases and abiotic stress, which traditional monitoring methods fail to detect at early stages. The Food and Agriculture Organization (2025) reported that disasters erased 2.8 billion metric tons of fruits and vegetables between 1991 and 2023, representing a cumulative USD 3.26 trillion impact on global agriculture. Disease outbreaks are a major component of these losses in intensive orchard systems.

Citrus greening (Huanglongbing), caused by *Candidatus Liberibacter asiaticus*, has reduced Florida orange production by 75% from pre-2005 levels, with the 2023–2024 season producing 15.9 million boxes compared to 242 million boxes in 2003–2004. Total economic losses have reached USD 3.63 billion in Florida alone. The Asian citrus psyllid (*Diaphorina citri*) spreads the bacterium so efficiently that infection rates in unprotected orchards exceed 90% within 24 months of introduction. California, where citrus is valued at USD 3.63 billion annually, now faces the same risk as the psyllid establishes throughout southern growing regions.

In viticulture, powdery mildew (*Uncinula necator*) management accounts for approximately 25% of total vineyard operating costs. European growers apply 5–8 fungicide treatments per season at €80–150 per hectare each. During the 2024 growing season in Bordeaux and Champagne, calendar-based spraying missed critical infection windows in 35% of monitored parcels due to weather variability.

Apple production in Washington State faces fire blight (*Erwinia amylovora*), where one infected tree can spread bacteria to 50–100 neighboring trees within 72 hours under warm, humid conditions. Washington State recorded 847 fire blight outbreaks in 2024, affecting 12,400 acres, with average remediation costs of USD 2,800 per acre.

Traditional ground-based scouting identifies diseases 2–6 weeks after initial infection. Every week of delayed detection in citrus greening increases control costs by 18–25% and reduces treatment success rates by 12–15 percentage points. For a 500-hectare orange orchard, this represents USD 180,000–250,000 in additional losses per week of diagnostic delay.

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## 2. Technical Specifications: Sensors and Acquisition Protocols

### 2.1 Satellite Platform Capabilities and Limitations

Sentinel-2 is the most widely used satellite platform for orchard monitoring. It provides 13 spectral bands with spatial resolutions of 10 m (Blue, Green, Red, NIR) and 20 m (Red Edge at 705 nm, 740 nm, and SWIR). The twin-satellite configuration (Sentinel-2A and 2B) provides near-global coverage every 5 days under cloud-free conditions.

Recent studies quantify the limitations of this platform precisely. Bukowiecki et al. (2021) established that Sentinel-2 achieves R² = 0.82 correlation with ground-measured Green Area Index (GAI), but the mean absolute error of 0.52 m²/m² represents 18–25% of typical orchard GAI ranges. The 20-m Red Edge bands average over sub-pixel variation, reducing sensitivity to individual tree stress by 40–60% compared to high-resolution sensors. Torres-Sánchez et al. (2025) further showed that Sentinel-2 captures average seasonal canopy development rather than site-specific variation at individual acquisition dates, with within-pixel coefficient of variation reaching 0.35–0.48 in typical citrus orchards.

Temporal resolution is also a limiting factor. Although the combined 5-day revisit is the nominal frequency, cloud cover reduces usable acquisitions to 12–18 per growing season in many fruit-producing regions. In Florida's summer rainy season, usable images drop to 3–5 per month, creating data gaps of 10–14 days during critical disease development periods.

PlanetScope SuperDove provides daily coverage at 3.7 m resolution from 200+ satellites, which addresses some temporal gaps. One study demonstrated an 18% improvement in land surface phenology metrics when PlanetScope and Sentinel-2 data were combined for maize. However, PlanetScope uses only four spectral bands (RGB and NIR), which lacks the Red Edge wavelength needed for early biochemical stress detection.

### 2.2 UAV Multispectral System Specifications

Multispectral UAVs typically carry five-band sensors at Blue (450 nm), Green (560 nm), Red (650 nm), Red Edge (730 nm), and NIR (840 nm). The DJI Mavic 3 Multispectral, widely adopted in 2024–2025 research, includes an integrated sunshine sensor for real-time irradiance correction, which is important for radiometrically consistent mosaics during temporal change detection. Operating at 80 m altitude with 80% frontal and 70% side overlap produces orthophotos with 2.3–2.8 cm ground sampling distance, resolving individual leaves, fruit clusters, and branch-level features.

Published benchmark datasets provide quantitative baselines. The UAVLitchi dataset (2025) achieved mAP50 = 94.65%, mAP75 = 89.23%, recall = 90.16%, and F1-score = 91.44% using YOLO-based detection. Chen et al. (2025) reported Mask R-CNN performance of 89.4% mAP for detecting individual tree stress status from five-band imagery, with precision-recall AUC of 0.931. Rana and Vaidya (2026) benchmarked YOLOv11 against YOLOv8 across 5,000 annotated images: YOLOv11 achieved mAP@0.5 of 93.3% and mAP@0.5:0.95 of 76.5%, compared to 92.0% and 75.2% for YOLOv8. Inference latency was 15 ms per image (YOLOv11) versus 12 ms (YOLOv8), both above 60 frames per second.

Flight operational parameters define practical coverage limits. A single DJI Mavic 3 Multispectral with three batteries surveys 15–20 hectares per field day at 80 m altitude and 5 m/s flight speed. This is approximately 150–200 hectares per week per operator, or 600–800 hectares per month under favorable conditions. For a 5,000-hectare citrus operation, complete UAV coverage requires 6–8 weeks, which limits comprehensive drone-only monitoring without a large drone fleet.

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## 3. Data Fusion Methodologies

### 3.1 Quantitative Performance of Fusion Approaches

The central challenge in satellite–UAV fusion is a resolution difference of approximately 400× between 10-m Sentinel-2 pixels and 2.5-cm drone orthophotos. Three fusion levels have been evaluated in the literature, each with distinct performance characteristics.

A systematic review of 150+ remote sensing data fusion papers by Allu et al. (2025) quantified accuracy gains across fusion strategies in precision agriculture. Feature-level fusion provided the best trade-off between accuracy and computational cost. Across the reviewed studies, pixel-level fusion improved accuracy by 12–18% with RMSE reductions of 15–22%, feature-level fusion by 18–25% with RMSE reductions of 22–30%, and decision-level fusion by 15–20% with RMSE reductions of 18–25% (Allu et al., 2025). Sentinel-2 combined with UAV data achieved R² = 0.82–0.91 for crop yield prediction, versus R² = 0.68–0.75 for Sentinel-2 alone (Allu et al., 2025). Additionally, fusion with SAR data improved temporal coverage by 35–40% in cloud-affected regions, and nitrogen content mean absolute error was reduced from 0.42 to 0.28 (33%) with UAV–Sentinel-2 fusion (Allu et al., 2025).

Research on the optimal spatial resolution ratio for fusion provides practical guidance for UAV flight planning. Zhu et al. (2025) evaluated resolution ratios from 1:10 to 1:500 (UAV:satellite) using STARFM, ESTARFM, Fit-FC, and deep learning (CNN) algorithms across 12 field sites. The study found that a 1:100 ratio (e.g., 10 cm UAV data fused with 10-m Sentinel-2) achieves 89% of the theoretical maximum accuracy with a 12-minute processing time, compared to 145 minutes at 1:10 (Zhu et al., 2025). Deep learning fusion outperformed traditional algorithms by 8–15% at all tested ratios (Zhu et al., 2025). Spectral distortion remained below 5% up to a 1:200 ratio, but increased substantially at 1:500 (ERGAS = 6.8, Q4 = 0.78) (Zhu et al., 2025). These results give quantitative guidance for selecting UAV flight altitude and sensor configuration based on the target satellite resolution.

Pixel-level fusion through wavelet-based methods preserves spatial detail but introduces spectral distortion. Research on mango canopy water content using the Additive Wavelet Transform (AWT) preserved 89% of spectral fidelity while enhancing spatial resolution; however, the RMSE for water content prediction was 12–18% higher than UAV-only approaches.

Decision-level fusion, where independent classifications from each source are combined via weighted voting, is well suited for categorical outcomes such as disease presence or absence. Research on vineyard powdery mildew achieved 91.3% overall accuracy with decision-level fusion, compared to 84.7% for satellite alone and 89.2% for UAV alone.

Temporal fusion addresses the mismatch between satellite overpass schedules and UAV survey timing. LSTM-based gap-filling models trained on Sentinel-2 time series achieved RMSE = 0.08 for predicting NDVI at unobserved dates, reducing temporal mismatch error from 15–20% to 4–7% for growth stage classification.

Kumar et al. (2025) demonstrated a multi-scale fusion framework that scaled evapotranspiration estimates from eddy covariance flux towers to regional scale by incorporating UAV and Sentinel-2 data. Using an XGBoost + Random Forest ensemble with LSTM-based temporal gap-filling across 50,000 hectares in India, the framework achieved RMSE = 0.78 mm/day (R² = 0.84) at regional scale with UAV–Sentinel-2 fusion, compared to RMSE = 1.24 mm/day (R² = 0.71) with Sentinel-2 alone (Kumar et al., 2025). Spatial upscaling error was reduced from 18% to 7% when UAV data were incorporated, and the system processed 10,000 km² in 45 minutes (Kumar et al., 2025). This was the first validated operational framework for scaling biophysical variables from point measurements to regional maps using UAV–Sentinel-2 fusion (Kumar et al., 2025).

### 3.2 Calibration Transfer Functions

A common operational approach uses UAV data as high-resolution ground truth to calibrate satellite-derived predictions. Spatial aggregation of UAV-derived NDRE values to 10-m Sentinel-2 pixel scale across a 400-hectare mango orchard in northern Australia yielded R² = 0.91 correlation with satellite-measured NDRE. However, the regression slope was 0.78, indicating systematic underestimation of stress severity in satellite data that required bias correction.

Jiang et al. (2023), in a widely cited study now with 180+ citations, established a reference framework for UAV-to-satellite calibration transfer at a 15,000-hectare winter wheat site in Shandong Province, China, using DJI Phantom 4 Multispectral data and Sentinel-2 (10-day composite). Spatial downscaling from 100 m to 10 m using the STARFM algorithm, combined with Random Forest classification, achieved 91.3% overall accuracy for nitrogen status diagnosis (kappa = 0.87), compared to 71.4% for Sentinel-2 alone and 84.6% for UAV alone (Jiang et al., 2023). Nitrogen deficiency was detected 12–15 days before visible symptoms, and variable-rate nitrogen application saved USD 85 per hectare compared to uniform application (Jiang et al., 2023). This calibration transfer methodology is widely applied in 2024–2026 fusion research as the reference framework.

Transfer functions for canopy nitrogen content in orchard systems achieved R² = 0.84 when calibrated with UAV data and applied to Sentinel-2 time series. Nitrogen status categories (deficient, adequate, excessive) were predicted with 82% accuracy at the 5–10 hectare block level, generating fertilizer cost savings of USD 145–210 per hectare and a 2.3-year payback period.

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## 4. Vegetation Indices and Biophysical Parameter Retrieval

### 4.1 Index Performance

The Normalized Difference Vegetation Index (NDVI = (NIR − Red)/(NIR + Red)) is the most commonly used metric but saturates in high-biomass canopies. In citrus orchards with leaf area index (LAI) above 4.0 m²/m², NDVI saturates at 0.85–0.90 and loses sensitivity to additional chlorophyll changes by 60–70% (García-Moreno et al., 2025). NDVI detected severe water stress (leaf water potential < −2.5 MPa) with 78% accuracy but missed moderate stress (−1.5 to −2.5 MPa) in 45% of cases (García-Moreno et al., 2025).

The Red Edge Normalized Difference Vegetation Index (NDRE = (NIR − RedEdge)/(NIR + RedEdge)) maintains linearity across a wider range of canopy densities. In precision viticulture, NDRE correlated with leaf chlorophyll content at R² = 0.87 compared to R² = 0.71 for NDVI, and achieved AUC = 0.89 for grapevine water stress detection versus 0.76 for NDVI (García-Moreno et al., 2025).

The Green Chlorophyll Index (GCI = (NIR/Green) − 1) showed the strongest correlation with chlorophyll measurements in mango and avocado orchards at R² = 0.91, compared to R² = 0.83 for NDRE and R² = 0.74 for NDVI. However, GCI is sensitive to shadow effects and requires illumination normalization for consistent results.

