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Sensors and Artificial Intelligence in Smart Agriculture

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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: 31 October 2024 | Viewed by 3820

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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

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
multispectral image
geotagged data
machine learning
neural network
precision agriculture

Published Papers (3 papers)

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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 - 08 Oct 2023

Viewed by 839

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 1 | Viewed by 909

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 2 | Viewed by 1569

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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