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
Plants
Special Issues
Machine Learning and Spectroscopy for Plant Phenotyping and Physiological Analysis
share announcement

Machine Learning and Spectroscopy for Plant Phenotyping and Physiological Analysis

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1731

Special Issue Editors

Dr. Renan Falcioni

E-Mail Website
Guest Editor
Department of Agronomy, State University of Maringá, Av. Colombo 5790, Maringá 87020-900, Brazil
Interests: biochemical and molecular analyses; chlorophyll a fluorescence; gas-exchange; plant phenotyping; photosynthesis; spectroscopy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marcos Rafael Nanni

E-Mail Website
Guest Editor
Department of Agronomy, State University of Maringá, Av. Colombo 5790, Maringá 87020-900, Brazil
Interests: data fusion and processing; machine learning; multispectral and hyperspectral sensors; remote sensing; precision agriculture; UAV
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in machine learning (ML) and spectroscopy have revolutionized plant phenotyping and physiological analysis. This Special Issue aims to explore the intersection of these technologies in advancing plant science, offering innovative solutions for plant research, crop management, and environmental monitoring. ML algorithms and spectroscopy techniques, such as hyperspectral and multispectral proximal and imaging sensing, have proven invaluable in enhancing the precision and efficiency of phenotyping, enabling a deeper understanding of plant growth, health, and responses to environmental factors.

In this Special Issue, we invite contributions that address the application of ML algorithms and spectroscopy in plant phenotyping, ranging from the analysis of plant morphology to the study of physiological traits such as photosynthesis, chlorophyll fluorescence, and gas exchange. We also welcome studies on the integration of these tools with remote sensing and UAV technologies, particularly in general plant analysis, precision agriculture, and large-scale crop monitoring. This Issue will serve as a platform to showcase cutting-edge research, offering insights into how these techniques can be applied to a variety of plants and crop improvements and to climate change adaptation through phenotyping and physiological analysis.

Dr. Renan Falcioni
Prof. Dr. Marcos Rafael Nanni
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

  • agricultural remote sensing
  • chlorophyll fluorescence
  • crop yield estimation
  • plant analysis
  • hyperspectral imaging
  • machine learning for phenotyping
  • modelling
  • multispectral sensing
  • pigment estimation
  • plant growth prediction
  • plant health monitoring
  • plant modelling
  • plant morphology analysis
  • plant phenotyping
  • precision agriculture
  • stress
  • remote sensing
  • spectroscopy in plant science
  • photosynthesis
  • UAV

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

23 pages, 8051 KB  
Article
Estimating Rice Cropping Area and Analyzing Land Use and Land Cover Changes in Jiangsu Province Using Multispectral Satellite Imagery
by Kashif Ali Solangi, Canhua Yang, Farheen Solangi, Weirong Zhang, Jinling Zhang and Chuan Jin
Plants 2026, 15(5), 715; https://doi.org/10.3390/plants15050715 - 27 Feb 2026
Viewed by 367
Abstract
Climate change and growing populations are major challenges for food security. Understanding single-season rice (SSR) growth patterns and how much area changes over time is essential for sustaining rice distribution patterns and ensuring food security. This study utilized ground trothing data with the [...] Read more.
Climate change and growing populations are major challenges for food security. Understanding single-season rice (SSR) growth patterns and how much area changes over time is essential for sustaining rice distribution patterns and ensuring food security. This study utilized ground trothing data with the remote sensing (RS) technique for estimation of the SSR pattern in Jiangsu Province. A total of 1700 rice and 470 non-rice points were collected during the field visit in April–September 2023 across Jiangsu Province. The current study employed advanced machine learning (ML) and the random forest (RF) model using Google Earth Engine (GEE). This study evaluates the SSR cropping area, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and land use–land cover (LULC) variation from 2018 to 2023 with different satellites. The results of NDVI show an increasing trend with mean values rising from 0.30 in 2018 to 0.42 in 2023. Additionally, higher mean values of LST were noticed in 2020 by 14.4 °C and in 2022 by 14.1 °C. Furthermore, the SSR area has significantly changed, mostly in the eastern and southern regions of Jiangsu Province, from 2018 to 2023. The higher rice cropping area decreased by 1.42% in 2019 compared to 2018, while the total reduction over the 2018–2023 period was 0.92%. Total cultivated crop areas continued to decline because most of the crop areas changed into built-up areas, resulting in a total variation of 2.75% from 2020 to 2023. The overall accuracy of RF model range was 77.33% to 93.55% with a Kappa coefficient of 0.55 and 0.87, indicating moderate to substantial classification agreement across the study period. The results of LULC indicate that the crop area decreased by 4.13% from 2018 to 2023, and major areas were converted into water bodies and built areas. In conclusion, the single-season cropping pattern decreased during the study period, accompanied by a reduction in total cropland area in Jiangsu Province. Therefore, these findings highlight the influence of urbanization and climate change on agricultural land and emphasize adaptive strategies in Jiangsu Province to ensure food security in the face of environmental challenges. Full article
(This article belongs to the Special Issue Machine Learning and Spectroscopy for Plant Phenotyping and Physiological Analysis)
►▼ Show Figures

Figure 1

25 pages, 5836 KB  
Article
MRSliceNet: Multi-Scale Recursive Slice and Context Fusion Network for Instance Segmentation of Leaves from Plant Point Clouds
by Shan Liu, Guangshuai Wang, Hongbin Fang, Min Huang, Tengping Jiang and Yongjun Wang
Plants 2025, 14(21), 3349; https://doi.org/10.3390/plants14213349 - 31 Oct 2025
Viewed by 888
Abstract
Plant phenotyping plays a vital role in connecting genotype to environmental adaptability, with important applications in crop breeding and precision agriculture. Traditional leaf measurement methods are laborious and destructive, while modern 3D sensing technologies like LiDAR provide high-resolution point clouds but face challenges [...] Read more.
Plant phenotyping plays a vital role in connecting genotype to environmental adaptability, with important applications in crop breeding and precision agriculture. Traditional leaf measurement methods are laborious and destructive, while modern 3D sensing technologies like LiDAR provide high-resolution point clouds but face challenges in automatic leaf segmentation due to occlusion, geometric similarity, and uneven point density. To address these challenges, we propose MRSliceNet, an end-to-end deep learning framework inspired by human visual cognition. The network integrates three key components: a Multi-scale Recursive Slicing Module (MRSM) for detailed local feature extraction, a Context Fusion Module (CFM) that combines local and global features through attention mechanisms, and an Instance-Aware Clustering Head (IACH) that generates discriminative embeddings for precise instance separation. Extensive experiments on two challenging datasets show that our method establishes new state-of-the-art performance, achieving AP of 55.04%/53.78%, AP50 of 65.37%/64.00%, and AP25 of 74.68%/73.45% on Dataset A and Dataset B, respectively. The proposed framework not only produces clear boundaries and reliable instance identification but also provides an effective solution for automated plant phenotyping, as evidenced by its successful implementation in real-world agricultural research pipelines. Full article
(This article belongs to the Special Issue Machine Learning and Spectroscopy for Plant Phenotyping and Physiological Analysis)
►▼ 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