Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Ground, Proximal and Remote Sensing for Precision Agriculture Applications
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Ground, Proximal and Remote Sensing for Precision Agriculture Applications

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: 30 September 2024 | Viewed by 2728

Special Issue Editors

Dr. Alessandro Mei

E-Mail Website
Guest Editor
Institute of Atmospheric Pollution Research (CNR-IIA), National Research Council of Italy, Monterotondo, RM, Italy
Interests: geomatics; UAV; aircraft and satellite remote sensing; multispectral and hyperspectral remote sensing; imaging spectroscopy; precision agriculture; infrastructures analysis by remote sensing techniques; photogrammetry and 3D modelling; GIS and geospatial statistics; CAL/VAL; land use land cover change; development of multi-parametric approaches for environmental monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Yung-Chung Chuang

E-Mail Website
Guest Editor
Department of Urban Planning and Spatial Information, Feng-Chia University, Taichung 407802, Taiwan
Interests: geographic vegetation analysis by remote sensing techniques; precision agriculture and forestry; information science; aerial survey; digital elevation model technologies and applications; quantitative analysis of watershed geomorphology; environmental planning and management; geospatial statistics; remote sensing
Dr. Carlos Lozano Fondón

E-Mail
Guest Editor
CREA—Council for Research in Agriculture and Economics Research Centre for Agriculture and Environment, 50125 Florence, Italy
Interests: remote and proximal soil sensing; digital soil mapping; spectroscopy; gamma-ray radiometrics; geomatics; soil monitoring; soil survey; soil biodiversity; ecological networks; ecosystem modelling

Special Issue Information

Dear Colleagues,

Precision Agriculture (PA) is one of the most prominent and increasing segments in the agricultural economic area. Nevertheless, most of criticalities for small companies are referred to their deficiency to access to the newest technologies. That’s why, the adoption of specific and low-cost technologies could strongly promote a sustainable and economical management of medium and small agricultural systems. The soil composition variability, microclimate and lighting conditions may differ within each Production Unit and affect vegetative vigor and the physiological parameters of plants, with direct effects on qualitative and quantitative production. It is known that the PA encompasses a wide number of methodologies that monitor different aspects of soils, plant vigor, or the vegetative–productive balance. These localized inspections allow to obtain site-specific characteristics and to program specifical approaches. In the last decade, there has been a progressive diffusion of Unmanned Aerial Systems (UAS) technologies for PA purposes, as rapid and accurate methodologies designed to evaluate the spatial variability of geometric plant parameters and plant vigor analysis by means of commercial and Open Source (OS) software. Considering the significant market increase in UAS in agricultural applications, the need for such kind of technologies in the agricultural sector could generate a great economic impact. Such tendence is evident by the reinforcement of the scientific compound focusing on topics related to UASs in precision agriculture compounds. In addition to UAS and satellite platforms, the application of different ground sensing technologies (such as spectroscopy and Unmanned Ground Vehicles (UGVs)) could reinforce the capacities to analyze and extract specific information by computing, for example, site-specific time-series analysis and low-cost Decision Support Systems (DSS).

The aim of this Special Issue is to collect papers that present innovative studies, sensors, or technologies for the improvement of PA and PF (Precision Forestry) via the use of combined Ground, Proximal and Remote sensing technologies. The integration of the above technologies in the immediate future should simplify management and promote carbon farming and agroforestry practices simultaneously. Considering the need to reduce procedural budgets and enhance the accuracy of maps, papers that use OS software and cost-effective technologies will be more than welcomed. For cost-efficient technologies data fusion can be considered by integrating different spatial, spectral, and radiometric data resolutions.

The scope of this Special Issue, regarding the combination or single use of ground, proximal and remote sensing in PA and PF, includes, but is not limited to, the following:

  • Soil digital mapping;    
  • Canopy condition assessment;
  • Canopy architecture computation;
  • Missing plants detection;
  • Biophysical plant parameters;
  • Spatio-temporal analysis;
  • Geostatistics of ground sensing;
  • Decision support system;
  • Carbon farming.

