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Agronomy
Special Issues
Crop Nutrition Diagnosis and Efficient Production
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Crop Nutrition Diagnosis and Efficient Production

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 15 April 2026 | Viewed by 4433

Special Issue Editors

Dr. Shijie Tian

E-Mail Website
Guest Editor
College of Information Engineering, Northwest A&F University, Yangling 712100, China
Interests: plant phenomics; agricultural artificial intelligence; agricultural big data; precision agriculture; crop efficient production
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jin Hu

E-Mail Website
Guest Editor
College of Information Engineering, Northwest A&F University, Yangling, Xianyang, China
Interests: agricultural artificial intelligence; agricultural big data; precision agriculture; efficient production of crops
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Crop nutrition monitoring and efficient production technologies are core components of modern agricultural technological progress, directly impacting agricultural productivity and sustainability. In the context of a continuously growing global population and increasingly scarce natural resources, precise and efficient agricultural production technologies are especially critical. Exploring the requirements for nutrients, such as water and nitrogen, under different production scenarios and environmental constraints, performing crop nutrition diagnostics, and advancing precision water and fertilizer management techniques based on diagnostic results are crucial for enhancing crop yields and optimizing resource use efficiency. In recent decades, the field of crop nutrition management has undergone a significant transformation from a reliance on expert experience to intelligent production modes based on the Internet of Things and information technology. This shift has not only increased the scientific and precise nature of agricultural production but has also promoted the sustainable development of the agricultural ecosystem.

This Special Issue aims to delve into the diagnostic technologies for key nutritional elements such as water and nitrogen and their application in achieving efficient crop production. We cordially invite researchers worldwide to submit original research and review articles on the following topics:

  • High-precision diagnostic technologies for determining the nutritional status of water and nitrogen in crops;
  • The development and practical application of integrated water and fertilizer management systems;
  • Innovative applications of intelligent agricultural technologies in crop nutrition management;
  • Studies on the real-time impacts of climate change on the water and nitrogen requirements of crops;
  • Innovative developments and practical cases of precision fertilization techniques;
  • Other key technologies applied in crop nutrition diagnostics and efficient production.

By collating and sharing these research outcomes, we hope that this Special Issue will provide practical scientific evidence and technical support for global agricultural producers, researchers, and policymakers, jointly pushing agricultural production in more efficient, environmentally friendly, and sustainable directions.

Dr. Shijie Tian
Prof. Dr. Jin Hu
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. Agronomy is an international peer-reviewed open access monthly 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

  • precision agriculture
  • agricultural artificial intelligence
  • drone remote sensing
  • hyperspectral imaging
  • precision water and fertilizer management

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

Published Papers (5 papers)

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Research

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34 pages, 18125 KB  
Article
Crushed, Squeezed, or Pressed? How Extraction Methods Influence Sap Analysis
by Javier Santa Cruz, Diego Calbucheo, Samuel Valdebenito, Camila Cáceres, Priscila Castillo, Marcelo Aguilar, Ignacia Hernández, Hernán Allendes, Kooichi Vidal and Patricia Peñaloza
Agronomy 2025, 15(11), 2572; https://doi.org/10.3390/agronomy15112572 - 7 Nov 2025
Viewed by 487
Abstract
Sap analysis provides a fast and promising approach to diagnosing plant nutritional status, yet methodological gaps remain a crucial obstacle to widespread adoption. Understanding how different extraction methods influence sap composition is key to improving the consistency and diagnostic reliability of this technique. [...] Read more.
Sap analysis provides a fast and promising approach to diagnosing plant nutritional status, yet methodological gaps remain a crucial obstacle to widespread adoption. Understanding how different extraction methods influence sap composition is key to improving the consistency and diagnostic reliability of this technique. Therefore, five methods were compared based on a range of chemical and physical parameters of broccoli petiole sap. Multiple statistical approaches were used to evaluate method effects on individual parameters and their inter-relationships. Extraction method significantly influenced chemical profiles—altering means, variability and distributional shapes—whereas physical attributes varied less across methods. Relationships among traits were observed; however, the consistency of patterns varied depending on the method. Overall, these results suggest that refining method selection could enhance both diagnostic reliability and the depth of interpretive analysis. This calls for rethinking current sap analysis practices, raising awareness of methodological variability and encouraging the development of robust, standardized approaches for reliable and comparable sap-based diagnostics. Full article
(This article belongs to the Special Issue Crop Nutrition Diagnosis and Efficient Production)
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16 pages, 13271 KB  
Article
Smartphone-Based Estimation of Cotton Leaf Nitrogen: A Learning Approach with Multi-Color Space Fusion
by Shun Chen, Shizhe Qin, Yu Wang, Lulu Ma and Xin Lv
Agronomy 2025, 15(10), 2330; https://doi.org/10.3390/agronomy15102330 - 2 Oct 2025
Viewed by 619
Abstract
To address the limitations of traditional cotton leaf nitrogen content estimation methods, which include low efficiency, high cost, poor portability, and challenges in vegetation index acquisition owing to environmental interference, this study focused on emerging non-destructive nutrient estimation technologies. This study proposed an [...] Read more.
To address the limitations of traditional cotton leaf nitrogen content estimation methods, which include low efficiency, high cost, poor portability, and challenges in vegetation index acquisition owing to environmental interference, this study focused on emerging non-destructive nutrient estimation technologies. This study proposed an innovative method that integrates multi-color space fusion with deep and machine learning to estimate cotton leaf nitrogen content using smartphone-captured digital images. A dataset comprising smartphone-acquired cotton leaf images was processed through threshold segmentation and preprocessing, then converted into RGB, HSV, and Lab color spaces. The models were developed using deep-learning architectures including AlexNet, VGGNet-11, and ResNet-50. The conclusions of this study are as follows: (1) The optimal single-color-space nitrogen estimation model achieved a validation set R2 of 0.776. (2) Feature-level fusion by concatenation of multidimensional feature vectors extracted from three color spaces using the optimal model, combined with an attention learning mechanism, improved the validation R2 to 0.827. (3) Decision-level fusion by concatenating nitrogen estimation values from optimal models of different color spaces into a multi-source decision dataset, followed by machine learning regression modeling, increased the final validation R2 to 0.830. The dual fusion method effectively enabled rapid and accurate nitrogen estimation in cotton crops using smartphone images, achieving an accuracy 5–7% higher than that of single-color-space models. The proposed method provides scientific support for efficient cotton production and promotes sustainable development in the cotton industry. Full article
(This article belongs to the Special Issue Crop Nutrition Diagnosis and Efficient Production)
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21 pages, 3946 KB  
Article
Research on Non Destructive Detection Method and Model Op-Timization of Nitrogen in Facility Lettuce Based on THz and NIR Hyperspectral
by Yixue Zhang, Jialiang Zheng, Jingbo Zhi, Jili Guo, Jin Hu, Wei Liu, Tiezhu Li and Xiaodong Zhang
Agronomy 2025, 15(10), 2261; https://doi.org/10.3390/agronomy15102261 - 24 Sep 2025
Viewed by 536
Abstract
Considering the growing demand for modern facility agriculture, it is essential to develop non-destructive technologies for assessing lettuce nutritional status. To overcome the limitations of traditional methods, which are destructive and time-consuming, this study proposes a multimodal non-destructive nitrogen detection method for lettuce [...] Read more.
Considering the growing demand for modern facility agriculture, it is essential to develop non-destructive technologies for assessing lettuce nutritional status. To overcome the limitations of traditional methods, which are destructive and time-consuming, this study proposes a multimodal non-destructive nitrogen detection method for lettuce based on multi-source imaging. The approach integrates terahertz time-domain spectroscopy (THz-TDS) and near-infrared hyperspectral imaging (NIR-HSI) to achieve rapid and non-invasive nitrogen detection. Spectral imaging data of lettuce samples under different nitrogen gradients (20–150%) were simultaneously acquired using a THz-TDS system (0.2–1.2 THz) and a NIR-HSI system (1000–1600 nm), with image segmentation applied to remove background interference. During data processing, Savitzky–Golay smoothing, MSC (for THz data), and SNV (for NIR data) were employed for combined preprocessing, and sample partitioning was performed using the SPXY algorithm. Subsequently, SCARS/iPLS/IRIV algorithms were applied for THz feature selection, while RF/SPA/ICO methods were used for NIR feature screening, followed by nitrogen content prediction modeling with LS-SVM and KELM. Furthermore, small-sample learning was utilized to fuse crop feature information from the two modalities, providing a more comprehensive and effective detection strategy. The results demonstrated that the THz-based model with SCARS-selected power spectrum features and an RBF-kernel LS-SVM achieved the best predictive performance (R2 = 0.96, RMSE = 0.20), while the NIR-based model with ICO features and an RBF-kernel LS-SVM achieved the highest accuracy (R2 = 0.967, RMSE = 0.193). The fusion model, combining SCARS and ICO features, exhibited the best overall performance, with training accuracy of 96.25% and prediction accuracy of 95.94%. This dual-spectral technique leverages the complementary responses of nitrogen in molecular vibrations (THz) and organic chemical bonds (NIR), significantly enhancing model performance. To the best of our knowledge, this is the first study to realize the synergistic application of THz and NIR spectroscopy in nitrogen detection of facility-grown lettuce, providing a high-precision, non-destructive solution for rapid crop nutrition diagnosis. Full article
(This article belongs to the Special Issue Crop Nutrition Diagnosis and Efficient Production)
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22 pages, 3825 KB  
Article
Impedance-Driven Decoupling Water–Nitrogen Stress in Wheat: A Parallel Machine Learning Framework Leveraging Leaf Electrophysiology
by Shuang Zhang, Xintong Du, Bo Zhang, Yanyou Wu, Xinyi Yang, Xinkang Hu and Chundu Wu
Agronomy 2025, 15(7), 1612; https://doi.org/10.3390/agronomy15071612 - 1 Jul 2025
Viewed by 825
Abstract
Accurately monitoring coupled water–nitrogen stress is critical for wheat (Triticum aestivum L.) productivity under climate change. This study developed a machine learning framework utilizing multimodal leaf electrophysiological signals––intrinsic resistance, impedance, capacitive reactance, inductive reactance, and capacitance––to decouple water and nitrogen stress signatures [...] Read more.
Accurately monitoring coupled water–nitrogen stress is critical for wheat (Triticum aestivum L.) productivity under climate change. This study developed a machine learning framework utilizing multimodal leaf electrophysiological signals––intrinsic resistance, impedance, capacitive reactance, inductive reactance, and capacitance––to decouple water and nitrogen stress signatures in wheat. A parallel modelling strategy was implemented employing Gradient Boosting, Random Forest, and Ridge Regression, selecting the optimal algorithm per feature based on predictive performance. Controlled pot experiments revealed IZ as the paramount biomarker across leaf positions, indicating its sensitivity to ion flux perturbations under abiotic stress. Crucially, algorithm-feature specificity was identified: Ridge Regression excelled in modeling linear responses due to its superior noise suppression, while GB effectively captured nonlinear dynamics. Flag leaves during reproductive stages provided significantly more stable predictions compared to vegetative third leaves, aligning with their physiological primacy as source organs. This framework offers a robust, non-invasive approach for real-time water and nitrogen stress diagnostics in precision agriculture. Full article
(This article belongs to the Special Issue Crop Nutrition Diagnosis and Efficient Production)
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Review

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42 pages, 1679 KB  
Review
Analysis of the Current Situation and Trends of Optical Sensing Technology Application for Facility Vegetable Life Information Detection
by Xiaodong Zhang, Zonghua Leng, Xinchen Wang, Shijie Tian, Yixue Zhang, Xiangyu Han and Zhaowei Li
Agronomy 2025, 15(9), 2229; https://doi.org/10.3390/agronomy15092229 - 21 Sep 2025
Cited by 1 | Viewed by 1196
Abstract
The production of facility vegetables is of great significance but there are still limitations to this production in terms of yield and quality. Optical sensing technology offers a rapid and non-destructive solution for phenotypic analysis, which is superior to traditional destructive methods. This [...] Read more.
The production of facility vegetables is of great significance but there are still limitations to this production in terms of yield and quality. Optical sensing technology offers a rapid and non-destructive solution for phenotypic analysis, which is superior to traditional destructive methods. This article reviews and analyzes nine optical sensing technologies, including RGB imaging, and introduces the application of various algorithms in combination with detection principles throughout the entire growth cycle as well as key phenotypic characteristics of facility vegetables. Each technology has its advantages. For example, RGB and multi/high-spectrum technologies are the most frequently used while thermal imaging is particularly suitable for early detection of non-biological and biological stress responses, and these technologies can effectively obtain physiological, biochemical, yield, and quality information about crops. However, current research mainly focuses on laboratory verification and there is still a significant gap when it comes to practical production. Future progress will depend on the integration of multiple sensing technologies, data analysis based on artificial intelligence, and improvements in model interpretability. These developments will be crucial for ultimately achieving precise breeding and intelligent greenhouse management systems, and will gradually transition from basic phenotypic analysis to comprehensive decision support systems. Full article
(This article belongs to the Special Issue Crop Nutrition Diagnosis and Efficient Production)
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