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New Progress on Remote Sensing Technology and Its Application in Agriculture

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 1952

Special Issue Editors

Dr. Jiang Chen

E-Mail Website
Guest Editor
Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: remote sensing; data fusion; radiative transfer modelling; machine learning; yield prediction; agricultural remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Wen Zhuo

E-Mail Website
Guest Editor
School of Earth, Environment & Society, McMaster University, Hamilton, ON L8S 4K1, Canada
Interests: crop yield estimation; crop model data assimilation; drought monitoring; causal inference; causal effect analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Rui Sun

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: remote sensing of vegetation productivity and evapotranspiration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global agriculture faces increasing demands for productivity and sustainability, and remote sensing technology offers critical tools for addressing these challenges. Traditional agricultural monitoring methods rely on limited data sources, often lacking the spatial, temporal, and spectral richness needed for accurate assessments. This single-source approach constrains precision, making it difficult to capture dynamic crop conditions and environmental variability effectively. The integration of multisource data fusion, radiative transfer models, and artificial intelligence (AI) will advance sustainable agriculture. Multisource data fusion combines satellite, UAV, and ground-based data, allowing for the more comprehensive and accurate monitoring of crop conditions, soil moisture, and environmental variables. Radiative transfer models enhance our understanding of the interaction between electromagnetic radiation and vegetation, enabling the precise retrieval of crop characteristics. AI, including machine learning and deep learning, plays a critical role in analyzing complex datasets, facilitating crop stress detection, yield prediction, and disease monitoring. Together, these technologies provide actionable insights for precision agriculture, resource optimization, and climate resilience, supporting sustainable farming practices globally. This Special Issue requests research on innovative approaches and applications, fostering collaboration across disciplines to address key agricultural challenges.

Dr. Jiang Chen
Dr. Wen Zhuo
Prof. Dr. Rui Sun
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences 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 2400 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

  • multisource data fusion
  • radiative transfer models
  • artificial intelligence
  • data assimilation
  • sustainable agriculture
  • precision agriculture
  • digital agriculture
  • crop monitoring
  • climate resilience

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Published Papers (2 papers)

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Research

18 pages, 2503 KiB  
Article
Estimation of Amino Acid and Tea Polyphenol Content of Tea Fresh Leaves Based on Fractional-Order Differential Spectroscopy
by Shirui Li, Rui Sun, Xin Li, Yang Li, Liang Zhao, Xinyu Huang and Yufei Xu
Appl. Sci. 2025, 15(11), 5792; https://doi.org/10.3390/app15115792 - 22 May 2025
Viewed by 613
Abstract
Amino acids (AAs) and tea polyphenols (TPs) are essential quality indicators in tea, impacting sensory attributes and economic value. Hyperspectral technology enables efficient, real-time detection of these compounds on field-grown tea leaves. “The original spectra were preprocessed using fractional-order derivatives (0.1–1.0 orders) to [...] Read more.
Amino acids (AAs) and tea polyphenols (TPs) are essential quality indicators in tea, impacting sensory attributes and economic value. Hyperspectral technology enables efficient, real-time detection of these compounds on field-grown tea leaves. “The original spectra were preprocessed using fractional-order derivatives (0.1–1.0 orders) to enhance subtle spectral features. Compared to fixed integer-order derivatives (e.g., first or second order), fractional-order derivatives allow continuous tuning between 0 and 1, thereby amplifying minor absorption peaks while effectively suppressing noise amplification”. The Competitive Adaptive Reweighted Sampling (CARS) method selects optimal spectral bands, and Partial Least Squares Regression (PLSR) models were built with raw spectral reflectance as independent variables and AA and TP content as dependent variables. Results show that FOD had better prediction accuracy compared to classical integer-order derivatives, e.g., the optimal FOD order of 0.7 for AA prediction increased the R2 from 0.73 to 0.80 and reduced the RMSE from 0.30% to 0.25%, while for TP prediction, a FOD order of 0.1 raised the R2 from 0.40 to 0.42 and lowered the RMSE from 4.03% to 3.96%. In addition, CARS shows a better performance over the correlation coefficient (CC) method in model accuracy, contributing to more accurate selection of sensitive bands for the content prediction of tea ingredients. Our FOD–CARS–PLSR models achieved an R2 of 0.80 and RMSE of 0.25% for AAs, and an R2 of 0.42 and RMSE of 3.96% for TPs in fresh tea leaves. Beyond tea quality monitoring, this flexible preprocessing and modeling framework can be readily adapted to estimate biochemical or biophysical properties in other crops, soils, or vegetated ecosystems, offering a generalizable tool for precision agriculture and environmental sensing. Full article
(This article belongs to the Special Issue New Progress on Remote Sensing Technology and Its Application in Agriculture)
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18 pages, 2306 KiB  
Article
A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
by Xinwei Zhao, Shengbo Chen, Yucheng Xu and Zibo Wang
Appl. Sci. 2025, 15(2), 764; https://doi.org/10.3390/app15020764 - 14 Jan 2025
Viewed by 1017
Abstract
Accurate diagnosis of plant phosphorus nutritional status is critical for optimizing agricultural practices and enhancing resource efficiency. Existing methods are limited to qualitatively assessing plant phosphorus nutritional status and cannot quantitatively estimate the plant’s phosphorus requirements. Moreover, these methods are time-consuming, making them [...] Read more.
Accurate diagnosis of plant phosphorus nutritional status is critical for optimizing agricultural practices and enhancing resource efficiency. Existing methods are limited to qualitatively assessing plant phosphorus nutritional status and cannot quantitatively estimate the plant’s phosphorus requirements. Moreover, these methods are time-consuming, making them impractical for large-scale application. In this study, we developed an advanced phosphorus absorption model (Pabs) that integrates the phosphorus nutrition index (PNI) and phosphorus use efficiency (PUE). The PUE, a critical metric for assessing phosphate fertilizer use efficiency, was quantified by comparing yields under fertilized and unfertilized conditions. Utilizing the Agricultural Production Systems Simulator (APSIM) model, we simulated maize (Zea mays L.) phosphorus concentration (P) and aboveground biomass (Bio) under varying phosphorus application rates. The model exhibited robust performance, achieving an R2 above 0.95 and an RMSE below 0.22. Based on the APSIM model simulations, a phosphorus dilution curve (Pc = 3.17 Bio−0.29, R2 = 0.98) was established, reflecting the dilution trends of phosphorus across growth stages. Furthermore, the use of vegetation indices (VIS) to evaluate phosphorus nutritional status also showed promising results, with inversion accuracies exceeding 0.70. To validate the model, field sampling was conducted in maize-growing regions of Changchun. Results demonstrated a correct diagnosis rate of 75%, underscoring the model’s capacity to accurately estimate phosphorus requirements on a regional scale. These findings highlight the Pabs model as a reliable tool for precision phosphorus management, offering significant potential to optimize fertilization strategies and support sustainable agricultural systems. Full article
(This article belongs to the Special Issue New Progress on Remote Sensing Technology and Its Application in Agriculture)
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