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Precision Agriculture and Sensor Systems—2nd Edition
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Precision Agriculture and Sensor Systems—2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 3 August 2024 | Viewed by 4758

Special Issue Editors

Prof. Dr. Viacheslav Adamchuk

E-Mail Website
Guest Editor
Department of Bioresource Engineering, 21,111 Lakeshore Road, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
Interests: development of soil and plant sensor systems; geospatial data processing; navigation of agricultural vehicles; implementation of precision agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Abdul M. Mouazen

E-Mail Website
Guest Editor
Precision Soil and Crop Engineering (Precision Scoring), Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, Blok B, 1st Floor, 9000 Gent, Belgium
Interests: proximal soil sensing; soil and water management; soil dynamics; tillage; traction; compaction; mechanical weeding; soil remediation and management and precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

You are invited to submit a manuscript to a special issue of Sensors. This issue will summarize cutting-edge research on the development and application of new sensor systems to support precision agriculture. We are especially interested in contributions on novel approaches to characterize soil, plants and animals as well as new ways to use sensor data to support the decision-making process.

Prof. Dr. Viacheslav Adamchuk
Prof. Dr. Abdul M. Mouazen
Guest Editors

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. Sensors 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 2600 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.

Published Papers (3 papers)

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Research

17 pages, 3815 KiB  
Article
Site-Independent Mapping of Clay Content in Vineyard Soils via Mobile Proximal Gamma-Ray Spectrometry and Machine Learning Calibrations
by Ralf Wehrle and Stefan Pätzold
Sensors 2024, 24(14), 4528; https://doi.org/10.3390/s24144528 - 12 Jul 2024
Viewed by 235
Abstract
Vineyards hold considerable soil variability between regions and plots, and there is frequently large soil heterogeneity within plots. Clay content in vineyard soils is of interest with respect to soil management, environmental monitoring, and wine quality. However, spatially resolved clay mapping is laborious [...] Read more.
Vineyards hold considerable soil variability between regions and plots, and there is frequently large soil heterogeneity within plots. Clay content in vineyard soils is of interest with respect to soil management, environmental monitoring, and wine quality. However, spatially resolved clay mapping is laborious and expensive. Gamma-ray spectrometry (GS) is a suitable tool for predicting clay content in precision agriculture when locally calibrated, but it has scarcely been tested site-independently and in vineyards. This study evaluated GS to predict clay content with a site-independent calibration and four machine learning algorithms (Support Vector Machines, Random Forest, k-Nearest Neighbors, and Bayesian regulated neuronal networks) in eight vineyards from four German vine-growing regions. Clay content in the studied soils ranged from 62 to 647 g kg−1. The Random Forest calibration was most suitable. Test set evaluation revealed good model performance for the entire dataset with RPIQ = 4.64, RMSEP = 56.7 g kg−1, and R2 = 0.87; however, prediction quality varied between the sites. Overall, GS with the Random Forest model calibration was appropriate to predict the clay content and its spatial distribution, even for heterogeneous geopedological settings and in individual plots. Therefore, GS is considered a valuable tool for soil mapping in vineyards, where clay content and product quality are closely linked. Full article
(This article belongs to the Special Issue Precision Agriculture and Sensor Systems—2nd Edition)
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17 pages, 2879 KiB  
Article
Prediction Accuracy of Soil Chemical Parameters by Field- and Laboratory-Obtained vis-NIR Spectra after External Parameter Orthogonalization
by Konrad Metzger, Frank Liebisch, Juan M. Herrera, Thomas Guillaume and Luca Bragazza
Sensors 2024, 24(11), 3556; https://doi.org/10.3390/s24113556 - 31 May 2024
Viewed by 2205
Abstract
One challenge in predicting soil parameters using in situ visible and near infrared spectroscopy is the distortion of the spectra due to soil moisture. External parameter orthogonalization (EPO) is a mathematical method to remove unwanted variability from spectra. We created two different EPO [...] Read more.
One challenge in predicting soil parameters using in situ visible and near infrared spectroscopy is the distortion of the spectra due to soil moisture. External parameter orthogonalization (EPO) is a mathematical method to remove unwanted variability from spectra. We created two different EPO correction matrices based on the difference between spectra collected in situ and, respectively, spectra collected from the same soil samples after drying and sieving and after drying, sieving and finely grinding. Spectra from 134 soil samples recorded with two different spectrometers were split into calibration and validation sets and the two EPO corrections were applied. Clay, organic carbon and total nitrogen content were predicted by partial least squares regression for uncorrected and EPO-corrected spectra using models based on the same type of spectra (“within domain”) as well as using laboratory-based models to predict in situ collected spectra (“cross-domain”). Our results show that the within-domain prediction of clay is improved with EPO corrections only for the research grade spectrometer, with no improvement for the other parameters. For the cross-domain predictions, there was a positive effect from both EPO corrections on all parameters. Overall, we also found that in situ collected spectra provided an equally successful prediction as laboratory-based spectra. Full article
(This article belongs to the Special Issue Precision Agriculture and Sensor Systems—2nd Edition)
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19 pages, 5382 KiB  
Article
Development of a Quick-Install Rapid Phenotyping System
by Roberto M. Buelvas, Viacheslav I. Adamchuk, John Lan, Valerio Hoyos-Villegas, Arlene Whitmore and Martina V. Stromvik
Sensors 2023, 23(9), 4253; https://doi.org/10.3390/s23094253 - 25 Apr 2023
Viewed by 1600
Abstract
In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing [...] Read more.
In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale. Full article
(This article belongs to the Special Issue Precision Agriculture and Sensor Systems—2nd Edition)
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