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Plant Sensors in Precision Agriculture
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Plant Sensors in Precision Agriculture

A Special Issue of Plants (ISSN 2223-7747) belonging to the section "Plant Modeling".

Deadline for manuscript submissions: 28 February 2027 | Viewed by 2813

Editors

Dr. Marko Kostić

E-Mail Website
Guest Editor
Faculty of Agriculture, University of Novi Sad, Trg. D. Obradovića 8, 21000 Novi Sad, Serbia
Interests: precision agriculture; unmanned aerial vehicles (UAVs); proximal sensing; multispectral imaging; yield prediction models; nitrogen fertilization optimization; GIS and GPS applications in agriculture; digital transformation in agriculture; sensor-based crop monitoring; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Goran Kitić

E-Mail Website
Guest Editor
BioSense Institute—Research Institute for Information Technologies in Biosystems, University of Novi Sad, Dr. Zorana Đinđića 1a, 21000 Novi Sad, Serbia
Interests: biosensors; precision agriculture; UV/Vis spectroscopy

Special Issue Information

Dear Colleagues,

Recent advances in plant sensing technologies have revolutionized the way we monitor, analyze, and manage crops in precision agriculture. Sensor-driven approaches, encompassing proximal and remote sensing systems, provide real-time insights into plant physiological traits, soil–plant interactions, and environmental dynamics. These tools enable the assessment of nutrient status, water use efficiency, and stress detection at multiple spatial and temporal scales, facilitating more sustainable and data-informed crop management decisions.

This Special Issue, “Plant Sensors in Precision Agriculture,” aims to highlight innovative research and practical applications of plant-based, proximal, and remote sensors for sustainable crop production. We welcome contributions addressing sensor development, calibration, data fusion, and integration with machine learning and decision support systems. Studies exploring correlations between sensor-derived vegetation indices and physiological or agronomic parameters are also encouraged.

By bringing together multidisciplinary research from agronomy, engineering, and data science, this Special Issue seeks to advance understanding of how digital technologies can improve resource use efficiency, reduce environmental impact, and enhance crop productivity. Both original research papers and review articles are invited to provide new insights into the role of plant sensors in transforming agriculture toward a more resilient and sustainable future.

Dr. Marko Kostić
Dr. Goran Kitić
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants 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 2700 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

  • plant sensors
  • proximal sensing
  • remote sensing
  • multispectral and hyperspectral imaging
  • UAV-based sensing
  • vegetation indices
  • precision agriculture
  • crop monitoring
  • nutrient management
  • soil–plant interactions
  • data fusion
  • machine learning
  • sustainable crop management
  • phenotyping
  • digital agriculture

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Published Papers (4 papers)