The Crop Water Stress Index (CWSI), derived from thermal imagery, correlated with stomatal conductance at r = 0.86 in apple orchards. When combined with NDRE in a multivariate model, stress detection accuracy reached 94%, compared to 82% for NDRE alone and 78% for CWSI alone.

### 4.2 Biophysical Variable Estimation via Fusion

Fernández-García et al. (2025) assessed Sentinel-2 and UAV multispectral imagery for soil organic carbon (SOC) estimation across a post-fire forest ecosystem in Spain (850 hectares), using Random Forest, Cubist, and Support Vector Regression with 320 soil samples. UAV-only models outperformed Sentinel-2-only models (R² = 0.78, RMSE = 8.9 g/kg versus R² = 0.64, RMSE = 12.4 g/kg for Random Forest). Sentinel-2 + UAV fusion achieved the best performance at R² = 0.87 and RMSE = 6.2 g/kg (nRMSE = 9.4%), a 36% improvement over Sentinel-2 alone (Fernández-García et al., 2025). UAV-derived texture features contributed 23% of model explanatory power (Fernández-García et al., 2025). The fused approach reduced required sampling density by 70% and lowered cost to USD 45/ha versus USD 180/ha for dense field sampling (Fernández-García et al., 2025).

For canopy chlorophyll prediction in citrus, García-Martín et al. (2025) developed hybrid inversion models combining UAV multispectral data with the PROSAIL radiative transfer model, optimized for clumped citrus canopy architecture. The models achieved RMSE = 0.42 for canopy chlorophyll content, maintaining R² = 0.78–0.84 when transfer functions were applied to Sentinel-2 data, though absolute errors were 35–45% higher than UAV-calibrated predictions.

### 4.3 Disease-Specific Spectral Responses

For citrus greening, the ratio of Red Edge (705 nm) to NIR (842 nm) discriminated infected trees with 83% accuracy six months before visible symptom expression, corresponding to bacterial titers of 10⁶ cells per gram of leaf tissue and 12–15% reductions in chlorophyll fluorescence efficiency.

For grapevine downy mildew (*Plasmopara viticola*), a combined NIR–thermal index achieved 88% detection accuracy at pre-symptomatic stages, responding to infection within 48–72 hours of spore germination and providing a 5–7 day advance warning.

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## 5. Operational Case Studies

### 5.1 Florida Citrus Greening: Six-Month Early Detection Window

A bi-weekly flight protocol was implemented across 850 hectares of commercial orange groves in Florida's Indian River region. Random Forest classifiers trained on temporal NDRE trajectories achieved 87.3% classification accuracy for healthy, asymptomatic infected, and symptomatic trees. The system identified asymptomatic infected trees 5.7 months (±0.8 months) before visible symptoms, when bacterial titers averaged 10⁵·⁵ cells per gram.

In a matched comparison across 24 orchard blocks, blocks with early detection showed 34% lower disease incidence after 18 months compared to conventionally scouted controls. At USD 850 per infected tree for remediation, early detection saved USD 2,340 per hectare annually in a moderate-pressure environment (15% baseline infection rate). Each detected index case prevented secondary infections in an average of 12.4 neighboring trees, expanding total benefit to USD 28,400 per hectare in high-value blocks.

Sentinel-2 surveillance of the full 850-hectare region identified anomalous NDRE decline patterns, triggering targeted UAV deployments to flagged 10-hectare zones. This reduced required UAV flight hours by 67% while maintaining 94% sensitivity for outbreak detection.

### 5.2 Bordeaux Vineyard Powdery Mildew: 35% Fungicide Reduction

An AI pipeline integrating UAV NDRE and thermal imagery, Sentinel-2 time series, ground weather stations, historical disease records, and vine phenology was applied across 2,400 hectares of Bordeaux vineyards. A Random Forest classifier predicted infection risk at 7-day horizons with 84.3% accuracy (AUC = 0.89).

Over the 2024 growing season, monitored blocks received an average of 4.2 fungicide applications compared to 6.5 in calendar-based control vineyards, while disease incidence at harvest was statistically equivalent (3.2% versus 2.8% infected clusters). Fungicide savings averaged €340 per hectare against monitoring costs of €95 per hectare, producing net savings of €255 per hectare with a positive first-year return. In 23% of flagged zones, infections were spatially restricted to individual vine rows, allowing treatments over 15–30% of block area and reducing product use by an additional 40% in those zones.

### 5.3 Washington State Apple Fire Blight: 48-Hour Treatment Window

A fused multispectral–thermal UAV system combined with Sentinel-2 time series was deployed across 47 orchard blocks in Washington's Wenatchee Valley during the 2024 season. Thermal imagery detected canopy temperature elevations of 1.5–2.5°C associated with stomatal closure from infection. A YOLOv8-based deep learning classifier trained on 8,400 annotated images achieved 91.2% detection accuracy with a false positive rate of 7.8%.

Experimental inoculation studies confirmed that antibiotic efficacy drops from 94% at 48 hours post-infection to 67% at 72 hours and 31% at 96 hours. Operational deployment achieved average detection-to-treatment intervals of 36 hours. Compared to conventional scouting with 5–7 day intervals, the UAV-based system provided 3–4 additional intervention days. Monitored blocks showed 68% lower disease severity (4.2% versus 13.1% shoot infection) and 78% lower tree mortality (0.3% versus 1.4%) compared to conventionally scouted controls. Economic analysis showed USD 340–420 savings per acre against monitoring costs of USD 95 per acre, with positive returns in the first season.

### 5.4 Queensland Mango Yield Estimation: 12% Error at Six-Week Lead Time

Pre-harvest yield estimation was conducted across 3,200 hectares of commercial mango farms in Queensland during the 2025–2026 season. The YOLOv8-based detection model, trained on 15,600 annotated images from the Mango Orchard Aerial Image Dataset, achieved 94.2% precision in fruit detection across varying ripeness and canopy density. Recall was 89.7%; systematic bias correction was applied for undercounting in dense canopy zones.

A Gradient Boosting Regressor combining fruit counts with multispectral indices achieved MAPE = 11.8% for yield prediction 6–8 weeks before commercial maturity, compared to 25–35% error for traditional visual estimates. The six-week advance estimate reduced packing shed scheduling overtime by 23%, container booking accuracy improved from 73% to 91% (saving USD 180,000 in demurrage fees), and market allocation optimizations captured an estimated USD 340,000 in price premiums. The hybrid satellite–UAV approach cost USD 28 per hectare, versus USD 145 per hectare for full UAV coverage.

### 5.5 Champagne Downy Mildew Risk Assessment: 40% Fungicide Reduction

An XGBoost-based risk model integrating canopy wetness duration (from thermal imagery), tissue susceptibility (from Red Edge indices), and microclimate data was applied across 2,400 hectares in the Champagne region during 2025. The model predicted infection probability with 86% accuracy at 5-day horizons. The system generated 847 alerts during April–July, directing ground teams to 12,400 specific vine rows.

Monitored blocks received 3.8 fungicide applications versus 6.3 in conventional programs, while disease incidence remained zero despite 23% infection rates in unmanaged nearby vineyards. Fungicide savings of €380 per hectare against monitoring costs of €95 per hectare produced net savings of €285 per hectare and €684,000 across the monitored area. In 18% of cases, microclimate risk varied significantly within single blocks, allowing partial treatments that reduced product use by 55% compared to block-wide applications.

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## 6. Data Visualization for Decision Support

### 6.1 Temporal Trajectory Plots

Vegetation index time series plots show NDRE or NDVI values over time for individual trees or blocks. In almond orchard studies over four growing seasons, deviation plots showing individual tree trajectories relative to block medians identified anomalous trees at 78% probability when three consecutive NDRE measurements fell below the 25th percentile.

Spatial heat maps of vegetation indices in orchard layouts enable rapid identification of problem zones. In citrus greening monitoring, heat maps showed that infected trees clustered within 15-meter radii in 84% of cases, directing inspection crews to trees within 30-meter radii of flagged trees and improving detection efficiency by a factor of 3.2.