Article types: research articles, review papers, communication papers, and technical notes.

Dr. Alessandro Mei
Dr. Yung-Chung Chuang
Dr. Carlos Lozano Fondón
Guest Editors

Roberto Barbetti
Guest Editor Assistant
Email: [email protected]
CREA—Council for Research in Agriculture and Economics Research Centre for Forestry and Wood, 0142 Casale Monferrato, Italy
Interests: soil survey and applied pedology; digital soil mapping; soil monitoring on the go proximal soil sensors; soil spectroscopy and soil spectral library (SSL); digital soil assessment of agricultural suitability; precision farming and precision forestry

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • satellite remote sensing
  • UAS and UGV
  • digital soil mapping
  • ground sensor network
  • vegetation analysis and yield estimation
  • precision agriculture and forestry
  • image processing and pattern recognition
  • field and laboratory Spectroscopy
  • DEM, DTM, DSM, DDM
  • artificial intelligence
  • decision support systems
  • geographical information systems
  • geostatistics

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 17350 KiB  
Article
Multispectral, Thermographic and Spectroradiometric Analyses Unravel Bio-Stimulatory Effects of Wood Distillate in Field-Grown Chickpea (Cicer arietinum L.)
by Pablo Carril, Ilaria Colzi, Riccardo Salvini, Luisa Beltramone, Andrea Rindinella, Andrea Ermini, Cristina Gonnelli, Andrea Garzelli and Stefano Loppi
Remote Sens. 2024, 16(14), 2524; https://doi.org/10.3390/rs16142524 - 10 Jul 2024
Viewed by 618
Abstract
Wood distillate (WD) has recently emerged as a promising bio-stimulant for sustainable legume crop production, owing to its ability to enhance seed yield and quality. However, no studies exist on the effects of WD on chickpea plants at pre-harvesting stages, hindering the farmers’ [...] Read more.
Wood distillate (WD) has recently emerged as a promising bio-stimulant for sustainable legume crop production, owing to its ability to enhance seed yield and quality. However, no studies exist on the effects of WD on chickpea plants at pre-harvesting stages, hindering the farmers’ ability to acquire valuable knowledge on the early action of WD on the plants’ status and preventing the establishment of proactive measures to optimize WD use in agriculture. In this study, two multispectral, thermographic and spectroradiometric surveys, along with in-situ measurements of specific plant biometric traits, were conducted across the reproductive stage of field-grown chickpea in order to evaluate the early involvement of WD on plant health. The acquired multispectral images were used to calculate the Normalized Difference Vegetation Index (NDVI), revealing a notable ~35% increase in NDVI scores of WD-treated plants at the onset of physiological maturity, and indicating an improved plant status compared to the control (water-treated) plants. Moreover, control and WD-treated plants exhibited distinct spectral signatures across the visible, near-infrared (NIR) and short-wave infrared (SWIR) spectra, suggesting potential changes in their photosynthetic capacity, structural properties and water content both at the leaf and at the pod level. Furthermore, WD-treated plants showed a 25% increase in pod production, particularly at the beginning of seed maturity, suggesting that enhancements in plant status were also reflected in higher pod yields. These results point to a beneficial effect of WD on plant health during the preliminary stages of seed formation and indicate that a combination of both multispectral and spectroradiometric analyses can provide critical insights on the status of chickpea crops at pre-harvesting stages. In addition, these findings emphasize the importance of analyzing pre-harvesting stages to gain insights into the early involvement of WD in promoting plant health and, ultimately, in predicting final crop yields. Full article
(This article belongs to the Special Issue Ground, Proximal and Remote Sensing for Precision Agriculture Applications)
►▼ Show Figures

