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Spectroscopy and Sensing Technologies for Smart Agriculture
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Spectroscopy and Sensing Technologies for Smart Agriculture

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 4674

Special Issue Editor

Dr. Md Zafar Iqbal

E-Mail Website
Guest Editor
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA
Interests: agricultural engineering; precision agriculture; cotton engineering; smart agriculture

Special Issue Information

Dear Colleagues,

Global agriculture faces mounting challenges as it strives to meet rising demands for food, water, and energy while conserving resources and reducing environmental impact. Issues such as climate change, declining soil health, and the prevalence of pests and diseases further intensify the pressure to adopt more sustainable farming practices.

Spectroscopy and advanced sensing technologies offer powerful tools to address these challenges by enabling non-destructive, real-time monitoring of crops, soils, and the environment. Combined with machine learning, robotics, and IoT-based platforms, these approaches are driving innovations in precision and smart agriculture. From early disease detection to nutrient and water-use optimization, sensor-driven solutions are helping growers make data-informed decisions that improve productivity and sustainability.

This Special Issue, ‘Spectroscopy and Sensing Technologies for Smart Agriculture’, invites original research articles and reviews that highlight the latest advances, applications, and future directions in this rapidly evolving field. Contributions may include, but are not limited to, the following topics:

  • Development and application of spectroscopy techniques (hyperspectral, multispectral, Raman, fluorescence, NIR, etc.).
  • Sensor-based monitoring of crops, soils, water, and environment.
  • Machine learning and AI for spectral data analysis and decision support.
  • UAVs, ground-based, and robotic platforms for agricultural sensing.
  • IoT, wireless networks, and data fusion for smart farming.
  • Automated detection of diseases, stresses, and yield estimation.
  • Smart irrigation, nutrient management, and resource efficiency.

We warmly invite you to contribute your valuable research to this Special Issue and help advance the adoption of data-driven and sustainable agricultural practices.

Dr. Md Zafar Iqbal
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 250 words) can be sent to the Editorial Office for assessment.

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

  • spectroscopy (hyperspectral, multispectral, Raman, NIR, fluorescence)
  • precision and smart agriculture
  • sensor-based technologies
  • machine learning and AI
  • agricultural robotics and automation
  • IoT and data fusion
  • crop and soil monitoring
  • stress, disease, and yield detection
  • smart irrigation and water management
  • decision support systems