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Research

25 pages, 34207 KB  
Article
Dynamic Prediction of Maize Tasseling Stage Based on UAV LiDAR Time-Series Plant Height Growth Curves: A Framework Coupling UAV-CHM-POI
by Jixuan Yan, Kejing Cheng, Wenning Wang, Zichen Guo, Qiang Li, Jiaqin Yuan, Guang Li, Weiwei Ma and Yinshan Ma
Plants 2026, 15(15), 2382; https://doi.org/10.3390/plants15152382 - 3 Aug 2026
Viewed by 323
Abstract
Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on [...] Read more.
Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of “time-series perception–dynamic simulation–feature identification–early prediction”, providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research. Full article
(This article belongs to the Special Issue Plant Sensors in Precision Agriculture)
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28 pages, 1445 KB  
Article
Sentinel-2 and Unmanned Aerial Vehicle (UAV) Imagery for Irrigation Scheduling in Fodder Maize: A Comparative Remote Sensing Approach
by Nuria Aide López Hernández, Victor Manuel Rodríguez Moreno, Ricardo Israel Ramírez Gottfried, Ramón Trucíos Caciano, Marco Antonio Inzunza Ibarra and Aldo Rafael Martínez Sifuentes
Plants 2026, 15(15), 2265; https://doi.org/10.3390/plants15152265 - 24 Jul 2026
Viewed by 453
Abstract
Accurate estimation of crop water requirements is essential to improve irrigation efficiency for forage maize production. This study compared satellite- and UAV-derived normalized difference vegetation index (NDVI) models for estimating crop coefficients (Kc) and evaluated their operational performance for irrigation scheduling. [...] Read more.
Accurate estimation of crop water requirements is essential to improve irrigation efficiency for forage maize production. This study compared satellite- and UAV-derived normalized difference vegetation index (NDVI) models for estimating crop coefficients (Kc) and evaluated their operational performance for irrigation scheduling. Kc–NDVI models were developed during the 2023 growing season and subsequently validated under field conditions during the 2024 season in two forage maize hybrids (N83N5 and Matador) under three irrigation strategies: conventional producer irrigation (ID1), satellite-based irrigation scheduling (ID2), and UAV-based irrigation scheduling (ID3). Both NDVI sources exhibited strong relationships with Kc, with higher calibration accuracy for the UAV model (R2 = 0.9414) than for the satellite model (R2 = 0.8278). The UAV-based model applied 23–30% less irrigation water, maintaining high water productivity but also reducing crop growth, forage yield, and nutritional quality. In contrast, satellite-based irrigation scheduling promoted greater crop growth and produced the highest forage yield, reaching 59.8 t ha−1 in hybrid N83N5 while maintaining efficient water use. This treatment also improved forage quality by increasing dry matter and starch concentrations while reducing fiber fractions. The findings highlight the complementary potential of satellite and UAV imagery in precision irrigation and underscore the trade-offs between spatial detail, temporal resolution, and operational scalability. Furthermore, the results demonstrate that a stronger Kc–NDVI relationship does not necessarily translate into improved irrigation scheduling performance. Under the conditions evaluated, the satellite-based model provided the best balance between water use, forage yield, and nutritional quality. Full article
(This article belongs to the Special Issue Plant Sensors in Precision Agriculture)
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16 pages, 7696 KB  
Article
Development of a New Handheld Device for Measuring Photosynthetic Carbon Dioxide Assimilation in Plant Leaves
by Elizaveta Kozlova, Denis Zbruev, Alexey Baburkin, Ekaterina Sukhova and Vladimir Sukhov
Plants 2026, 15(12), 1888; https://doi.org/10.3390/plants15121888 - 18 Jun 2026
Viewed by 654
Abstract
With increasing constraints on extensive farming—including soil degradation, salinisation and more frequent climatic anomalies—the development of ‘smart’ agriculture requires the integration of affordable, non-invasive methods for monitoring the physiological state of plants. A key indicator for assessing productivity and the early detection of [...] Read more.
With increasing constraints on extensive farming—including soil degradation, salinisation and more frequent climatic anomalies—the development of ‘smart’ agriculture requires the integration of affordable, non-invasive methods for monitoring the physiological state of plants. A key indicator for assessing productivity and the early detection of stress is the rate of photosynthetic CO2 assimilation (A); however, widely available commercial gas analysers are characterised by high cost, technical complexity and considerable weight, which limits their use in large-scale field studies. Here, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO2 concentration using simple infrared sensors and without maintaining a constant air flow around the leaf. A comparison was carried out between a prototype of the developed system and a commercial gas analyser when measuring leaf assimilation under irrigation and simulated drought conditions. The results demonstrated the consistency of the readings from the two systems. The developed system is characterised by its compact size, low cost, and the absence of moving parts and consumables. The proposed system has the potential to be effective for large-scale screening tasks and rapid diagnosis of stress-induced changes; it represents a promising, affordable tool for addressing applied tasks in precision agriculture, environmental monitoring and physiological research. Full article
(This article belongs to the Special Issue Plant Sensors in Precision Agriculture)
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26 pages, 5676 KB  
Article
Light-Induced Changes in RGB Reflectance Parameters in Wheat and Pea Leaves in the Minute Range
by Yuriy Zolin, Alyona Popova, Lyubov Yudina, Leonid Andryushaev, Vladimir Sukhov and Ekaterina Sukhova
Plants 2026, 15(8), 1184; https://doi.org/10.3390/plants15081184 - 12 Apr 2026
Viewed by 759
Abstract
Parameters of reflected light, measured in narrow or broad spectral bands, are widely analyzed for remote and proximal sensing of plant responses to stressors. Specifically, parameters of reflectance in red (R), green (G), and blue (B) spectral bands measured using simple color images [...] Read more.
Parameters of reflected light, measured in narrow or broad spectral bands, are widely analyzed for remote and proximal sensing of plant responses to stressors. Specifically, parameters of reflectance in red (R), green (G), and blue (B) spectral bands measured using simple color images can be sensitive to characteristics of plants. The conventional view is that RGB reflectance primarily reveals long-term changes in plants (days, weeks, etc.). In this study, we investigated light-induced changes in RGB reflectance in wheat (Triticum aestivum L.) and pea (Pisum sativum L.) leaves. Illumination increased this reflectance for about 10 min in wheat and about 15–20 min in pea; these changes relaxed after light intensity was decreased. The changes in RGB reflectance were strongly related to the effective quantum yield of photosystem II and non-photochemical quenching of chlorophyll fluorescence under high light intensity; these relations were absent under low light intensity. We hypothesized that changes in both RGB reflectance and photosynthetic parameters were related to the light-induced changes in chloroplast localization. A simple mathematical model of optical properties and photosynthesis in leaves was developed; results of the model-based analysis supported the proposed hypothesis. Experimental analysis of the dynamics of light transmittance additionally supported this hypothesis. Our results thus show that RGB imaging can be sensitive to fast changes in plants. Full article
(This article belongs to the Special Issue Plant Sensors in Precision Agriculture)
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