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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: closed (15 February 2025) | Viewed by 12410

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Special Issue Editors

Dr. Giuseppe Rossi

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Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura 13, 50145 Florence, Italy
Interests: topography; remote sensing; environmental monitoring and analysis
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gabriel Araújo e Silva Ferraz

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Department of Agricultural Engineering, School of Engineering, Federal University of Lavras—UFLA, P.O. Box 3037, Lavras 37200-900, Brazil
Interests: remote sensing; UAV in agriculture and livestock; digital and precision farming and livestock
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Prof. Dr. Leonardo Conti

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Department of Industrial Engineering, University of Florence, I-50144 Florence, Italy
Interests: UAV in agriculture, livestock and wildlife; geographic information systems; remote sensing; environmental monitoring and analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Diego Bedin Marin

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Guest Editor

Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura 13, 50145 Florence, Italy
Interests: UAV; remote sensing; digital and precision agriculture; spectral analysis and machine learning

Special Issue Information

Dear Colleagues,

Remote Sensing technologies in precision agriculture have increased exponentially in recent decades. The unprecedented availability of spectral sensors has promoted the use of aerial remote sensing systems in many applications in agriculture, including pest and disease management, irrigation management, nutrient application, and yield prediction. The great advantage of this technology over traditional crop monitoring methods is the rapid and continuous data collection, allowing farmers, researchers and rural professionals to better understand the field studied, having this information in real-time or in a relatively short time data processing.

However, new challenges have also emerged, as the aerial remote sensing systems generate a large volume of spectral data due to the high spatial, spectral and temporal resolutions indispensable for the application in precision agriculture. In view of this, it has been necessary to use data processing techniques, such as big data analysis, artificial intelligence, deep learning, machine learning and other intelligent technologies, to extract useful information from the large volume of data and assist in the decision process for field crops.

This Special Edition is intended for a global research community involved in data analysis and processing and developing innovative aerial remote sensing system solutions for agriculture. Contributions may include, but are not limited to: UAS applied in the detection of pests and diseases, weeds, estimation of planted area and productivity, monitoring of plant health, detection of failures in irrigation, fertilization or soil preparation, analysis of coverage vegetation, topography, drainage and soil type, spectral data analysis, hyperspectral, multispectral and thermal image processing and UAV design for agricultural uses.

Dr. Giuseppe Rossi
Prof. Dr. Gabriel Araújo e Silva Ferraz
Prof. Dr. Leonardo Conti
Dr. Diego Bedin Marin
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

unmanned aerial systems (UAS)
remote sensing
precision agriculture
digital agriculture
ultra-high spatial resolution
hyperspectral images
multispectral images
RGB images
vegetation indices
machine and deep learning

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Published Papers (6 papers)

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Research

24 pages, 12343 KiB

Open AccessArticle

Multispectral UAV Image Classification of Jimson Weed (Datura stramonium L.) in Common Bean (Phaseolus vulgaris L.)

by Marlies Lauwers, Benny De Cauwer, David Nuyttens, Wouter H. Maes and Jan G. Pieters

Remote Sens. 2024, 16(18), 3538; https://doi.org/10.3390/rs16183538 - 23 Sep 2024

Viewed by 1068

Abstract

Jimson weed (Datura stramonium L.) is a toxic weed that is occasionally found in fields with common bean (Phaseolus vulgaris L.) for the processing industry. Common bean growers are required to manually remove toxic weeds. If toxic weed plants remain, the standing crop will be rejected. Hence, the implementation of an automatic weed detection system aiding the farmers is badly needed. The overall goal of this study was to investigate if D. stramonium can be located in common bean fields using an unmanned aerial vehicle (UAV)-based ten-band multispectral camera. Therefore four objectives were defined: (I) assessing the spectral discriminative capacity between common bean and D. stramonium by the development and application of logistic regression models; (II) examining the influence of ground sampling distance (GSD) on model performance; and improving model generalization by (III) incorporating the use of vegetation indices and cumulative distribution function (CDF) matching and by (IV) combining spectral data from multiple common bean fields with the use of leave-one-group-out cross-validation (LOGO CV). Logistic regression models were created using data from fields at four different locations in Belgium. Based on the results, it was concluded that common bean and D. stramonium are separable based on multispectral information. A model trained and tested on the data of one location obtained a validation true positive rate and true negative rate of 99% and 95%, respectively. In this study, where D. stramonium had a mean plant size of 0.038 m² (

