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The Application of Remote Sensing for Agricultural Monitoring

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A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 3604

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Editors

Dr. Wei Xue

E-Mail Website
Guest Editor

Colleague of Ecology and Environment, Xinjiang University, Urumqi 830017, China
Interests: retrieval of biophysical traits of crops by using remote sensing data; reconstruction of Landsat EVI trajectory
Special Issues, Collections and Topics in MDPI journals

Dr. Ruyin Cao

E-Mail Website
Guest Editor

School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: algorithm development; time-series remote sensing; vegetation phenology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing techniques are powerful tools for monitoring and evaluating soil water and nutrient conditions, crop growth, and other environmental indicators. In addition to satellite remote sensing, new devices like chlorophyll fluorescence imagers/meters, close-range drones/unmanned aerial vehicles, and mobile ground robots equipped with various sensors provide vast amounts of data. This data presents significant challenges in processing, analysis, and assimilation for practical agricultural applications. With the help of advanced non-parametric regression algorithms such as machine learning and artificial intelligence, new and effective information hidden in remote sensing big data could be unraveled, allowing for the optimization of crop production at unprecedented spatial and temporal scales.

To advance and optimize technology, analysis, and monitoring methods, this Special Issue welcomes original research and comprehensive reviews on the following topics: (1) advancements in remote sensing equipment and application scenarios, (2) enhanced fusion of multi-source remote sensing data for retrieving biophysical and biochemical variables of crops and soils, (3) innovative methodologies and image analysis tools for agricultural monitoring using remote sensing data, and (4) expanding the scope of remote sensing applications in agricultural monitoring.

Dr. Wei Xue
Dr. Ruyin Cao
Guest Editors

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Keywords

remote sensing
crop monitoring
algorithms
machine learning
artificial intelligence
field management

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Published Papers (4 papers)

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20 pages, 26047 KB

Open AccessArticle

Reproducible Benchmarking of Tomato Detection in Greenhouse: Comparing Attention-Augmented and Baseline Detectors

by Kaan Arik and Burak Ağgül

AgriEngineering 2026, 8(7), 275; https://doi.org/10.3390/agriengineering8070275 - 6 Jul 2026

Viewed by 223

Abstract

Accurate tomato detection in greenhouse imagery is essential for robotic harvesting, yield estimation, and crop monitoring, yet visual clutter, fruit overlap, partial occlusion, and variable illumination remain challenging for object detectors. Although attention modules are frequently used in agricultural vision studies to improve feature discrimination, their practical contribution is often reported without controlled comparison against strong baseline detectors. This study presents a reproducible and deployment-aware benchmark for single-class greenhouse tomato detection using 895 images with 4930 annotated tomato instances in PASCAL VOC format. The first experimental block used a fixed 70/20/10 split to compare Faster R-CNN, four attention-augmented Faster R-CNN variants, Cascade R-CNN with ResNet101-DCN-FPN, and YOLOv11s attention variants. A second extended protocol converted the annotations to YOLO format and evaluated YOLO-family detectors and RT-DETR-l under a stratified 70/15/15 split, including ablation, robustness, seed-stability, and deployment analyses. The annotation audit confirmed valid bounding boxes, no empty images, and a high proportion of small tomato instances. In the first block, attention integration did not consistently improve detection performance, whereas Cascade R-CNN achieved the highest accuracy with 92.80%

{mAP}_{0.5}

and 90.80%

F_{1}

-score. In the extended protocol, RT-DETR-l obtained the highest test accuracy with 91.49%

{mAP}_{0.5}

and 58.59%

{mAP}_{0.5 : 0.95}

, while Final-YOLO11s achieved comparable performance with lower latency, reaching 91.42%

{mAP}_{0.5}

, 58.37%

{mAP}_{0.5 : 0.95}

, and 86.19%

F_{1}

-score. Across three seeds, Final-YOLO11s obtained a stable mean

{mAP}_{0.5}

of 90.84%. Robustness analysis showed that motion blur and Gaussian noise caused the largest degradation, whereas compact YOLO models exported reliably to ONNX and TensorRT. Overall, the results indicate that localization quality, robustness, latency, model size, stability, and export capability should be considered together, and that adding attention modules by default is less reliable than evidence-driven detector selection. Full article

(This article belongs to the Special Issue The Application of Remote Sensing for Agricultural Monitoring)

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33 pages, 54800 KB

Open AccessArticle

Predicting Grain Yield and Popping Expansion in Native Peruvian Popcorn and Purple-Kernel Hybrids Using Multitemporal Unmanned Aerial Vehicle-Derived Multispectral and Textural Indices

by Elias Huanuqueño-Coca, José Huanuqueño-Murillo, Roxana Peña-Amaro, David Quispe-Tito, Lena Cruz-Villacorta, Indira Betalleluz-Pallardel, Javier Quille-Mamani and Lia Ramos-Fernández

AgriEngineering 2026, 8(6), 209; https://doi.org/10.3390/agriengineering8060209 - 27 May 2026