Figure 1

25 pages, 10742 KiB  
Article
Estimation of Anthocyanins in Winter Wheat Based on Band Screening Method and Genetic Algorithm Optimization Models
by Huiling Miao, Xiaokai Chen, Yiming Guo, Qi Wang, Rui Zhang and Qingrui Chang
Remote Sens. 2024, 16(13), 2324; https://doi.org/10.3390/rs16132324 - 26 Jun 2024
Viewed by 868
Abstract
Anthocyanin can improve the stress tolerance and disease resistance of winter wheat to a certain extent, so timely and accurate monitoring of anthocyanin content is crucial for the growth and development of winter wheat. This study measured the ground-based hyperspectral reflectance and the [...] Read more.
Anthocyanin can improve the stress tolerance and disease resistance of winter wheat to a certain extent, so timely and accurate monitoring of anthocyanin content is crucial for the growth and development of winter wheat. This study measured the ground-based hyperspectral reflectance and the corresponding anthocyanin concentration at four key growth stages—booting, heading, flowering, and filling—to explore the spectral detection of anthocyanin in winter wheat leaves. Firstly, the first-order differential spectra (FDS) are obtained by processing based on the original spectra (OS). Then, sensitive bands (SBS), the five vegetation indices for optimal two-band combinations (VIo2), and the five vegetation indices for optimal three-band combinations (VIo3) were selected from OS and FDS by band screening methods. Finally, modeling methods such as RF, BP, and KELM, as well as models optimized by genetic algorithm (GA), were used to estimate anthocyanin content at different growth stages. The results showed that (1) among all the models, the GA_RF had incredible performance, VIo3 was the superior parameter for estimating anthocyanin values, and the model GA_RF of FDS data based on VIo3 for the filling stage (Rv2 = 0.950, RMSEv = 0.005, RPDv = 4.575) provided the best estimation of anthocyanin. (2) the first-order differential processing could highlight the degree of response of SBS, VIo2, and VIo3 to the anthocyanin values. The model performances of the FDS were better than that of OS on the whole, and the Rv2 of the optimal models of FDS were all greater than 0.89. (3) GA had optimizing effects on the RF, BP, and KELM, and overall, the GA models improved the R2 by 0.00%-18.93% compared to the original models. These results will provide scientific support for the use of hyperspectral techniques to monitor anthocyanin in the future. Full article
(This article belongs to the Special Issue Ground, Proximal and Remote Sensing for Precision Agriculture Applications)
►▼ Show Figures

Graphical abstract

26 pages, 9900 KiB  
Article
Ground-Based Hyperspectral Estimation of Maize Leaf Chlorophyll Content Considering Phenological Characteristics
by Yiming Guo, Shiyu Jiang, Huiling Miao, Zhenghua Song, Junru Yu, Song Guo and Qingrui Chang
Remote Sens. 2024, 16(12), 2133; https://doi.org/10.3390/rs16122133 - 13 Jun 2024
Cited by 1 | Viewed by 583
Abstract
Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the maize LCC during four critical growth stages and investigate the ability of phenological parameters (PPs) to estimate the LCC. First, four spectra [...] Read more.
Accurately measuring leaf chlorophyll content (LCC) is crucial for monitoring maize growth. This study aims to rapidly and non-destructively estimate the maize LCC during four critical growth stages and investigate the ability of phenological parameters (PPs) to estimate the LCC. First, four spectra were obtained by spectral denoising followed by spectral transformation. Next, sensitive bands (Rλ), spectral indices (SIs), and PPs were extracted from all four spectra at each growth stage. Then, univariate models were constructed to determine their potential for independent LCC estimation. The multivariate regression models for the LCC (LCC-MR) were built based on SIs, SIs + Rλ, and SIs + Rλ + PPs after feature variable selection. The results indicate that our machine-learning-based LCC-MR models demonstrated high overall accuracy. Notably, 83.33% and 58.33% of these models showed improved accuracy when the Rλ and PPs were successively introduced to the SIs. Additionally, the model accuracies of the milk-ripe and tasseling stages outperformed those of the flare–opening and jointing stages under identical conditions. The optimal model was created using XGBoost, incorporating the SI, Rλ, and PP variables at the R3 stage. These findings will provide guidance and support for maize growth monitoring and management. Full article
(This article belongs to the Special Issue Ground, Proximal and Remote Sensing for Precision Agriculture Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop