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Application of Satellite and UAV Data in Precision Agriculture

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 25 May 2025 | Viewed by 14040

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Special Issue Editor

Dr. Mobushir Riaz Khan

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Guest Editor

School of Environmental Sciences, Charles Sturt University, Albury, NSW, Australia
Interests: remote sensing; geospatial data; cloud computing; ICT in agriculture

Special Issue Information

Dear Colleagues,

With the increase in the world's population and the reduction in land resources, it is imperative to find a way to improve the efficiency of agricultural production and make it develop sustainably. Precision agriculture is a management strategy that supports management decisions through the collection and analysis of temporal, spatial, and ancillary data. It revolutionizes agriculture by improving productivity and reducing environmental impacts.

The goal of precision agriculture is to increase crop yields while minimizing inputs such as water, fertilizer, and pesticides. As a space–air–ground integrated information collection technology, remote sensing has the potential to provide people with detailed and accurate data, enabling precise planting and intelligent management. Satellite and UAV data are widely used in crop monitoring, providing up-to-date information on moisture stress, nutrient levels, and disease. It can provide farmers with guidance to optimize crop inputs, such as water, fertilizer, or chemicals. As technology continues to evolve, precision agriculture becomes more sophisticated, enabling farmers to achieve even greater levels of productivity and sustainability.

The Special Issue invites contributions using satellite and UAV data in precision agriculture. Topics of interest for this Special Issue include, but are not limited to:

Decision support systems for agricultural monitoring;
Water resource management;
IoT in agriculture;
Soil fertility and plant nutrition;
Soil moisture and plant water content;
Yield monitoring and mapping;
Insect pest monitoring and management;
Variable rate applications;
Stakeholder perception on the adoption of digital technologies in agriculture.

Dr. Mobushir Riaz Khan
Guest Editor

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Keywords

precision agriculture
decision support systems in agriculture
crop growth modeling
crop yield estimation
crop water stress detection
soil properties mapping
satellite and UAV data

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Published Papers (9 papers)

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Research

21 pages, 8325 KiB

Open AccessArticle

Estimation of Forage Biomass in Oat (Avena sativa) Using Agronomic Variables through UAV Multispectral Imaging

by Julio Urquizo, Dennis Ccopi, Kevin Ortega, Italo Castañeda, Solanch Patricio, Jorge Passuni, Deyanira Figueroa, Lucia Enriquez, Zoila Ore and Samuel Pizarro

Remote Sens. 2024, 16(19), 3720; https://doi.org/10.3390/rs16193720 - 6 Oct 2024

Viewed by 1455

Abstract

Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R² of 0.52 on the test set, followed by Support Vector Machines with an R² of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose. Full article

(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)

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25 pages, 14077 KiB

Open AccessArticle

Estimating Leaf Area Index in Apple Orchard by UAV Multispectral Images with Spectral and Texture Information

by Junru Yu, Yu Zhang, Zhenghua Song, Danyao Jiang, Yiming Guo, Yanfu Liu and Qingrui Chang

Remote Sens. 2024, 16(17), 3237; https://doi.org/10.3390/rs16173237 - 31 Aug 2024

Viewed by 1622

Abstract

The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was proposed. Specifically, field measurements were conducted to collect LAI data for 108 sample points during the final flowering (FF), fruit setting (FS), and fruit expansion (FE) stages of apple growth in 2023. Concurrently, UAV multispectral images were obtained to extract spectral and texture information (Gabor transform). The Support Vector Regression Recursive Feature Elimination (SVR-REF) was employed to select optimal features as inputs for constructing models to estimate the LAI. Finally, the optimal model was used for LAI mapping. The results indicate that integrating spectral and texture information effectively enhances the accuracy of LAI estimation, with the relative prediction deviation (RPD) for all models being greater than 2. The Categorical Boosting (CatBoost) model established for FF exhibits the highest accuracy, with a validation set R², root mean square error (RMSE), and RPD of 0.867, 0.203, and 2.482, respectively. UAV multispectral imagery proves to be valuable in estimating apple orchard LAIs, offering real-time monitoring of apple growth and providing a scientific basis for orchard management. Full article

(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)

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20 pages, 31921 KiB

Open AccessArticle

High-Precision Mango Orchard Mapping Using a Deep Learning Pipeline Leveraging Object Detection and Segmentation

by Muhammad Munir Afsar, Asim Dilawar Bakhshi, Muhammad Shahid Iqbal, Ejaz Hussain and Javed Iqbal

Remote Sens. 2024, 16(17), 3207; https://doi.org/10.3390/rs16173207 - 30 Aug 2024

Viewed by 1179

Abstract

Precision agriculture-based orchard management relies heavily on the accurate delineation of tree canopies, especially for high-value crops like mangoes. Traditional GIS and remote sensing methods, such as Object-Based Imagery Analysis (OBIA), often face challenges due to overlapping canopies, complex tree structures, and varied light conditions. This study aims to enhance the accuracy of mango orchard mapping by developing a novel deep-learning approach that combines fine-tuned object detection and segmentation techniques. UAV imagery was collected over a 65-acre mango orchard in Multan, Pakistan, and processed into an RGB orthomosaic with a 3 cm ground sampling distance. The You Only Look Once (YOLOv7) framework was trained on an annotated dataset to detect individual mango trees. The resultant bounding boxes were used as prompts for the segment anything model (SAM) for precise delineation of canopy boundaries. Validation against ground truth data of 175 manually digitized trees showed a strong correlation (

