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Feature Papers in Smart Agriculture 2024

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 5693

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Special Issue Editor

Prof. Dr. Wonsuk (Daniel) Lee

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Guest Editor

Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611-0570, USA
Interests: precision agriculture; artificial intelligence; sensor development; machine vision/image processing; GNSS/GIS; variable rate technology; yield mapping; machine systems design; instrumentation; remote sensing; NIR spectroscopy; farm automation
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Special Issue Information

Dear Colleagues,

We are pleased to announce that the section Smart Agriculture is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our section and outstanding scholars in this research field. We welcome contributions as well as recommendations from the EBMs.

The purpose of this Special Issue is to publish a set of papers that typifies the very best insightful and influential original articles or reviews in which our section’s EBMs and outstanding scholars discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be published in a printed edition book after the deadline and will be extensively promoted. The Special Issue engages in topics such as artificial intelligence, IoT, UAVs, and robots, and their applications in the field of smart farming, precision livestock management, aquaculture, greenhouse technology, etc. In addition, any articles related to smart agriculture are welcome that highlight technological innovation in software and hardware development applied to crop and animal production.

We would also like to take this opportunity to ask more scholars to join the section Smart Agriculture so that we can work together to further develop this exciting field of research.

Prof. Dr. Wonsuk (Daniel) Lee
Guest Editor

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.

Keywords

sensor
artificial intelligence
IoT
UAV
robot
smart agriculture
smart farming
precision livestock management

Published Papers (5 papers)

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21 pages, 18332 KiB

Open AccessArticle

Automated Region of Interest-Based Data Augmentation for Fallen Person Detection in Off-Road Autonomous Agricultural Vehicles

by Hwapyeong Baek, Seunghyun Yu, Seungwook Son, Jongwoong Seo and Yongwha Chung

Sensors 2024, 24(7), 2371; https://doi.org/10.3390/s24072371 - 08 Apr 2024

Viewed by 351

Abstract

Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry. Full article

(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2024)

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15 pages, 2559 KiB

Open AccessArticle

Evaluating Bacterial Nanocellulose Interfaces for Recording Surface Biopotentials from Plants

by James Reynolds, Michael Wilkins, Devon Martin, Matthew Taggart, Kristina R. Rivera, Meral Tunc-Ozdemir, Thomas Rufty, Edgar Lobaton, Alper Bozkurt and Michael A. Daniele

Sensors 2024, 24(7), 2335; https://doi.org/10.3390/s24072335 - 06 Apr 2024

Viewed by 334

Abstract

The study of plant electrophysiology offers promising techniques to track plant health and stress in vivo for both agricultural and environmental monitoring applications. Use of superficial electrodes on the plant body to record surface potentials may provide new phenotyping insights. Bacterial nanocellulose (BNC) is a flexible, optically translucent, and water-vapor-permeable material with low manufacturing costs, making it an ideal substrate for non-invasive and non-destructive plant electrodes. This work presents BNC electrodes with screen-printed carbon (graphite) ink-based conductive traces and pads. It investigates the potential of these electrodes for plant surface electrophysiology measurements in comparison to commercially available standard wet gel and needle electrodes. The electrochemically active surface area and impedance of the BNC electrodes varied based on the annealing temperature and time over the ranges of 50 °C to 90 °C and 5 to 60 min, respectively. The water vapor transfer rate and optical transmittance of the BNC substrate were measured to estimate the level of occlusion caused by these surface electrodes on the plant tissue. The total reduction in chlorophyll content under the electrodes was measured after the electrodes were placed on maize leaves for up to 300 h, showing that the BNC caused only a 16% reduction. Maize leaf transpiration was reduced by only 20% under the BNC electrodes after 72 h compared to a 60% reduction under wet gel electrodes in 48 h. On three different model plants, BNC–carbon ink surface electrodes and standard invasive needle electrodes were shown to have a comparable signal quality, with a correlation coefficient of >0.9, when measuring surface biopotentials induced by acute environmental stressors. These are strong indications of the superior performance of the BNC substrate with screen-printed graphite ink as an electrode material for plant surface biopotential recordings. Full article

(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2024)

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18 pages, 13630 KiB

Open AccessArticle

Temporal Stability of Management Zone Patterns: Case Study with Contact and Non-Contact Soil Electrical Conductivity Sensors in Dryland Pastures

by João Serrano, Shakib Shahidian, José Marques da Silva, Luís L. Paniágua, Francisco J. Rebollo and Francisco J. Moral

Sensors 2024, 24(5), 1623; https://doi.org/10.3390/s24051623 - 01 Mar 2024

Viewed by 1410

Abstract

Precision agriculture (PA) intends to validate technological tools that capture soil and crop spatial variability, which constitute the basis for the establishment of differentiated management zones (MZs). Soil apparent electrical conductivity (EC_a) sensors are commonly used to survey soil spatial variability. It is essential for surveys to have temporal stability to ensure correct medium- and long-term decisions. The aim of this study was to assess the temporal stability of MZ patterns using different types of EC_a sensors, namely an EC_a contact-type sensor (Veris 2000 XA, Veris Technologies, Salina, KS, USA) and an electromagnetic induction sensor (EM-38, Geonics Ltd., Mississauga, ON, Canada). These sensors were used in four fields of dryland pastures in the Alentejo region of Portugal. The first survey was carried out in October 2018, and the second was carried out in September 2020. Data processing involved synchronizing the geographic coordinates obtained using the two types of sensors in each location and establishing MZs based on a geostatistical analysis of elevation and EC_a data. Although the basic technologies have different principles (contact versus non-contact sensors), the surveys were carried out at different soil moisture conditions and were temporarily separated (about 2 years); the EC_a measurements showed statistically significant correlations in all experimental fields (correlation coefficients between 0.449 and 0.618), which were reflected in the spatially stable patterns of the MZ maps (averaging 52% of the total area across the four experimental fields). These results provide perspectives for future developments, which will need to occur in the creation of algorithms that allow the spatial variability and temporal stability of EC_a to be validated through smart soil sampling and analysis to generate recommendations for sustained soil amendment or fertilization. Full article

