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Plants
Special Issues
Crop Nutrition Diagnosis and Regulation
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Crop Nutrition Diagnosis and Regulation

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 516

Special Issue Editors

Prof. Dr. Zunfu Lv

E-Mail Website
Guest Editor
The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, Institute of Root and Tuber Crops, College of Advanced Agricultural Sciences, Zhejiang A&F University, Wusu Street # 666, Ling’an District, Hangzhou 311300, China
Interests: nutrition diagnosis; remote sensing; crop model
Prof. Dr. Guoquan Lu

E-Mail Website
Guest Editor
The Key Laboratory for Quality Improvement of Agricultural Products of Zhejiang Province, Institute of Root and Tuber Crops, College of Advanced Agricultural Sciences, Zhejiang A&F University, Wusu Street # 666, Ling’an District, Hangzhou 311300, China
Interests: crop quality; crop nutrition

Special Issue Information

Dear Colleagues,

The focus of this Special Issue is to utilize technological means, such as crop models, remote sensing, and image analysis, to analyze crop growth indicators, nutritional indicators, yield, and quality, thereby achieving precise diagnosis of crop nutrition, accurate prediction of growth information, and precise regulation of yield and quality. By employing crop models and other technical tools, crop nutrition diagnosis models will be established for the purpose of diagnosing nutrient deficiencies and crop growth models will be constructed for the purpose of predicting yield and quality. Remote sensing technologies will be used to monitor crop nutrition and growth indicators, as well as to predict crop growth and yield quality. By integrating models and remote sensing technologies, intelligent algorithms will be developed to monitor and predict crop growth indicators or yield quality. Additionally, artificial intelligence combined with image analysis will be utilized to monitor crop nutrition and growth indicators, and models or algorithms will be constructed to reflect crop growth information.

Prof. Dr. Zunfu Lv
Prof. Dr. Guoquan Lu
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. 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

  • yield prediction
  • nutrition diagnosis
  • model construction
  • crop nutrition
  • crop model
  • remote sensing
  • growth regulation
  • crop yield
  • crop quality

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Published Papers (1 paper)

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Research

14 pages, 2366 KiB  
Article
Rice Growth Estimation and Yield Prediction by Combining the DSSAT Model and Remote Sensing Data Using the Monte Carlo Markov Chain Technique
by Yingbo Chen, Siyu Wang, Zhankui Xue, Jijie Hu, Shaojie Chen and Zunfu Lv
Plants 2025, 14(8), 1206; https://doi.org/10.3390/plants14081206 - 14 Apr 2025
Viewed by 454
Abstract
The integration of crop models and remote sensing data has become a useful method for monitoring crop growth status and crop yield based on data assimilation. The objective of this study was to use leaf area index (LAI) values and plant nitrogen accumulation [...] Read more.
The integration of crop models and remote sensing data has become a useful method for monitoring crop growth status and crop yield based on data assimilation. The objective of this study was to use leaf area index (LAI) values and plant nitrogen accumulation (PNA) values generated from spectral indices to calibrate the Decision Support System for Agrotechnology Transfer (DSSAT) model using the Monte Carlo Markov Chain (MCMC) technique. The initial management parameters, including sowing date, sowing rate, and nitrogen rate, are recalibrated based on the relationship between the remote sensing state variables and the simulated state variables. This integrated technique was tested on independent datasets acquired from three rice field tests at the experimental site in Deqing, China. The results showed that the data assimilation method achieved the most accurate LAI (R2 = 0.939 and RMSE = 0.74) and PNA (R2 = 0.926 and RMSE = 7.3 kg/ha) estimations compared with the spectral index method. Average differences (RE, %) between the inverted initialized parameters and the original input parameters for sowing date, seeding rate, and nitrogen amount were 1.33%, 4.75%, and 8.16%, respectively. The estimated yield was in good agreement with the measured yield (R2 = 0.79 and RMSE = 661 kg/ha). The average root mean square deviation (RMSD) for the simulated values of yield was 745 kg/ha. Yield uncertainty from data assimilation between crop models and remote sensing was quantified. This study found that data assimilation of crop models and remote sensing data using the MCMC technique could improve the estimation of rice leaf area index (LAI), plant nitrogen accumulation (PNA), and yield. Data assimilation using the MCMC technique improves the prediction of LAI, PNA, and yield by solving the saturation effect of the normalized difference vegetation index (NDVI). This method proposed in this study can provide precise decision-making support for field management and anticipate regional yield fluctuations in advance. Full article
(This article belongs to the Special Issue Crop Nutrition Diagnosis and Regulation)
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