R^{2}

= 0.97), indicating high accuracy and minimal bias. The proposed method achieved a mean absolute percentage error (MAPE) of 4.94% and root mean square error (RMSE) of 80.23 sq ft against manually digitized tree canopies with an average size of 1290.14 sq ft. The proposed approach effectively addresses common issues such as inaccurate bounding boxes and over- or under-segmentation of tree canopies. The enhanced accuracy can substantially assist in various downstream tasks such as tree location mapping, canopy volume estimation, health monitoring, and crop yield estimation. Full article

(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)

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22 pages, 11626 KiB

Open AccessArticle

Assessment of Semi-Automated Techniques for Crop Mapping in Chile Based on Global Land Cover Satellite Data

by Matías Volke, María Pedreros-Guarda, Karen Escalona, Eduardo Acuña and Raúl Orrego

Remote Sens. 2024, 16(16), 2964; https://doi.org/10.3390/rs16162964 - 12 Aug 2024

Viewed by 1026

Abstract

In recent years, the Chilean agricultural sector has undergone significant changes, but there is a lack of data that can be used to accurately identify these transformations. A study was conducted to assess the effectiveness of different spatial resolutions used by global land cover products (MODIS, ESA and Dynamic World (DW)), in addition to the demi-automated methods applied to them, for the identification of agricultural areas, using the publicly available agricultural survey for 2021. It was found that lower-spatial-resolution collections consistently underestimated crop areas, while collections with higher spatial resolutions overestimated them. The low-spatial-resolution collection, MODIS, underestimated cropland by 46% in 2021, while moderate-resolution collections, such as ESA and DW, overestimated cropland by 39.1% and 93.8%, respectively. Overall, edge-pixel-filtering and a machine learning semi-automated reclassification methodology improved the accuracy of the original global collections, with differences of only 11% when using the DW collection. While there are limitations in certain regions, the use of global land cover collections and filtering methods as training samples can be valuable in areas where high-resolution data are lacking. Future research should focus on validating and adapting these approaches to ensure their effectiveness in sustainable agriculture and ecosystem conservation on a global scale. Full article

(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)

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19 pages, 10716 KiB

Open AccessArticle

Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot

by Jenniffer Carolina Triana-Martinez, Andrés Marino Álvarez-Meza, Julian Gil-González, Tom De Swaef and Jose A. Fernandez-Gallego

Remote Sens. 2024, 16(15), 2854; https://doi.org/10.3390/rs16152854 - 4 Aug 2024

Viewed by 1112

Abstract

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets. Full article

(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)

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13 pages, 4563 KiB

Open AccessArticle

Biomass Estimation of Milk Vetch Using UAV Hyperspectral Imagery and Machine Learning

by Hao Hu, Hongkui Zhou, Kai Cao, Weidong Lou, Guangzhi Zhang, Qing Gu and Jianhong Wang

Remote Sens. 2024, 16(12), 2183; https://doi.org/10.3390/rs16122183 - 16 Jun 2024

Cited by 1 | Viewed by 1005

Abstract

Milk vetch (Astragalus sinicus L.) is a winter-growing plant that can enhance soil fertility and provide essential nutrients for subsequent season crops. The fertilizing capacity of milk vetch is closely related to its above-ground biomass. Compared to the manual measurement methods of milk vetch biomass, remote sensing-based estimation methods have the advantages of rapid, noninvasive, and large-scale measurement. However, few studies have been conducted on remote sensing-based estimation of milk vetch biomass. To address this shortcoming, this study proposes combining unmanned aerial vehicle (UAV)-based hyperspectral imagery and machine learning algorithms for accurate estimation of milk vetch biomass. Through the analysis of hyperspectral images and feature selection based on the Pearson correlation and principal component analysis, vegetation indices (VIs), including near-infrared reflectance (NIR), red-edge spectral transform index (RE), and difference vegetation index (DVI), are selected as estimation metrics of the model development process. Four machine learning methods, including random forest (RF), multiple linear regression (MLR), deep neural network (DNN), and support vector machine (SVM), are used to construct the biomass models. The results show that the RF estimation model exhibits the highest coefficient of determination (R²) of 0.950 and the lowest relative root-mean-squared error (RRMSE) of 14.86% among all the models. Notably, the DNN model demonstrates promising performance on the test set, with the R² and RRMSE values slightly superior and inferior to those of the RF, respectively. The proposed method based on UAV imagery and machine learning can provide an accurate and reliable large-scale estimation of milk vetch biomass. Full article

(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)

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22 pages, 9510 KiB

Open AccessArticle

Retrieval of Crop Canopy Chlorophyll: Machine Learning vs. Radiative Transfer Model

by Mir Md Tasnim Alam, Anita Simic Milas, Mateo Gašparović and Henry Poku Osei

Remote Sens. 2024, 16(12), 2058; https://doi.org/10.3390/rs16122058 - 7 Jun 2024

Viewed by 1810

Abstract

In recent years, the utilization of machine learning algorithms and advancements in unmanned aerial vehicle (UAV) technology have caused significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV–satellite data fusion have emerged as two prominent approaches for the estimation of vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for the mapping of crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV–satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV–satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV–RapidEye dataset exhibits the highest coefficient of determination (R²) and the lowest root mean square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian process regression (GPR) (R² = 0.89 and 0.89 and RMSE = 8.99 µg/cm² and 9.65 µg/cm², respectively). Similar performance is observed for the UAV–Landsat and UAV–PlanetScope datasets (R² = 0.86 and 0.87 for KRR, respectively). For the hybrid models, the maximum performance is attained with the Landsat data using KRR and GPR (R² = 0.77 and 0.51 and RMSE = 33.10 µg/cm² and 42.91 µg/cm², respectively), followed by R² = 0.75 and RMSE = 39.78 µg/cm² for the PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, the RapidEye data yield the most stable performance, with the R² ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm² to 33.07 µg/cm². The study highlights the importance of synergizing UAV and satellite data, which enables the effective monitoring of canopy chlorophyll in small agricultural lands. Full article

(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)

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18 pages, 11407 KiB

Open AccessArticle

Estimation of Rice Plant Coverage Using Sentinel-2 Based on UAV-Observed Data

by Yuki Sato, Takeshi Tsuji and Masayuki Matsuoka

Remote Sens. 2024, 16(9), 1628; https://doi.org/10.3390/rs16091628 - 2 May 2024

Cited by 1 | Viewed by 1300

Abstract

Vegetation coverage is a crucial parameter in agriculture, as it offers essential insight into crop growth and health conditions. The spatial resolution of spaceborne sensors is limited, hindering the precise measurement of vegetation coverage. Consequently, fine-resolution ground observation data are indispensable for establishing correlations between remotely sensed reflectance and plant coverage. We estimated rice plant coverage per pixel using time-series Sentinel-2 Multispectral Instrument (MSI) data, enabling the monitoring of rice growth conditions over a wide area. Coverage was calculated using unmanned aerial vehicle (UAV) data with a spatial resolution of 3 cm with the spectral unmixing method. Coverage maps were generated every 2–3 weeks throughout the rice-growing season. Subsequently, crop growth was estimated at 10 m resolution through multiple linear regression utilizing Sentinel-2 MSI reflectance data and coverage maps. In this process, a geometric registration of MSI and UAV data was conducted to improve their spatial agreement. The coefficients of determination (R²) of the multiple linear regression models were 0.92 and 0.94 for the Level-1C and Level-2A products of Sentinel-2 MSI, respectively. The root mean square errors of estimated rice plant coverage were 10.77% and 9.34%, respectively. This study highlights the promise of satellite time-series models for accurate estimation of rice plant coverage. Full article

(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)

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19 pages, 11920 KiB

Open AccessArticle

Mapping Leaf Area Index at Various Rice Growth Stages in Southern India Using Airborne Hyperspectral Remote Sensing

by Mathyam Prabhakar, Kodigal A. Gopinath, Nakka Ravi Kumar, Merugu Thirupathi, Uppu Sai Sravan, Golla Srasvan Kumar, Gutti Samba Siva, Pebbeti Chandana and Vinod Kumar Singh

Remote Sens. 2024, 16(6), 954; https://doi.org/10.3390/rs16060954 - 8 Mar 2024

Cited by 2 | Viewed by 1894

Abstract

Globally, rice is one of the most important staple food crops. The most significant metric for evaluating the rice growth and productivity is the Leaf Area Index (LAI), which can be effectively monitored using remote sensing data. Hyperspectral remote sensing provides contiguous bands at narrow wavelengths for mapping LAI at various rice phenological stages, and it is functionally related to canopy spectral reflectance. Hyperspectral signatures for different phases of rice crop growth was recorded using Airborne Visible Near-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) along with corresponding ground based observations. Ground-based hyperspectral canopy spectral reflectance measurements were recorded with FieldSpec 3 Hi-Res spectroradiometer (ASD Inc., Forsyth County, GA, USA; spectral range: 350–2500 nm) and LAI data from 132 farmer’s fields in Southern India. Among 29 hyperspectral vegetation indices tested, 8 were found promising for mapping rice LAI at various phenological stages. Among all the growth stages, the elongation stage was the most accurately estimated using vegetation indices that exhibited a significant correlation with the airborne hyperspectral reflectance. The validation of hyperspectral vegetation indices revealed that the best fit model for estimating rice LAI was mND₇₀₅ (red-edge, blue, and NIR bands) at seedling and elongation, SAVI (red and NIR bands) at tillering and WDRVI (red and NIR bands) at booting stage. Full article

(This article belongs to the Special Issue Application of Satellite and UAV Data in Precision Agriculture)

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