Viewed by 725

Abstract

Popping expansion is the main quality trait determining the commercial value of popcorn maize, yet its evaluation requires destructive grain sampling. We investigated whether multitemporal UAV multispectral and textural features could predict grain yield and popping expansion in a native population of Peruvian popcorn and its five purple-kernel corn hybrids grown in 16 drainage lysimeters (80 subplots) under controlled irrigation in Lima, Peru. Eight UAV flights were conducted between 50 and 117 days after sowing, and 8 vegetation indices plus 5 GLCM texture metrics were extracted from canopy-masked imagery. Six regression algorithms were trained using Sequential Forward Selection (SFS; applied to five of six algorithms) and validated by Leave-One-Lysimeter-Out cross-validation (LOGO). Early grain, grain filling, and maturity were the most informative stages for yield prediction. The best model, obtained at maturity, was SVR-rbf using SCCCI and Homogeneity, reaching R² = 0.66 and RMSE = 1.23 t ha⁻¹. SCCCI was the most consistently selected predictor across models. By contrast, popping expansion was poorly predicted (R² = 0.17), indicating that this post-harvest quality trait is only weakly linked to canopy-level spectral information. Multitemporal UAV phenotyping therefore shows promise for non-destructive yield screening, but not for replacing direct popping expansion measurements. Full article

(This article belongs to the Special Issue The Application of Remote Sensing for Agricultural Monitoring)

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37 pages, 28225 KB

Open AccessArticle

Hierarchical Spectral Modelling of Pasture Nutrition: From Laboratory to Sentinel-2 via UAV Hyperspectral

by Jason Barnetson, Hemant Raj Pandeya and Grant Fraser

AgriEngineering 2026, 8(4), 143; https://doi.org/10.3390/agriengineering8040143 - 7 Apr 2026

Viewed by 1057

Abstract

This study demonstrates a hierarchical spectral modelling approach for predicting pasture nutrition metrics using TabPFN (Tabular Prior-Data Fitted Network), a transformer-based machine learning architecture. In the face of climate variability, aligning stocking rates with pasture resources is crucial for sustainable livestock grazing, requiring accurate assessments of both pasture biomass and nutrient composition. Our research, conducted across diverse growth stages at five tropical and subtropical savanna rangeland properties in Queensland, Australia, with native and introduced C4 grasses, employed a hierarchical sampling and modelling strategy that scales from laboratory spectroscopy to Sentinel-2 satellite predictions via uncrewed aerial vehicle (UAV) hyperspectral imaging. Spectral data were collected from leaf (laboratory spectroscopy) through field (point measurements), UAV hyperspectral imaging, and Sentinel-2 satellite imagery. Traditional laboratory wet chemistry methods determined plant leaf and stem nutrient content, from which crude protein (CP = total nitrogen (TN) × 6.25) and dry matter digestibility (DMD = 88.9–0.779 × acid detergent fibre (ADF)) were derived. TabPFN models were trained at each spatial scale, achieving validation

R^{2}

of 0.76 for crude protein at the leaf scale, 0.95 at the UAV scale, and 0.92 at the Sentinel-2 satellite scale. For dry matter digestibility, validation

R^{2}

was 0.88 at the UAV scale and 0.73 at the Sentinel-2 scale. A pasture classification masking approach using a deep neural network with 98.6% accuracy (7 classes) was implemented to focus predictions on productive pasture areas, excluding bare soil and woody vegetation. The Sentinel-2 models were trained on 462 samples from 19 site–date combinations across 11 field sites. The TabPFN architecture provided notable advantages over traditional neural networks: no hyperparameter tuning required, faster training, and superior generalisation from limited training samples. These results demonstrate the potential for accurate and efficient prediction and mapping of pasture quality across large areas (100 s–1000 s

{k m}^{2}

) using freely available satellite imagery and open-source machine learning frameworks. Full article

(This article belongs to the Special Issue The Application of Remote Sensing for Agricultural Monitoring)

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48 pages, 15092 KB

Open AccessSystematic Review

Extraction of Plant Physiological Features Using Multispectral Imaging and Spectrophotometry: A Systematic Review Highlighting Research Gaps for Stenocereus spp.

by Rosa Janette Pérez-Chimal, Claudia Angélica Rivera-Romero, Julián Moisés Estudillo-Ayala, Remberto Sandoval-Aréchiga, Alejandro Barrientos-García and Jorge Ulises Muñoz-Minjares

AgriEngineering 2026, 8(5), 162; https://doi.org/10.3390/agriengineering8050162 - 27 Apr 2026

Viewed by 1035

Abstract

Objectives: Multispectral imaging and spectrophotometry are widely used to estimate plant physiological characteristics, yet the literature remains fragmented across sensors, indices, and analytical approaches. Methods: This systematic review followed PRISMA 2020 and was preregistered in OSF (Open Science Framework). Web of Science, Scopus, Google Scholar, and Consensus were searched up to January 2025 for peer-reviewed studies and selected gray literature studies focused on plant physiological trait estimation using multispectral or spectrophotometric methods. From 256 identified records, 96 studies met the eligibility criteria. Methodological quality was assessed across five domains, and results were synthesized narratively owing to high heterogeneity. Results: A total of 96 studies met the eligibility criteria. Among these, multispectral sensors were the most commonly used (40.7%), followed by UAV-mounted platforms (25.9%), while hyperspectral sensors accounted for 18.5% of the studies. The most frequently used vegetation index was NDVI, reported in 87% of the studies, mainly for estimating vigor, biomass, and canopy structure. Discussion: Although multispectral indices reliably capture key agronomic traits, cross-study comparability is currently hampered by significant methodological variability and a lack of consistent validation protocols. Conclusions: Multispectral imaging and spectrophotometry are effective tools for estimating plant physiological traits, but greater standardization is needed across studies. Owing to the limited number of studies on Stenocereus spp., the review was expanded to plants in general; the shortage of reports addressing Stenocereus spp. highlights the need for future research in these species. Full article

(This article belongs to the Special Issue The Application of Remote Sensing for Agricultural Monitoring)

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