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Plants
Special Issues
Remote and Proximal Sensing for Diagnosis of Plant Health
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Remote and Proximal Sensing for Diagnosis of Plant Health

A special issue of Plants (ISSN 2223-7747).

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1562

Special Issue Editors

Dr. Jie Wang

E-Mail Website
Guest Editor
College of Resources and Environment, Southwest University, Chongqing 400716, China
Interests: remote sensing for plant nutrient diagnosis; precision nutrient management
Dr. Ruth Kerry

E-Mail Website
Guest Editor
Department of Geography, Brigham Young University, Provo, UT 84602, USA
Interests: spatial analysis; geostatistical analysis; soil science; precision agriculture; weeds; sensed data; environmental geography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate plant nutrient diagnosis is essential for optimizing crop productivity, ensuring food security, and promoting sustainable agricultural practices, and traditional methods for assessing plant nutrients are often labor-intensive and time-consuming. Remote sensing technologies, including LiDAR and hyperspectral, multispectral, and thermal imaging, provide non-destructive, rapid, and scalable solutions for monitoring plant nutrient status at different spatial and temporal scales. This Special Issue focuses on recent advancements in remote sensing techniques for plant nutrient diagnosis, including innovative sensor applications, data fusion strategies, and machine learning approaches. Topics of interest include, but are not limited to, spectral indices for nutrient estimation, UAV-based nutrient mapping, canopy radiative transfer modeling, and the integration of satellite and proximal sensing data for plant nutrient diagnosis. Special emphasis is placed on addressing challenges such as environmental variability, the confounding effects of canopy vertical structure and chlorophyll content, and the development of robust calibration models for nutrient assessment. By bringing together cutting-edge research and technological innovations, this Special Issue aims to provide valuable insights into improving nutrient diagnosis for precision nutrient management and sustainable agriculture. We welcome original research articles, reviews, and case studies that contribute to advancing the role of remote sensing in plant nutrient diagnostics.

Dr. Jie Wang
Dr. Ruth Kerry
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-blind 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

  • remote sensing
  • plant nutrient diagnosis
  • hyper-/multi-spectral imaging
  • UAV-based monitoring
  • precision nutrient management

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Published Papers (2 papers)

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Research

17 pages, 4632 KB  
Article
Estimation of Nitrogen Status in Zanthoxylum armatum var. novemfolius Using Machine Learning Algorithms and UAV Hyperspectral and LiDAR Data Fusion
by Shangyuan Zhao, Yong Wei, Jinkun Zhao, Shuai Wang, Xin Ye, Xiaojun Shi and Jie Wang
Plants 2026, 15(7), 1119; https://doi.org/10.3390/plants15071119 - 6 Apr 2026
Viewed by 488
Abstract
Accurate monitoring of nitrogen (N) status is critical for precision N management and optimizing the yield and quality of Zanthoxylum armatum var. novemfolius (ZA). However, individual sensors often struggle to simultaneously capture the biochemical variations and complex canopy structural changes of ZA. Therefore, [...] Read more.
Accurate monitoring of nitrogen (N) status is critical for precision N management and optimizing the yield and quality of Zanthoxylum armatum var. novemfolius (ZA). However, individual sensors often struggle to simultaneously capture the biochemical variations and complex canopy structural changes of ZA. Therefore, field experiments were conducted over two consecutive years, applying four N-application rates (0, 150, 300, and 450 kg N ha−1) to ZA. At each phenological stage, hyperspectral imagery and LiDAR point clouds were collected via three UAV flight altitudes (60 m, 80 m, and 100 m), and canopy nitrogen concentration (CNC) and aboveground nitrogen accumulation (AGNA) were measured. This study developed a framework by synergistically fusing UAV-derived hyperspectral imaging (HSI) and LiDAR data for CNC and AGNA monitoring. Results showed that the response of nitrogen status indicators to fertilization was phenology-specific: CNC showed no significant difference (p > 0.05) among treatments during the vigorous vegetative growth stage (VGS) but differed significantly (p < 0.05) during the fruit expansion stage (FES); AGNA differed significantly among treatments at VGS and FES (p < 0.05). The two-step screening yielded NDSI (732, 879) and NDSI (560, 690) as the optimal CNC indicators at VGS and FES, respectively (r = 0.83 and 0.93), whereas the NDSI (711, 986) and NDSI (515, 736) were identified as the optimal AGNA indicators at VGS and FES, respectively (r = 0.91 and 0.71). Across all phenological stages, Random Forest Regression consistently delivered the highest accuracy for CNC (R2 = 0.93–0.98, RMSE = 0.87–1.02 g kg−1) and AGNA (R2 = 0.95–0.97, RMSE = 1.92–2.55 g plant−1), outperforming MLR, PLSR, and SVR. This synergistic framework provides a high-precision, non-destructive methodology for the precision N monitoring of woody crops. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Diagnosis of Plant Health)
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25 pages, 2944 KB  
Article
Mulberry Drought Diagnosis: Integrating Proximal Sensing and Metabolomics for Remote Monitoring
by Liang Yang, Cheng Li, Huaqi Gao, Zhiqi Hong, Yong He and Lingxia Huang
Plants 2026, 15(5), 741; https://doi.org/10.3390/plants15050741 - 28 Feb 2026
Cited by 1 | Viewed by 550
Abstract
Drought is the most severe natural hazard threatening agricultural production. Mulberry (Morus alba L.) is an important crop for the sericulture industry, and its drought tolerance has been extensively studied. In this study, the phenotypic and physiological responses of two different mulberry [...] Read more.
Drought is the most severe natural hazard threatening agricultural production. Mulberry (Morus alba L.) is an important crop for the sericulture industry, and its drought tolerance has been extensively studied. In this study, the phenotypic and physiological responses of two different mulberry tree genotypes (711 and NS8) to drought stress were investigated, with the aim of screening potential nondestructive traits and understand interrelationships. The significant reductions of digital biomass (DB), leaf area (LA), and projected leaf area (PLA) in morphological traits indicated that drought led to a decrease in mulberry yield. The change of color traits RFarRed and RNIR were associated with pigments and leaf morphology. Vegetation indexes were also significantly affected by drought stress. Due to their had high correlation coefficients and good linear relationships with yield, DB and LA can be used as yield proxy traits for this measure. Drought-sensitive traits were identified using PCA and correlation analysis, and the results showed that greenness (GR) was a proxy predictor of drought stress. For antioxidant defenses, CAT activity and phenolic compound content were significantly decreased. Metabolomics analysis revealed that genotype 711 exhibited 1691 differential metabolites under drought stress; these mainly comprised amino acids, lipids, and phenolic acids, which were mainly enriched in secondary metabolism and flavonoid biosynthesis. Drought also reprogrammed carbohydrate, secondary compounds, and amino acid metabolism. The results revealed that the phenotypic response of two mulberry trees to drought, as well as the integration of phenotypic traits with metabolic traits, could help us to understand drought tolerance mechanisms and benefit efficient selection and breeding of fitter genotypes. Full article
(This article belongs to the Special Issue Remote and Proximal Sensing for Diagnosis of Plant Health)
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