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

Jump to: Review

17 pages, 2217 KB  
Article
Beyond Conventional Methods: Rapid and Precise Quantification of Polyphenols in Vigna umbellata via Hyperspectral Imaging Enhanced by Multi-Scale Residual CNN
by Hao Liang, Xin Yang, Nan Wang, Xinyue Lu, Wenwu Zou, Aicun Zhou, Xiongwei Lou and Yufei Lin
Sensors 2026, 26(8), 2356; https://doi.org/10.3390/s26082356 - 11 Apr 2026
Viewed by 543
Abstract
Vigna umbellate, a typical edible and medicinal crop, is rich in polyphenolic compounds with antioxidant, antibacterial, anti-inflammatory, and lipid-regulating activities. However, traditional methods for polyphenol content detection rely on chemical analysis, which is cumbersome and time-consuming, making it difficult to meet the [...] Read more.
Vigna umbellate, a typical edible and medicinal crop, is rich in polyphenolic compounds with antioxidant, antibacterial, anti-inflammatory, and lipid-regulating activities. However, traditional methods for polyphenol content detection rely on chemical analysis, which is cumbersome and time-consuming, making it difficult to meet the demands of high-throughput rapid detection. Although hyperspectral imaging technology offers the potential for non-destructive and rapid detection, existing analytical methods are often limited by issues such as high spectral band redundancy, insufficient feature extraction, and inadequate model stability, which constrain prediction accuracy and practical application potential. To address this, this study proposes a multi-scale residual convolutional neural network (MS-RCNN) based on competitive adaptive reweighted sampling (CARS) for feature band selection, combined with near-infrared hyperspectral imaging technology, to construct a rapid and non-destructive prediction model for the polyphenol content of Vigna umbellata. The model employs a parallel multi-scale convolutional module to extract spectral features with different receptive fields, and incorporates residual connections and adaptive pooling mechanisms to enhance feature reuse and robustness. Experiments compared the performance of partial least squares regression (PLSR), least squares support vector machine (LS-SVM), multi-scale convolutional neural network (MS-CNN), and MS-RCNN models. The results indicate that the MS-RCNN model based on CARS screening achieved the best prediction performance, with a coefficient of determination (R2) of 0.9467, a root mean square error of prediction (RMSEP) of 0.0448, and a residual predictive deviation (RPD) of 4.33. Compared with the optimal PLSR and LSSVM models, its R2 values were improved by 0.2078 and 0.1119, respectively. In summary, the MS-RCNN model proposed in this study enables rapid, non-destructive, and accurate prediction of polyphenol content in Vigna umbellata, providing an efficient technical approach for quality detection of edible and medicinal crops. Full article
(This article belongs to the Special Issue Spectroscopy and Sensing Technologies for Smart Agriculture)
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17 pages, 4725 KB  
Article
Hyperspectral Inversion of Soil Organic Carbon in Daylily Cultivation Areas of Yunzhou District
by Zelong Yao, Xiuping Ran, Chenbo Yang, Ping Li and Rutian Bi
Sensors 2026, 26(2), 740; https://doi.org/10.3390/s26020740 - 22 Jan 2026
Cited by 1 | Viewed by 418
Abstract
Accurate determination of Soil Organic Carbon (SOC), which is the foundation of soil health and safeguards ecological and food security, is crucial in local agricultural production. We aimed to investigate the influence of soil texture on hyperspectral models for predicting SOC content and [...] Read more.
Accurate determination of Soil Organic Carbon (SOC), which is the foundation of soil health and safeguards ecological and food security, is crucial in local agricultural production. We aimed to investigate the influence of soil texture on hyperspectral models for predicting SOC content and to evaluate the role of different preprocessing methods and feature band selection algorithms in improving modeling efficiency. Laboratory-determined SOC content and hyperspectral reflectance data were obtained using soil samples from daylily cultivation areas in Yunzhou District, Datong City. Mathematical transformations, including Savitzky–Golay smoothing (SG), First Derivative (FD), Second Derivative (SD), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV), were applied to the spectral reflectance data. Feature bands extracted based on the successive projection algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to establish SOC content inversion models employing four algorithms: partial least-squares regression (PLSR), Random Forest (RF), Backpropagation Neural Network (BP), and Convolutional Neural Network (CNN). The results indicate the following: (1) Preprocessing can effectively increase the correlation between the soil spectral reflectance process and SOC content. (2) SPA and CARS effectively screened the characteristic bands of SOC in daylily cultivated soil from the spectral curves. The SPA algorithm and CARS selected 4–11 and 9–122 bands, respectively, and both algorithms facilitated model construction. (3) Among all the constructed models, the FD-CARS-PLSR performed most prominently, with coefficients of determination (R2) for the training and validation sets reaching 0.93 and 0.83, respectively, demonstrating high model stability and reliability. (4) Incorporating soil texture as an auxiliary variable into the PLSR inversion model improved the inversion accuracy, with accuracy gains ranging between 0.01 and 0.05. Full article
(This article belongs to the Special Issue Spectroscopy and Sensing Technologies for Smart Agriculture)
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25 pages, 4995 KB  
Article
A Novel Self-Attention Mechanism-Based Dynamic Ensemble Model for Soil Hyperspectral Prediction
by Keyang Yin, Jia Deng, Huixia Li, Chunhui Feng and Jie Peng
Sensors 2026, 26(1), 195; https://doi.org/10.3390/s26010195 - 27 Dec 2025
Viewed by 738
Abstract
Visible–near-infrared spectroscopy enables rapid, non-destructive soil organic matter (SOM) detection, yet its prediction accuracy relies heavily on the effectiveness of the chosen algorithmic models. Weighted Averaging Ensemble Models (WAEM) are robust but face a key challenge: their performance depends on optimal base learner [...] Read more.
Visible–near-infrared spectroscopy enables rapid, non-destructive soil organic matter (SOM) detection, yet its prediction accuracy relies heavily on the effectiveness of the chosen algorithmic models. Weighted Averaging Ensemble Models (WAEM) are robust but face a key challenge: their performance depends on optimal base learner weight allocation, which standard evaluation indices often fail to achieve, risking biased weights and local optima. This study significantly enhances WAEM by determining optimal weights using information extracted from the model training process via seven methods, including reinforcement learning and a self-attention mechanism (Sam). Experiments on 704 soil samples from China’s Tarim River Basin employed a dynamic data structure for real-time weight updating. Results show that six WAEM methods utilizing training process information outperformed conventional evaluation index approaches. Improvements reduced WAEM root mean square error (RMSE) by 0.028–1.279 g kg−1 and increased the correlation coefficient (R2) by up to 0.06. Sam achieved the highest performance, with R2 and RMSE reaching 0.927 and 2.325 g kg−1, respectively. Furthermore, model R2 began converging at 26 base learners, indicating diminishing returns from adding more. This research confirms that dynamic WAEM weight allocation via Sam significantly boosts SOM prediction accuracy, providing a scientific foundation for infrared-based soil monitoring. Full article
(This article belongs to the Special Issue Spectroscopy and Sensing Technologies for Smart Agriculture)
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21 pages, 1974 KB  
Article
Low-Temperature Stress-Induced Changes in Cucumber Plants—A Near-Infrared Spectroscopy and Aquaphotomics Approach for Investigation
by Daniela Moyankova, Petya Stoykova, Petya Veleva, Nikolai K. Christov, Antoniya Petrova, Krasimir Rusanov and Stefka Atanassova
Sensors 2025, 25(24), 7602; https://doi.org/10.3390/s25247602 - 15 Dec 2025
Cited by 1 | Viewed by 888
Abstract
Low temperatures have a significant impact on the growth, development, and productivity of cucumber plants. The potential of near-infrared spectroscopy and the aquaphotomics approach for investigating chilling stress was studied in Voreas F1 and Gergana cultivars. Changes in the spectral patterns of cucumber [...] Read more.
Low temperatures have a significant impact on the growth, development, and productivity of cucumber plants. The potential of near-infrared spectroscopy and the aquaphotomics approach for investigating chilling stress was studied in Voreas F1 and Gergana cultivars. Changes in the spectral patterns of cucumber plants were compared with physiological and metabolic data. Voreas plants were unable to survive seven days of low-temperature stress due to a drastic increase in electrolyte leakage and a decrease in the net photosynthesis rate, stomatal conductance, and transpiration rate. Gergana plants survived chilling by preserving cell membrane integrity and photosynthesis efficiency. During chilling treatment, the content of most metabolites in both cultivars was reduced compared to the controls, yet it was much more pronounced in Voreas. We observed an increased accumulation of cinnamic acid on the seventh day only in the Gergana cultivar. A MicroNIR spectrometer was used for in vivo spectral measurements of cotyledons and the first two leaves. Differences in absorption spectra were observed among control, stressed, and recovered plants, across different days of stress, and between the studied cultivars. The most significant differences were in the 1300–1600 nm range, much smaller for Gergana than Voreas. Aquagrams of the two cultivars also reveal differences in their responses to low temperatures and changes in water molecular structure in the leaves. The errors of prediction for the days of chilling by using PLS models were from 0.96 to 1.14 days for independent validation, depending on the spectral data of different leaves used. Near-infrared spectroscopy and aquaphotomics can be used as additional tools for early detection of stress and investigation of low-temperature tolerance in cucumber cultivars. Full article
(This article belongs to the Special Issue Spectroscopy and Sensing Technologies for Smart Agriculture)
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Review

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29 pages, 6163 KB  
Review
Advances in Flow of Water Through Variably Saturated Soils: A Review of Model Approaches and Experimental Investigations with Use of Sensors
by Peter Uloho Osame, Ebikapaye Peretomode and Haval Kukha Hawez
Sensors 2025, 25(22), 7027; https://doi.org/10.3390/s25227027 - 17 Nov 2025
Cited by 2 | Viewed by 1645
Abstract
The study of the flow of water through soils is important and has applications in many fields such as irrigation in agriculture, engineering, hydrogeology, and earth sciences. Many research efforts have been focused on different aspects of the subject of flow through soils. [...] Read more.
The study of the flow of water through soils is important and has applications in many fields such as irrigation in agriculture, engineering, hydrogeology, and earth sciences. Many research efforts have been focused on different aspects of the subject of flow through soils. These include flow through the vadose zone where the flow is transient, saturated flow, soil water evaporation, Darcian or laminar flow, macroporous or differential flow, flow through homogeneous soils, and flow through heterogeneous soils. Although Darcy’s law is the most fundamental law governing soil water subsurface flow, it considers a linear relation between flow velocity and pressure gradient. Formulation of Darcy’s law is based on steady flow of incompressible liquid when the porous medium is isotropic, homogeneous, and saturated. However, these classical representations of water flow are not adequate when considering flow through natural soils, due to influences caused by the existence of macropores and spatial variability of soil properties. Despite researchers’ non-linear models which modify Darcy’s law, such as Richard’s equation for transient unsaturated flow of water in soils, determination of soil hydraulic properties also requires other techniques and measurement methods. This study focuses on model approaches and experimental investigations of water flow through the soil subsurface with instruments and sensors for determination of hydraulic properties and parameters for flow characterisation. It critically examines challenges and the accuracy of best practices and aims to present novel methods of experimental approach for potential solutions. Full article
(This article belongs to the Special Issue Spectroscopy and Sensing Technologies for Smart Agriculture)
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