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Agronomy
Special Issues
Smart Agriculture for Crop Phenotyping
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Smart Agriculture for Crop Phenotyping

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

Deadline for manuscript submissions: 20 October 2026 | Viewed by 3676

Special Issue Editors

Prof. Dr. Wenting Han

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
Interests: remote sensing; precision agriculture; water use efficiency; smart irrigation; evapotranspiration
Special Issues, Collections and Topics in MDPI journals
Dr. Liyuan Zhang

E-Mail Website
Guest Editor
Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Jiangsu University, Zhenjiang 212013, China
Interests: crop stress monitoring; UAV; phenotyping; image processing; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Current projections indicate that the world population will increase from 6.9 billion to 9.1 billion by 2050. In the context of climate change and finite agricultural resources, maintaining and enhancing crop yields has become an increasingly formidable challenge. Against this backdrop, crop phenotyping has emerged as a vital field in modern agricultural research, offering innovative solutions to address these pressing issues.

This Special Issue is dedicated to exploring the latest advancements in smart agriculture technologies for crop phenotyping. We aim to integrate interdisciplinary innovations across artificial intelligence (AI), machine learning, hyperspectral imaging, drone-based remote sensing, and robotic automation. By enabling high-throughput, non-invasive phenotyping data collection and intelligent analytics, these cutting-edge technologies empower researchers to decode dynamic crop growth patterns, stress-resilience traits, and genotype-phenotype associations with unparalleled precision. This, in turn, accelerates crop breeding cycles and optimizes field management practices, offering a promising pathway to enhance agricultural productivity and sustainability in an era of global challenges.

We invite original scientific contributions that highlight the latest advancements in smart agriculture technologies for crop phenotyping. Topics of interest include, but are not limited to, the following:

  • Development of multi-scale phenotyping systems using drones and ground robots to capture comprehensive and high-resolution data.
  • Applications of machine and deep learning in extracting morphological, physiological, and biochemical traits under biotic and abiotic stresses, providing deeper insights into crop performance and resilience.
  • Integration of high-throughput phenotyping platforms with AI-powered decision-support tools for precision agriculture, streamlining workflows from data collection to actionable insights.
  • Techniques for plant mapping, feature extraction, and stress estimation using UAVs and other remote sensing data, enabling the real-time monitoring and assessment of crop health.
  • Case studies showcasing the application of smart phenotyping for resource optimization (water, fertilizers), pest and disease monitoring, and climate adaptation strategies, demonstrating tangible benefits for sustainable agriculture.

Prof. Dr. Wenting Han
Dr. Liyuan Zhang
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 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

  • artificial intelligence
  • machine learning
  • deep learning
  • multi-scale phenotyping systems
  • prescription maps
  • site-specific crop management

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Published Papers (3 papers)

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Research

23 pages, 11762 KB  
Article
Effects of Spatial Resolution on Reflectance Responses to Soil Salinity in Plastic-Mulched Farmland
by Weitong Ma, Wenting Han, Xin Cui, Liyuan Zhang, Yaxiao Niu and Xinyang Fu
Agronomy 2026, 16(9), 863; https://doi.org/10.3390/agronomy16090863 - 24 Apr 2026
Viewed by 279
Abstract
Spectral remote sensing enables efficient acquisition of large-scale land surface information and is a key approach for monitoring soil salinity content (SSC). However, surface mulching significantly alters the spectral reflectance responses of croplands, increasing the uncertainty of SSC retrieval using remote sensing. This [...] Read more.
Spectral remote sensing enables efficient acquisition of large-scale land surface information and is a key approach for monitoring soil salinity content (SSC). However, surface mulching significantly alters the spectral reflectance responses of croplands, increasing the uncertainty of SSC retrieval using remote sensing. This study aimed to systematically identify SSC-sensitive spectral features under different mulching conditions and to evaluate the effects of spatial resolution on SSC–spectral relationships. Multi-resolution datasets were constructed based on plastic mulch geometric parameters, and SSC–spectral relationships were analyzed using correlation methods and recursive feature elimination (RFE). Results indicate that under near-ground ultra-high-resolution conditions, the correlation between inter-mulch bare soil spectral features and SSC was weakly influenced by mulch type, and distinguishing mulch types provides limited improvement in inter-variable relationships. Pearson’s r exceeded 0.40 for both white- and black-mulched samples, and distinguishing mulch types provided only marginal gains in model accuracy (RFR–RFE R2 = 0.9524 for white-mulched and 0.9252 without distinguishing; R2 = 0.9387 for black-mulched). In contrast, under multi-resolution settings at the field scale, separating black-mulched, white-mulched, and non-mulched fields significantly enhanced the correlation between spectral indices (SIs) and SSC, with the coefficient of determination (R2) based on the recursive feature elimination (RFE) algorithm increasing by up to 0.28. The highly sensitive SIs of non-mulched farmland are generally consistent with those of white-mulched farmland but differ markedly from those of black-mulched farmland. Scale optimization analysis further indicated that the optimal spatial resolution was 1.35 m for white-mulched and non-mulched farmland. Black-mulched farmland performed best at 5.4 m, likely because stronger spectral masking by black mulch increases mixed-pixel dominance and benefits from spatial aggregation. These findings provide methodological guidance and practical approaches to accurately retrieve SSC in plastic-mulched croplands and to determine the optimal image spatial resolution. Full article
(This article belongs to the Special Issue Smart Agriculture for Crop Phenotyping)
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23 pages, 33716 KB  
Article
SREM-Net: A Novel Leaf Disease Classification Model for Field Crops Based on Stylistic and Multiscale Feature Extraction
by Liruizhi Jia, Xiaoli Zhang, Bo Kong, Jiale Hu, Yutian Wu and Shengquan Liu
Agronomy 2026, 16(1), 58; https://doi.org/10.3390/agronomy16010058 - 24 Dec 2025
Viewed by 552
Abstract
Rapid and accurate identification of crop leaf diseases is essential for informed agricultural decision-making. However, achieving reliable classification remains challenging under conditions such as extreme lighting, complex color variations, and intricate structural backgrounds, particularly when early-stage symptoms are subtle and easily masked by [...] Read more.
Rapid and accurate identification of crop leaf diseases is essential for informed agricultural decision-making. However, achieving reliable classification remains challenging under conditions such as extreme lighting, complex color variations, and intricate structural backgrounds, particularly when early-stage symptoms are subtle and easily masked by surrounding tissues. To address these challenges, this study proposes a novel network architecture, SREM-Net, which incorporates stylistic and multiscale feature extraction strategies. Specifically, the model introduces the style recalibration MBconv (SRMB) to mitigate feature dilution caused by the coexistence of lesions and complex backgrounds. In addition, the EMF dynamically adjusts the receptive field, enabling the model to capture lesion distributions across the entire leaf while simultaneously emphasizing morphological details, edges, and fine-scale features. To improve interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to generate visual explanations of the detected diseases. On our self-constructed, weather-augmented MCCD dataset, the experimental results demonstrate that SREM-Net outperforms state-of-the-art networks such as LWMobileViT, MobileNetV3-CA, and LWDN, achieving F1-score improvements of 2.13%, 1.21%, and 1.18%, respectively. Full article
(This article belongs to the Special Issue Smart Agriculture for Crop Phenotyping)
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28 pages, 3417 KB  
Article
Non-Destructive Estimation of Area and Greenness in Leaf and Seedling Scales: A Case Study in Cucumber
by Georgios Tsaniklidis, Theodora Makraki, Dimitrios Papadimitriou, Nikolaos Nikoloudakis, Amin Taheri-Garavand and Dimitrios Fanourakis
Agronomy 2025, 15(10), 2294; https://doi.org/10.3390/agronomy15102294 - 28 Sep 2025
Cited by 19 | Viewed by 1913
Abstract
Leaf area (LA) and SPAD value (a proxy for chlorophyll content) are two key determinants of seedling quality. This study aimed to develop and validate approaches for the efficient retrieval of these features in order to facilitate both management and screening practices. In [...] Read more.
Leaf area (LA) and SPAD value (a proxy for chlorophyll content) are two key determinants of seedling quality. This study aimed to develop and validate approaches for the efficient retrieval of these features in order to facilitate both management and screening practices. In cucumber, different models were developed and tested for the accurate estimation of LA at the scale of the individual organ (cotyledon, leaf) by using its linear dimensions (length (L) and width (W)), and of the whole seedling by using the 2D image-extracted projected area (from three different angles: 0°, 45°, and 90°). At either scale, the SPAD value was computed by using image (90°)-based colorimetric features. The estimation of individual organ area was more accurate when using L alone, compared with W alone. By using the two dimensions and specific colorimetric features, the individual organ area (R2 ≥ 0.92) and SPAD value (R2 of 0.77) were accurately predicted. When considering a single view, the top one (90°) was associated with the highest accuracy in whole-seedling LA estimation, and the side view (0°) with the lowest (R2 of 0.88 and 0.73, respectively). Using any combination of two angles, the whole-seedling LA was accurately retrieved (R2 ≥ 0.88). When using colorimetric features, a poor estimation of the whole-seedling SPAD value was noted (R2 ≤ 0.43). The deployment of artificial neural networks (ANNs) further allowed the estimation of specific organ shape traits, and improved the accuracy of all the aforementioned predictions, including the whole-seedling SPAD value (R2 of 0.597). In conclusion, the findings of this study highlight that features readily retrieved from 2D images hold promising potential for improving screening routines within the nursery industry. Full article
(This article belongs to the Special Issue Smart Agriculture for Crop Phenotyping)
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