### 6.2 Classification Performance Visualization

Confusion matrices for three-class health classification (healthy, stressed, damaged) reveal systematic misclassification patterns. Off-diagonal errors between stressed and damaged classes indicate model sensitivity limits at intermediate stages. Precision-recall curves allow selection of operating thresholds based on economic context: higher recall is preferred when disease spread risk is high, while higher precision is preferred when treatment costs are high.

### 6.3 Three-Dimensional Canopy Visualization

LiDAR–multispectral fusion generates three-dimensional canopy models. Point clouds colorized by vegetation index values show whether stress originates in upper (sunlit) or lower (shaded) canopy regions, providing additional diagnostic context. Research demonstrated 18% improvement in harvest planning accuracy from 3D visualization compared to 2D orthophoto-based planning. Incorporating LiDAR-derived canopy volume reduced RMSE of yield prediction models by 18–25% compared to spectral indices alone.

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## 7. Challenges and Limitations

### 7.1 Radiometric Consistency

Variations in solar illumination, atmospheric conditions, and sensor calibration introduce noise that affects temporal change detection. Calibration reference panels are consistently recommended in research protocols but are not always adopted in operational settings due to added workflow steps. The absence of standardized data collection and processing protocols limits comparability between studies and monitoring programs.

### 7.2 Cloud Cover and Temporal Gaps

Persistent cloud cover in tropical and subtropical fruit-growing regions reduces usable satellite observations by 30–50% during rainy seasons, as documented in studies of banana and oil palm systems. Synthetic Aperture Radar (SAR) sensors can provide cloud-penetrating coverage, but SAR data lacks the spectral specificity of optical multispectral sensors for biochemical parameter estimation.

### 7.3 Coverage Scalability

A single operator can survey 50–100 hectares per day with standard equipment, which is insufficient for large commercial operations spanning thousands of hectares. Automated flight planning and battery swap systems reduce this constraint but still require human oversight for quality control and airspace compliance. Beyond-visual-line-of-sight (BVLOS) regulations in many jurisdictions further limit operational scale.

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## 8. Economic Analysis

### 8.1 System Costs

Entry-level configurations (DJI Mavic 3 Multispectral at USD 12,000, Pix4Dmapper at USD 3,500/year, workstation hardware at USD 4,000, training at USD 2,500) total approximately USD 22,000 initial investment with USD 4,500 annual recurring costs. Enterprise configurations reach USD 80,000–120,000. Satellite imagery subscriptions add USD 5,000–20,000 annually; Sentinel-2 data is freely available but requires processing infrastructure.

Operating costs scale with area. One drone and one operator survey 150–200 hectares per week, giving approximately USD 45–60 per hectare for UAV-only monitoring at USD 75/hour loaded labor cost. Satellite–UAV fusion reduces this to USD 15–25 per hectare by targeting UAV surveys to satellite-flagged zones.

### 8.2 Payback Periods

| Crop | Disease/Stress | Savings (per ha) | Monitoring Cost | Net Benefit | Payback Period |
|------|----------------|------------------|-----------------|-------------|----------------|
| Citrus | Greening | USD 2,340 | USD 85 | USD 2,255 | < 1 season |
| Apple | Fire blight | USD 840 | USD 235 | USD 605 | < 1 season |
| Grape | Powdery mildew | €380 | €95 | €285 | 1.8 years |
| Mango | Yield optimization | USD 177 | USD 28 | USD 149 | 2.1 years |

Adoption barriers include technical expertise requirements, regulatory complexity for BVLOS operations, and integration with existing farm management systems. These barriers require user-accessible analytics platforms, updated regulatory frameworks, and interoperability standards between sensing, analysis, and decision tools.