σ

= 0.020), a GSD of 2.1 cm was found to be appropriate. However, the results proved to be location dependent as the model was not able to reliably distinguish D. stramonium in two other datasets. Finally, the use of a LOGO CV obtained the best results. Although small D. stramonium plants were still systematically overlooked and classified as common bean, the model was capable of detecting large D. stramonium plants on three of the four fields. This study emphasizes the variability in reflectance data among different common bean fields and the importance of an independent dataset to test model generalization. Full article

(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)

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18 pages, 11915 KiB

Open AccessArticle

Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

by Freda Dorbu and Leila Hashemi-Beni

Remote Sens. 2024, 16(14), 2679; https://doi.org/10.3390/rs16142679 - 22 Jul 2024

Cited by 3 | Viewed by 1368

Abstract

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level. Full article

(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)

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15 pages, 7867 KiB

Open AccessArticle

Residual Ash Mapping and Coffee Plant Development Based on Multispectral RPA Images

by Lucas Santos Santana, Gabriel Araújo e Silva Ferraz, Mozarte Santos Santana, Nicole Lopes Bento, Josiane Maria da Silva and Rafael de Oliveira Faria

Remote Sens. 2024, 16(11), 1917; https://doi.org/10.3390/rs16111917 - 27 May 2024

Viewed by 1025

Abstract

Residues mapping can provide essential information about soil chemical elements’ behaviors and contribute to possible interferences in coffee tree development. Thus, the research objective was to monitor plant residue burning effects by analyzing the chemical elements in ash, using soil analysis, and applying vegetative indices obtained by RPA images. The samples were submitted for conventional soil analysis and atomic emission spectrometry (pure ash). The RPA multispectral images were used to form thirty-one vegetative indices. Thus, at the soil and ash collection points, the index performance was evaluated for six months and divided into three collection times. Then, the data were statistically analyzed to evaluate which index best separated the plants in regions with ash and ash-free soil. The pure ash deposits revealed expressive presences of K, Ca, Mg, and Al in addition to pH elevation. In areas with ash, the high temperature at the burning time may have caused elemental chemical transformations in the Al composition, making this element unavailable in soil analysis. The vegetative indices showed a significant difference only in coffee four months after planting. Among the thirty-one evaluated indices, only twenty were satisfactory for ash analysis. The burning of plant residues promoted the neutralization of Al. In addition, ash deposits in the soil added some essential elements for plant development. Negatively, they raised the PH and made micronutrients unavailable. The best vegetative indices for ash monitoring were the Normalized Near Infrared Index (NNIRI) and Normalized Green Index (NGI). Prior ash mapping can contribute to localized application in macro, such as K and limestone, reusing the number of elements already deposited by burning vegetables. Full article

(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)

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22 pages, 10945 KiB

Open AccessArticle

Analysis of the Influence of Polarization Measurement Errors on the Parameter and Characteristics Measurement of the Fully Polarized Entomological Radar

by Muyang Li, Teng Yu, Rui Wang, Weidong Li, Fan Zhang and Chunfeng Wu

Remote Sens. 2024, 16(7), 1220; https://doi.org/10.3390/rs16071220 - 30 Mar 2024

Viewed by 1023

Abstract

Measuring the orientation, mass and body length of migratory insects through entomological radar is crucial for early warnings of migratory pests. The fully polarized entomological radar is an efficient device for observing migratory insects by calculating insect parameters through the scattering matrix obtained from the target. However, the measured target scattering matrix will be affected by system polarization measurement errors, leading to errors in insect parameter calculation, while the related analysis is currently relatively limited. Therefore, the scattering matrix measurement process is modeled, followed by an analysis of the effects of different errors on orientation, mass and body length estimation. The influence of polarization measurement errors on insect scattering characteristics is also analyzed. The results present that for fixed polarization measurement errors, the measurement errors of insect orientation, mass and body length will vary with insect orientation in specific patterns, and the distribution of measured insect parameters will be drastically distorted compared to the true parameter distribution. In addition, polarization measurement errors could seriously disrupt the reciprocity and bilateral symmetry of the measured insect scattering matrix. These analyses and conclusions provide a good basis for eliminating the effects of polarization measurement errors and improving the accuracy of insect parameter measurement. Full article

(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)

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32 pages, 15910 KiB

Open AccessArticle

Evaluating the Efficacy of Segment Anything Model for Delineating Agriculture and Urban Green Spaces in Multiresolution Aerial and Spaceborne Remote Sensing Images

by Baoling Gui, Anshuman Bhardwaj and Lydia Sam

Remote Sens. 2024, 16(2), 414; https://doi.org/10.3390/rs16020414 - 20 Jan 2024

Cited by 14 | Viewed by 4633

Abstract

Segmentation of Agricultural Remote Sensing Images (ARSIs) stands as a pivotal component within the intelligent development path of agricultural information technology. Similarly, quick and effective delineation of urban green spaces (UGSs) in high-resolution images is also increasingly needed as input in various urban simulation models. Numerous segmentation algorithms exist for ARSIs and UGSs; however, a model with exceptional generalization capabilities and accuracy remains elusive. Notably, the newly released Segment Anything Model (SAM) by META AI is gaining significant recognition in various domains for segmenting conventional images, yielding commendable results. Nevertheless, SAM’s application in ARSI and UGS segmentation has been relatively limited. ARSIs and UGSs exhibit distinct image characteristics, such as prominent boundaries, larger frame sizes, and extensive data types and volumes. Presently, there is a dearth of research on how SAM can effectively handle various ARSI and UGS image types and deliver superior segmentation outcomes. Thus, as a novel attempt in this paper, we aim to evaluate SAM’s compatibility with a wide array of ARSI and UGS image types. The data acquisition platform comprises both aerial and spaceborne sensors, and the study sites encompass most regions of the United States, with images of varying resolutions and frame sizes. It is noteworthy that the segmentation effect of SAM is significantly influenced by the content of the image, as well as the stability and accuracy across images of different resolutions and sizes. However, in general, our findings indicate that resolution has a minimal impact on the effectiveness of conditional SAM-based segmentation, maintaining an overall segmentation accuracy above 90%. In contrast, the unsupervised segmentation approach, SAM, exhibits performance issues, with around 55% of images (3 m and coarser resolutions) experiencing lower accuracy on low-resolution images. Whereas frame size exerts a more substantial influence, as the image size increases, the accuracy of unsupervised segmentation methods decreases extremely fast, and conditional segmentation methods also show some degree of degradation. Additionally, SAM’s segmentation efficacy diminishes considerably in the case of images featuring unclear edges and minimal color distinctions. Consequently, we propose enhancing SAM’s capabilities by augmenting the training dataset and fine-tuning hyperparameters to align with the demands of ARSI and UGS image segmentation. Leveraging the multispectral nature and extensive data volumes of remote sensing images, the secondary development of SAM can harness its formidable segmentation potential to elevate the overall standard of ARSI and UGS image segmentation. Full article

(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)

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29 pages, 15594 KiB

Open AccessArticle

A Study on Leveraging Unmanned Aerial Vehicle Collaborative Driving and Aerial Photography Systems to Improve the Accuracy of Crop Phenotyping

by Kangbeen Lee and Xiongzhe Han

Remote Sens. 2023, 15(15), 3903; https://doi.org/10.3390/rs15153903 - 7 Aug 2023

Cited by 2 | Viewed by 1576

Abstract

Unmanned aerial vehicle (UAV)-based aerial images have enabled a prediction of various factors that affect crop growth. However, the single UAV system leaves much to be desired; the time lag between images affects the accuracy of crop information, lowers the image registration quality and a maximum flight time of 20–25 min, and limits the mission coverage. A multiple UAV system developed from our previous study was used to resolve the problems centered on image registration, battery duration and to improve the accuracy of crop phenotyping. The system can generate flight routes, perform synchronous flying, and ensure capturing and safety protocol. Artificial paddy plants were used to evaluate the multiple UAV system based on leaf area index (LAI) and crop height measurements. The multiple UAV system exhibited lower error rates on average than the single UAV system, with 13.535% (without wind effects) and 17.729–19.693% (with wind effects) for LAI measurements and 5.714% (without wind effect) and 4.418% (with wind effects) for crop’s height measurements. Moreover, the multiple UAV system reduced the flight time by 66%, demonstrating its ability to overcome battery-related barriers. The developed multiple UAV collaborative system has enormous potential to improve crop growth monitoring by addressing long flight time and low-quality phenotyping issues. Full article

(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)

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