(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2024)

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15 pages, 2419 KiB

Open AccessArticle

Evaluating the Soil Quality Index Using Three Methods to Assess Soil Fertility

by Hiba Chaudhry, Hiteshkumar Bhogilal Vasava, Songchao Chen, Daniel Saurette, Anshu Beri, Adam Gillespie and Asim Biswas

Sensors 2024, 24(3), 864; https://doi.org/10.3390/s24030864 - 29 Jan 2024

Viewed by 1718

Abstract

Soil health plays a crucial role in crop production, both in terms of quality and quantity, highlighting the importance of effective methods for preserving soil quality to ensure global food security. Soil quality indices (SQIs) have been widely utilized as comprehensive measures of soil function by integrating multiple physical, chemical, and biological soil properties. Traditional SQI analysis involves laborious and costly laboratory analyses, which limits its practicality. To overcome this limitation, our study explores the use of visible near-infrared (vis-NIR) spectroscopy as a rapid and non-destructive alternative for predicting soil properties and SQIs. This study specifically focused on seven soil indicators that contribute to soil fertility, including pH, organic matter (OM), potassium (K), calcium (Ca), magnesium (Mg), available phosphorous (P), and total nitrogen (TN). These properties play key roles in nutrient availability, pH regulation, and soil structure, influencing soil fertility and overall soil health. By utilizing vis-NIR spectroscopy, we were able to accurately predict the soil indicators with good accuracy using the Cubist model (R² = 0.35–0.93), offering a cost-effective and environmentally friendly alternative to traditional laboratory analyses. Using the seven soil indicators, we looked at three different approaches for calculating and predicting the SQI, including: (1) measured SQI (SQI_m), which is derived from laboratory-measured soil properties; (2) predicted SQI (SQI_p), which is calculated using predicted soil properties from spectral data; and (3) direct prediction of SQI (SQI_dp), The findings demonstrated that SQI_dp exhibited a higher accuracy (R² = 0.90) in predicting soil quality compared to SQI_p (R² = 0.23). Full article

(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2024)

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22 pages, 2847 KiB

Open AccessArticle

Rapid Determination of Wine Grape Maturity Level from pH, Titratable Acidity, and Sugar Content Using Non-Destructive In Situ Infrared Spectroscopy and Multi-Head Attention Convolutional Neural Networks

by Eleni Kalopesa, Theodoros Gkrimpizis, Nikiforos Samarinas, Nikolaos L. Tsakiridis and George C. Zalidis

Sensors 2023, 23(23), 9536; https://doi.org/10.3390/s23239536 - 30 Nov 2023

Viewed by 1134

Abstract

In the pursuit of enhancing the wine production process through the utilization of new technologies in viticulture, this study presents a novel approach for the rapid assessment of wine grape maturity levels using non-destructive, in situ infrared spectroscopy and artificial intelligence techniques. Building [...] Read more.

^{\circ}

Brix) from the visible and near-infrared (VNIR) and short-wave infrared (SWIR) regions, this research expands its scope to encompass pH and titratable acidity, critical parameters determining the grape maturity degree, and in turn, wine quality, offering a more representative estimation pathway. Data were collected from four grape varieties—Chardonnay, Malagouzia, Sauvignon Blanc, and Syrah—during the 2023 harvest and pre-harvest phenological stages in the vineyards of Ktima Gerovassiliou, northern Greece. A comprehensive spectral library was developed, covering the VNIR–SWIR spectrum (350–2500 nm), with measurements performed in situ. Ground truth data for pH, titratable acidity, and sugar content were obtained using conventional laboratory methods: total soluble solids (TSS) (

^{\circ}

Brix) by refractometry, titratable acidity by titration (expressed as mg tartaric acid per liter of must) and pH by a pH meter, analyzed at different maturation stages in the must samples. The maturity indicators were predicted from the point hyperspectral data by employing machine learning algorithms, including Partial Least Squares regression (PLS), Random Forest regression (RF), Support Vector Regression (SVR), and Convolutional Neural Networks (CNN), in conjunction with various pre-processing techniques. Multi-output models were also considered to simultaneously predict all three indicators to exploit their intercorrelations. A novel multi-input–multi-output CNN model was also proposed, incorporating a multi-head attention mechanism and enabling the identification of the spectral regions it focuses on, and thus having a higher interpretability degree. Our results indicate high accuracy in the estimation of sugar content, pH, and titratable acidity, with the best models yielding mean

R^{2}

values of 0.84, 0.76, and 0.79, respectively, across all properties. The multi-output models did not improve the prediction results compared to the best single-output models, and the proposed CNN model was on par with the next best model. The interpretability analysis highlighted that the CNN model focused on spectral regions associated with the presence of sugars (i.e., glucose and fructose) and of the carboxylic acid group. This study underscores the potential of portable spectrometry for real-time, non-destructive assessments of wine grape maturity, thereby providing valuable tools for informed decision making in the wine production industry. By integrating pH and titratable acidity into the analysis, our approach offers a holistic view of grape quality, facilitating more comprehensive and efficient viticultural practices. Full article

(This article belongs to the Special Issue Feature Papers in Smart Agriculture 2024)

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