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Agriculture
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Spectral Data Analytics for Crop Growth Information
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Spectral Data Analytics for Crop Growth Information

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: closed (25 February 2026) | Viewed by 4779

Special Issue Editors

Dr. Xiaodong Zhang

E-Mail Website
Guest Editor
School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: plant phenomics; spectral imaging; plant physiology; plant pathology; smart agriculture; agricultural mechanization
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

Special Issue Information

Dear Colleagues,

Crop spectral intelligent sensing and analytics represent a transformative force driving the development of digital agriculture. This technology enables high-throughput, non-destructive, and real-time monitoring of key growth parameters such as nutritional status, water dynamics, and stress responses by acquiring and interpreting spectral information from crops. The deep integration of artificial intelligence and machine learning methods has significantly enhanced the accuracy and efficiency of multi-trait crop phenotyping. This has not only substantially improved the capabilities of growth modeling, stress, and yield prediction, but also provides critical support for genotype–phenotype association analysis.

This Special Issue is dedicated to showcasing innovative research and integrated applications of spectral sensing technologies and AI algorithms in the field of intelligent crop growth monitoring. We warmly invite submissions from scholars across multiple disciplines, including agronomy, remote sensing, computer science, agricultural engineering, optical engineering, and biotechnology. Topics of interest include, but are not limited to, the following:

  1. Advanced Spectral Sensing Technologies: Development of novel indoor/field spectral sensors; and multi-platform (ground-based, airborne, satellite) and multi-scale (tissue, plant, canopy) spectral data acquisition, calibration, and fusion techniques.
  2. Intelligent Analytics Algorithms and Models: Innovation in spectral data modeling, feature mining, and parameter inversion algorithms based on AI methods such as deep learning and transfer learning; and integrated estimation of multiple crop indicators (nutrients, water, biomass, yield, etc.).
  3. Phenomics and Breeding Applications: Application of high-throughput spectral phenotyping in genotype–phenotype association studies; and spectral identification of crop resistance to biotic/abiotic stresses.
  4. Integrated Digital Agriculture Applications: Spectral sensing-based precision farming management (fertilization/irrigation); integrated growth modeling and yield prediction systems; and intelligent crop disease, pest diagnosis, and early warning technologies.

We welcome all types of submissions, including original research articles, reviews, and perspectives.

Dr. Xiaodong Zhang
Dr. Shijie Tian
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. Agriculture 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 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

  • crop monitoring
  • spectral analysis
  • artificial intelligence
  • high-throughput phenotyping
  • machine learning
  • spectral sensors
  • smart agriculture
  • germplasm evaluation

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Published Papers (2 papers)

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20 pages, 2348 KB  
Article
IFSA-Inception-CBAM: An Early Detection Model for Rice Blast Disease Based on Integrated Feature Selection and a Deep Convolutional Neural Network
by Dongxue Zhao, Zetong Fu, Qi Liu, Zhongyu Wang, Zijuan Wang, Mengying Liu and Shuai Feng
Agriculture 2026, 16(4), 468; https://doi.org/10.3390/agriculture16040468 - 18 Feb 2026
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Abstract
Rice blast disease is one of the most contagious and destructive diseases affecting rice, posing a serious threat to global rice production and the agricultural economy. To enable accurate early detection under field conditions, this study proposes an integrated feature sorting algorithm (IFSA). [...] Read more.
Rice blast disease is one of the most contagious and destructive diseases affecting rice, posing a serious threat to global rice production and the agricultural economy. To enable accurate early detection under field conditions, this study proposes an integrated feature sorting algorithm (IFSA). The algorithm integrates five spectral feature selection methods—partial least squares, successive projections algorithm (SPA), principal component analysis loading (PCA-Loading), genetic algorithm (GA), and random forest (RF)—and employs the Borda count method for comprehensive feature ranking and selection. Field experiments were conducted in Haicheng, Anshan, Liaoning Province, China, using the rice cultivar Yanfeng 47. A total of 4893 hyperspectral samples were collected under natural field conditions. The results demonstrate that IFSA effectively identifies key spectral wavelengths for the early diagnosis of rice blast disease, achieving significantly higher detection accuracy than conventional single-method dimensionality reduction approaches. Based on the IFSA-selected wavelengths, an early detection model (Inception-CBAM) was further developed by integrating a multi-channel convolutional neural network with a convolutional block attention module, thereby enhancing the extraction and recognition of early disease-related features. Compared with six baseline models (InceptionV4, ResNet, BiGRU, RF, support vector machine, and extreme learning machine), Inception-CBAM achieved an overall accuracy of 95.44 ± 0.50% and a Kappa coefficient of 93.92 ± 0.67% for early rice blast disease detection, outperforming all competing methods. This study confirms the effectiveness of IFSA for hyperspectral feature selection and demonstrates that the proposed Inception-CBAM model provides strong capability for early disease detection. Nevertheless, the data were collected from a single cultivar and a single region; therefore, the model’s generalization performance across broader environments requires further improvement. Future work will extend the evaluation to multi-cultivar and multi-region scenarios to facilitate practical deployment for real-time field diagnosis. Full article
(This article belongs to the Special Issue Spectral Data Analytics for Crop Growth Information)
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22 pages, 4631 KB  
Article
Crop Disease Spore Detection Method Based on Au@Ag NRS
by Yixue Zhang, Jili Guo, Fei Bian, Zhaowei Li, Chuandong Guo, Jialiang Zheng and Xiaodong Zhang
Agriculture 2025, 15(19), 2076; https://doi.org/10.3390/agriculture15192076 - 3 Oct 2025
Cited by 2 | Viewed by 3770
Abstract
Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via [...] Read more.
Crop diseases cause significant losses in agricultural production; early capture and identification of disease spores enable disease monitoring and prevention. This study experimentally optimized the preparation of Au@Ag NRS (Gold core@Silver shell Nanorods) sol as a Surface-Enhanced Raman Scattering (SERS) enhancement reagent via a modified seed-mediated growth method. Using an existing microfluidic chip developed by the research group, disease spores were separated and enriched, followed by combining Au@Ag NRS with Crop Disease Spores through electrostatic adsorption. Raman spectroscopy was employed to collect SERS fingerprint spectra of Crop Disease Spores. The spectra underwent baseline correction using Adaptive Least Squares (ALS) and standardization via Standard Normal Variate (SNV). Dimensionality reduction preprocessing was performed using Principal Component Analysis (PCA) and Successive Projections Algorithm combined with Competitive Adaptive Reweighted Sampling (SCARS). Classification was then executed using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The SCARS-MLP model achieved the highest accuracy at 97.92% on the test set, while SCARS-SVM, PCA-SVM, and SCARS-MLP models attained test set accuracy of 95.83%, 95.24%, and 96.55%, respectively. Thus, the proposed Au@Ag NRS-based SERS technology can be applied to detect airborne disease spores, establishing an early and precise method for Crop Disease detection. Full article
(This article belongs to the Special Issue Spectral Data Analytics for Crop Growth Information)
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