Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.6
4.1
Journals
Agronomy
Special Issues
Advancing Plant Phenotyping for Precision Crop Growth Monitoring and Forecast...
share announcement

Advancing Plant Phenotyping for Precision Crop Growth Monitoring and Forecast Leveraging In Situ, Remote and Proximal Sensing, and AI

A Special Issue of Agronomy (ISSN 2073-4395) belonging to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1769

Editors

Dr. Rubi Quiñones

E-Mail Website
Guest Editor
Department of Computer Science, Southern Illinois University Edwardsville, Edwardsville, IL 62026, USA
Interests: computer vision; artificial intelligence; remote sensing; plant phenotyping
Prof. Dr. Francisco Muñoz-Arriola

E-Mail Website
Guest Editor
1. School of Natural Resources, The University of Nebraska—Lincoln, Lincoln, NE 68588, USA
2. Department of Biological Systems Engineering, The University of Nebraska—Lincoln, Lincoln, NE 68588, USA
Interests: artificial intelligence; predictive analytics; remote sensing; plant phenotyping; genetics-by-environment modeling; climate analytics; energy systems

Special Issue Information

Dear Colleagues,

Plant phenotyping is becoming increasingly important for precision agriculture, enabling detailed assessment of crop growth, development, architecture, stress responses, and productivity across spatial and temporal scales. Recent advances in in situ, proximal, and remote sensing, ranging from field sensors to high-throughput imaging platforms to airborne systems and satellites, are transforming the way plant traits are measured, monitored, and forecasted. When combined with artificial intelligence and computer vision, these sensing technologies provide powerful tools for automated plant detection, segmentation, trait extraction, health assessment, and predictive modeling in diverse agricultural environments.

This Special Issue aims to highlight innovative research advancing plant phenotyping for precision crop growth monitoring and forecasting through the integration of sensing technologies, AI, and data-driven analytics. We welcome interdisciplinary contributions spanning agronomy, plant science, engineering, geospatial science, and computer science that develop robust, scalable methods for characterizing phenotype–genotype–environment interactions and converting complex sensing data into actionable insights.

Topics of interest include, but are not limited to, plant and canopy segmentation using computer vision; extraction of structural, morphological, physiological, and biochemical traits from in situ, proximal, and remote sensing data; high-throughput phenotyping systems; crop growth stage detection; crop health and stress diagnosis; yield forecasting; multimodal and multi-source data fusion; predictive modeling; and AI-enabled decision support for precision agriculture. Contributions addressing climate resilience are particularly encouraged.

Dr. Rubi Quiñones
Prof. Dr. Francisco Muñoz-Arriola
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. Agronomy 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

  • artificial intelligence
  • remote sensing
  • computer vision
  • crop growth monitoring
  • machine learning
  • deep learning
  • precision agriculture
  • multi-source data fusion
  • yield prediction
  • climate-resilient agriculture

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 1653 KB  
Article
Design and Greenhouse Sensing-Layer Validation of a Low-Cost Modular Agricultural Robot for Environmental Sensing, Telemetry and Remote Supervision in Precision Agriculture
by Bálint Ambrus, Gergely Teschner, Attila József Kovács, Miklós Neményi, Norbert Boros and Anikó Nyéki
Agronomy 2026, 16(12), 1139; https://doi.org/10.3390/agronomy16121139 - 10 Jun 2026
Viewed by 544
Abstract
Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node [...] Read more.
Wireless sensor networks (WSNs), IoT-enabled sensing, and mobile platforms are increasingly used in precision agriculture, but fixed stations cannot fully capture within-field or canopy-level variability. This study developed and greenhouse-tested a low-cost modular tracked robot as a wireless environmental-sensing and telemetry research node for future crop-monitoring applications, rather than as a fully validated autonomous field robot. An open-source tracked chassis was extended with Raspberry Pi edge computing, a Cube Orange autopilot, RTK-capable GNSS, 5G/VPN/MAVLink communication, and BME280, BH1750, MLX90614, RGB camera, and LiDAR-ready sensing. The platform measured 35 × 25 × 40 cm, weighed 6.4 kg, operated from a 12 V supply, and provided about 4 h of runtime under favorable conditions. Sensor data were logged locally and could be transmitted remotely, while telemetry was visualized in QGroundControl. The environmental sensing layer was compared with a calibrated Libelium Smart Agriculture Pro station in a greenhouse using 70 synchronized samples per variable across three sessions. Because the two nodes were placed close to one another but were not strictly co-located, the comparison quantifies operational sensing differences under greenhouse microclimatic gradients rather than pure laboratory sensor error. Regression was retained only as a trend-tracking metric, while method-comparison interpretation was added using bias and Bland–Altman limits of agreement. The pressure channel showed strong trend tracking (R2 = 0.992, RMSE = 0.024 hPa), whereas air temperature (R2 = 0.756, RMSE = 2.537 °C) and relative humidity (R2 = 0.817, RMSE = 5.024%) were suitable mainly for exploratory microclimate mapping and relative trend monitoring unless local calibration is applied. The title, claims and conclusions were therefore narrowed to greenhouse sensing-layer validation and future crop-monitoring deployment. Full article
(This article belongs to the Special Issue Advancing Plant Phenotyping for Precision Crop Growth Monitoring and Forecast Leveraging In Situ, Remote and Proximal Sensing, and AI)
►▼ Show Figures

Figure 1

21 pages, 27653 KB  
Article
Field Phenotyping of Triticale Overwintering Dynamics Under Varied Sowing Practices Using Spectral Indices
by Wenjun Gao, Xiaofeng Cao, Mengyu Sun, Ruyu Li, Tile Huang and Weiyue Ma
Agronomy 2026, 16(9), 880; https://doi.org/10.3390/agronomy16090880 - 27 Apr 2026
Viewed by 747
Abstract
This study aims to enhance the early warning and monitoring of frost damage in triticale (×Triticosecale Wittmack), as well as to identify frost-tolerant materials. To this end, this work focused on phenotyping the dynamics of triticale under different damage intensities using [...] Read more.
This study aims to enhance the early warning and monitoring of frost damage in triticale (×Triticosecale Wittmack), as well as to identify frost-tolerant materials. To this end, this work focused on phenotyping the dynamics of triticale under different damage intensities using spectral indices. Sixteen triticale genotypes were planted under three sowing date (SD) treatments, with three sowing rate (SR) gradients set for each SD. The multispectral data of triticale under six frost damage intensities were acquired using an unmanned aerial vehicle (UAV) platform. A total of eight spectral indices (SIs) were extracted from samples under each intensity. In general, for each combination of SD and SR, all SIs decreased monotonically with increasing damage intensity. These indices are therefore suitable for monitoring frost damage in triticale under complex sowing scenarios. Under early frost damage, the relative decline rates (RDRs) of the SRI (Simple Ratio Vegetation Index), EVI2 (Enhanced Vegetation Index 2), NIRv (Near-Infrared Reflectance of Vegetation), and GLI (Green Leaf Index) were higher than those of other indices, indicating that they are more sensitive to early frost damage and thus more suitable for frost warning. Under frost stress, the RDRs of the indices were higher in early-sown samples than in late-sown samples. SD plays a more significant role than SR in determining the response of triticale indices to frost damage. Models were developed to detect triticale under varying damage intensities with SIs and classification algorithms—XGBoost, Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Support Vector Machine (SVM). The SVM classifier demonstrated the best generalization performance (overall accuracy: 98.03%; F1-score: 0.98). The detection contributions of indices within the optimal model were evaluated by their respective SHAP (Shapley Additive Explanations) values. The GLI, NIRv, NDVI (Normalized Difference Vegetation Index), and GNDVI (Green Normalized Difference Vegetation Index) were identified as key indices, as they exhibit higher cumulative SHAP values. Identification models for triticale with different frost tolerance levels were established based on the time-series data of these key indices and the above four algorithms. The optimal model based on the SVM algorithm achieved an identification accuracy exceeding 90%. The average overwintering dynamics and frost damage responses of the key indices were analyzed for triticale with different frost tolerance levels under all treatments. Under frost stress, these indices and their RDRs in frost-tolerant triticale were generally higher and lower, respectively, than those in frost-sensitive triticale. These four key indices can thus assist in the identification of frost tolerance in triticale. This study aids in the early warning and monitoring of frost damage in triticale under complex planting scenarios and the evaluation of overwintering performance in triticale germplasm. Full article
(This article belongs to the Special Issue Advancing Plant Phenotyping for Precision Crop Growth Monitoring and Forecast Leveraging In Situ, Remote and Proximal Sensing, and AI)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop