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

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 12301

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Special Issue Editor

Dr. Zoran Stamenkovic

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Guest Editor

1. Institute for Computer Science, University of Potsdam, An der Bahn 2, 14476 Potsdam, Germany
2. IHP-Leibniz-Institut fur Innovative Mikroelektronik, Im Technologiepark 25, 15236 Frankfurt Oder, Germany
Interests: precision agriculture; machine learning; sensors; microelectronics
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Special Issue Information

Dear Colleagues,

We seek manuscripts on advanced machine learning techniques and tools that extract knowledge and learn from large volumes of multimodal agricultural data. The following areas are of the highest interest:

Multimodal/multisource data analytics techniques for precision agriculture (data obtained from wireless remote sensors, cameras, drones and databases with different spatial and temporal sampling patterns);
New feature extraction and knowledge extraction tools that help identify the most discriminative variables for detection/classification purposes, as well as those variables correlated with the onset of pests and diseases;
State-of-the-art machine learning tools for precision agriculture (deep neural networks for detection of pests and diseases, kernel methods for extraction nonlinear correlations among variables, and Gaussian processes to correlate and solve spatiotemporal regression problems involving multispectral images from drones and geotagged data from remote sensors).

Dr. Zoran Stamenkovic
Guest Editor

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Keywords

sensor
multispectral image
geotagged data
machine learning
neural network
precision agriculture

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Published Papers (6 papers)

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Research

27 pages, 3135 KiB

Open AccessArticle

Potato Late Blight Outbreak: A Study on Advanced Classification Models Based on Meteorological Data

by Parama Bagchi, Barbara Sawicka, Zoran Stamenkovic, Dušan Marković and Debotosh Bhattacharjee

Sensors 2024, 24(23), 7864; https://doi.org/10.3390/s24237864 - 9 Dec 2024

Viewed by 1688

Abstract

While past research has emphasized the importance of late blight infection detection and classification, anticipating the potato late blight infection is crucial from the economic point of view as it helps to significantly reduce the production cost. Furthermore, it is necessary to minimize the exposure of potatoes to harmful chemicals and pesticides due to their potential adverse effects on the human immune system. Our work is based on the precise classification of late blight infections in potatoes in European countries using real-time data from 1980 to 2000. To predict the potato late blight outbreak, we incorporated several hybrid machine learning models, as well as a unique combination of stacking classifier and logistic regression, achieving the highest prediction accuracy of 87.22%. Further enhancements of these models and the use of new data sources may lead to a higher late blight prediction accuracy and, consequently, a higher efficiency in managing potatoes’ health. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence in Smart Agriculture)

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34 pages, 3298 KiB

Open AccessArticle

Bee Together: Joining Bee Audio Datasets for Hive Extrapolation in AI-Based Monitoring

by Augustin Bricout, Philippe Leleux, Pascal Acco, Christophe Escriba, Jean-Yves Fourniols, Georges Soto-Romero and Rémi Floquet

Sensors 2024, 24(18), 6067; https://doi.org/10.3390/s24186067 - 19 Sep 2024

Cited by 1 | Viewed by 2335

Abstract

Beehive health monitoring has gained interest in the study of bees in biology, ecology, and agriculture. As audio sensors are less intrusive, a number of audio datasets (mainly labeled with the presence of a queen in the hive) have appeared in the literature, and interest in their classification has been raised. All studies have exhibited good accuracy, and a few have questioned and revealed that classification cannot be generalized to unseen hives. To increase the number of known hives, a review of open datasets is described, and a merger in the form of the “BeeTogether” dataset on the open Kaggle platform is proposed. This common framework standardizes the data format and features while providing data augmentation techniques and a methodology for measuring hives’ extrapolation properties. A classical classifier is proposed to benchmark the whole dataset, achieving the same good accuracy and poor hive generalization as those found in the literature. Insight into the role of the frequency of the classification of the presence of a queen is provided, and it is shown that this frequency mostly depends on a colony’s belonging. New classifiers inspired by contrastive learning are introduced to circumvent the effect of colony belonging and obtain both good accuracy and hive extrapolation abilities when learning changes in labels. A process for obtaining absolute labels was prototyped on an unsupervised dataset. Solving hive extrapolation with a common open platform and contrastive approach can result in effective applications in agriculture. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence in Smart Agriculture)

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22 pages, 12863 KiB

Open AccessArticle

Remote and Proximal Sensors Data Fusion: Digital Twins in Irrigation Management Zoning

by Hugo Rodrigues, Marcos B. Ceddia, Wagner Tassinari, Gustavo M. Vasques, Ziany N. Brandão, João P. S. Morais, Ronaldo P. Oliveira, Matheus L. Neves and Sílvio R. L. Tavares

Sensors 2024, 24(17), 5742; https://doi.org/10.3390/s24175742 - 4 Sep 2024

Cited by 2 | Viewed by 1169

Abstract

The scientific field of precision agriculture employs increasingly innovative techniques to optimize inputs, maximize profitability, and reduce environmental impact. However, obtaining a high number of soil samples is challenging in order to make precision agriculture viable. There is a trade-off between the amount of data needed and the time and resources spent to obtain these data compared to the accuracy of the maps produced with more or fewer points. In the present study, the research was based on an exhaustive dataset of apparent electrical conductivity (aEC) containing 3906 points distributed along 26 transects with spacing between each of up to 40 m, measured by the proximal soil sensor EM38-MK2, for a grain-producing area of 72 ha in São Paulo, Brazil. A second sparse dataset was simulated, showing only four transects with a 400 m distance and, in the end, only 162 aEC points. The aEC map via ordinary kriging (OK) from the grid with 26 transects was considered the reference, and two other mapping approaches were used to map aEC via sparse grid: kriging with external drift (KED) and geographically weighted regression (GWR). These last two methods allow the increment of auxiliary variables, such as those obtained by remote sensors that present spatial resolution compatible with the pivot scale, such as data from the Landsat-8, Aster, and Sentinel-2 satellites, as well as ten terrain covariates derived from the Alos Palsar digital elevation model. The KED method, when used with the sparse dataset, showed a relatively good fit to the aEC data (R² = 0.78), with moderate prediction accuracy (MAE = 1.26, RMSE = 1.62) and reasonable predictability (RPD = 1.76), outperforming the GWR method, which had the weakest performance (R² = 0.57, MAE = 1.78, RMSE = 2.30, RPD = 0.81). The reference aEC map using the exhaustive dataset and OK showed the highest accuracy with an R² of 0.97, no systematic bias (ME = 0), and excellent precision (RMSE = 0.56, RPD = 5.86). Management zones (MZs) derived from these maps were validated using soil texture data from clay samples measured at 0–10 cm depth in a grid of 72 points. The KED method demonstrated the highest potential for accurately defining MZs for irrigation, producing a map that closely resembled the reference MZ map, thereby providing reliable guidance for irrigation management. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence in Smart Agriculture)

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27 pages, 7201 KiB

Open AccessArticle

High-Order Neural-Network-Based Multi-Model Nonlinear Adaptive Decoupling Control for Microclimate Environment of Plant Factory

by Yonggang Wang, Ziqi Chen, Yingchun Jiang and Tan Liu

Sensors 2023, 23(19), 8323; https://doi.org/10.3390/s23198323 - 8 Oct 2023

Cited by 1 | Viewed by 1528

Abstract

Plant factory is an important field of practice in smart agriculture which uses highly sophisticated equipment for precision regulation of the environment to ensure crop growth and development efficiently. Environmental factors, such as temperature and humidity, significantly impact crop production in a plant factory. Given the inherent complexities of dynamic models associated with plant factory environments, including strong coupling, strong nonlinearity and multi-disturbances, a nonlinear adaptive decoupling control approach utilizing a high-order neural network is proposed which consists of a linear decoupling controller, a nonlinear decoupling controller and a switching function. In this paper, the parameters of the controller depend on the generalized minimum variance control rate, and an adaptive algorithm is presented to deal with uncertainties in the system. In addition, a high-order neural network is utilized to estimate the unmolded nonlinear terms, consequently mitigating the impact of nonlinearity on the system. The simulation results show that the mean error and standard error of the traditional controller for temperature control are 0.3615 and 0.8425, respectively. In contrast, the proposed control strategy has made significant improvements in both indicators, with results of 0.1655 and 0.6665, respectively. For humidity control, the mean error and standard error of the traditional controller are 0.1475 and 0.441, respectively. In comparison, the proposed control strategy has greatly improved on both indicators, with results of 0.0221 and 0.1541, respectively. The above results indicate that even under complex conditions, the proposed control strategy is capable of enabling the system to quickly track set values and enhance control performance. Overall, precise temperature and humidity control in plant factories and smart agriculture can enhance production efficiency, product quality and resource utilization. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence in Smart Agriculture)

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31 pages, 4136 KiB

Open AccessArticle

A Lightweight Fault-Detection Scheme for Resource-Constrained Solar Insecticidal Lamp IoTs

by Xing Yang, Lei Shu, Kailiang Li, Edmond Nurellari, Zhiqiang Huo and Yu Zhang

Sensors 2023, 23(15), 6672; https://doi.org/10.3390/s23156672 - 25 Jul 2023

Cited by 2 | Viewed by 1658

Abstract

The Solar Insecticidal Lamp Internet of Things (SIL-IoTs) is an emerging paradigm that extends Internet of Things (IoT) technology to agricultural-enabled electronic devices. Ensuring the dependability and safety of SIL-IoTs is crucial for pest monitoring, prediction, and prevention. However, SIL-IoTs can experience system performance degradation due to failures, which can be attributed to complex environmental changes and device deterioration in agricultural settings. This study proposes a sensor-level lightweight fault-detection scheme that takes into account realistic constraints such as computational resources and energy. By analyzing fault characteristics, we designed a distributed fault-detection method based on operation condition differences, interval number residuals, and feature residuals. Several experiments were conducted to validate the effectiveness of the proposed method. The results demonstrated that our method achieves an average F1-score of 95.59%. Furthermore, the proposed method only consumes an additional 0.27% of the total power, and utilizes 0.9% RAM and 3.1% Flash on the Arduino of the SIL-IoTs node. These findings indicated that the proposed method is lightweight and energy-efficient. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence in Smart Agriculture)

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21 pages, 5970 KiB

Open AccessArticle

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots

by Andres F. Duque, Diego Patino, Julian D. Colorado, Eliel Petro, Maria C. Rebolledo, Ivan F. Mondragon, Natalia Espinosa, Nelson Amezquita, Oscar D. Puentes, Diego Mendez and Andres Jaramillo-Botero

Sensors 2023, 23(13), 5917; https://doi.org/10.3390/s23135917 - 26 Jun 2023

Cited by 8 | Viewed by 3027

Abstract

The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs. Full article

(This article belongs to the Special Issue Sensors and Artificial Intelligence in Smart Agriculture)

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