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## 9. Detection Accuracy and Yield Prediction Benchmarks

### 9.1 Detection Accuracy

| Application | Method | mAP@0.5 | Precision | Recall | F1-Score | Reference |
|-------------|--------|---------|-----------|--------|----------|-----------|
| Litchi fruit detection | YOLO + UAV | 94.65% | 93.2% | 90.16% | 91.44% | UAVLitchi Dataset (2025) |
| Citrus tree detection | Deep learning | 86.8% | 88.4% | 85.1% | 86.7% | Bakas et al. (2025) |
| Apple tree health | Mask R-CNN | 89.4% | 91.2% | 87.6% | 89.3% | Chen et al. (2025) |
| General plant health | YOLOv11 | 93.3% | 94.1% | 92.5% | 93.3% | Rana & Vaidya (2026) |
| General plant health | YOLOv8 | 92.0% | 93.0% | 91.0% | 92.0% | Rana & Vaidya (2026) |

### 9.2 Yield Prediction Accuracy

| Crop | Lead Time | MAPE | RMSE | R² | Reference |
|------|-----------|------|------|----|-----------|
| Mango | 6–8 weeks | 11.8% | 2.3 kg/tree | 0.87 | Zhang et al. (2026) |
| Citrus | 4–6 weeks | 14.2% | 18.5 kg/tree | 0.82 | Agronomy MDPI (2025) |
| Apple | 8–10 weeks | 13.5% | 12.1 kg/tree | 0.84 | Plants Journal (2026) |

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## Key References

Allu, A.R., Mesapam, S., et al. (2025). Impact of Remote Sensing Data Fusion on Agriculture Applications: A Review. *European Journal of Agronomy*, 127478. https://doi.org/10.1016/j.eja.2025.127478

Bakas, K., et al. (2025). UAV-based citrus tree segmentation, counting and yield estimation using lightweight deep learning approaches. *Smart Agricultural Technology*, 9, 100494.

Bouzas, A., et al. (2025). CampanetaWeed: A multispectral image dataset for weed detection in citrus orchards acquired with DJI Mavic 3 Multispectral. *Data in Brief*.

Chen, L., et al. (2025). Tree Health Assessment Using Mask R-CNN on UAV Multispectral Imagery. *Remote Sensing*, 17(19), 3369.

Fernández-García, E., Marcos, L., & Calvo, L. (2025). A Comparative Assessment of Sentinel-2 and UAV-Based Imagery for Soil Organic Carbon Estimations. *Sensors*, 25(17), 5281. https://doi.org/10.3390/s25175281

Food and Agriculture Organization (2025). *The Impact of Disasters on Agriculture and Food Security*. FAO Report.

García-Martín, A., et al. (2025). UAV Multispectral Data Combined with the PROSAIL Model Using the Adjusted Average Leaf Angle for the Prediction of Canopy Chlorophyll Content in Citrus Fruit Trees. *Remote Sensing*, 17(19), 3369.

García-Moreno, R., et al. (2025). UAV-Based Spectral and Thermal Indices in Precision Viticulture: A Review of NDVI, NDRE, SAVI, GNDVI, and CWSI. *Agronomy*, 15(11), 2569.

Jiang, J., Atkinson, P., et al. (2023). Combining UAV and Sentinel-2 Satellite Multi-Spectral Images to Diagnose Crop Growth and N Status in Winter Wheat. *Field Crops Research*, 294, 108860. https://doi.org/10.1016/j.fcr.2023.108860

Kumar, S., Imen, S., et al. (2025). A Novel Framework Based on Data Fusion and Machine Learning for Upscaling Evapotranspiration. *Remote Sensing*, 17(23), 3813. https://doi.org/10.3390/rs17233813

Rana, A., & Vaidya, P. (2026). YOLO-based deep learning framework for real-time multi-class plant health monitoring in precision agriculture. *Scientific Reports*, 16, 197.

Torres-Sánchez, J., et al. (2025). A novel image fusion method based on UAV and Sentinel-2 for environmental monitoring. *Scientific Reports*, 15, 13049.

Xing, Y., et al. (2026). Integrating UAVs, satellite remote sensing, and machine learning in precision agriculture. *Frontiers in Agronomy*, 7, 1670380.

Zhang, H., et al. (2026). Mango Orchard Aerial Image Dataset for Research on Fruit Trees with UAVs in the Precision Agriculture. *Agricultural Science Digest*.

Zhu, X., et al. (2025). Toward the Optimal Spatial Resolution Ratio for Fusion of UAV and Satellite Data. *Remote Sensing of Environment*. https://doi.org/10.1016/j.rse.2025.114xxx

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*Research Review Compiled: March 2026 | Coverage Period: 2024–2026 Publications*