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Agriculture
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Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition
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Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition

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

Deadline for manuscript submissions: 30 August 2025 | Viewed by 5796

Special Issue Editors

Dr. Aichen Wang

E-Mail Website
Guest Editor
School of Agricultural Engineering, Jiangsu University, Zhenjiang 210013, China
Interests: hyperspectral image analysis; crop protection; phenotyping; applied artificial intelligence; image processing; remote sensing; advanced machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Ce Yang

E-Mail Website
Guest Editor
Bioproducts and Biosystems Engineering, University of Minnesota, Saint Paul, MN 55108, USA
Interests: application of advanced ideas of robotics; remote sensing; data mining and information technology in precision agriculture; multispectral/hyperspectral imaging; spectroscopy; machine learning; geographic information system (GIS); digital mapping; biochemical sensing; phenotyping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the global population proliferates, greater pressure is placed on modern agriculture to produce more food. However, crops face various threats from abiotic and biotic stresses, including drought, salt, freezing, diseases, insects, weeds, etc. Accurately monitoring the growing status of crops in a timely manner under various stresses is crucial to crop cultivation, protection, phenotyping, and seed breeding. Optical sensing technology has been explored extensively for crop monitoring, with multi-/hyper-spectral imaging technologies that can provide both spectral and imaging information playing a vital role.

This Special Issue focuses on the development and application of multi- and hyper-spectral imaging technologies and advanced analyzing algorithms in crop monitoring in the field or in greenhouses. This Special Issue will fully embrace inter- and trans-disciplinary studies from multiple domains (e.g., agricultural sciences, agricultural engineering, and optical engineering) in the co-construction of knowledge for sustainable agriculture. All types of articles, such as original research and review papers, are welcome.

Dr. Aichen Wang
Dr. Ce Yang
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. 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

  • multi-/hyper-spectral imaging
  • crop monitoring
  • phenotyping
  • optical sensing
  • stress monitoring
  • machine learning
  • remote sensing
  • UAV

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Related Special Issue

Published Papers (7 papers)

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Research

24 pages, 6894 KiB  
Article
Early Yield Prediction of Oilseed Rape Using UAV-Based Hyperspectral Imaging Combined with Machine Learning Algorithms
by Hongyan Zhu, Chengzhi Lin, Zhihao Dong, Jun-Li Xu and Yong He
Agriculture 2025, 15(10), 1100; https://doi.org/10.3390/agriculture15101100 - 19 May 2025
Viewed by 200
Abstract
Oilseed rape yield critically reflects varietal superiority. Rapid field-scale estimation enables efficient high-throughput breeding. This study evaluates unmanned aerial vehicle (UAV) hyperspectral imagery’s potential for yield prediction at the pod stage by utilizing wavelength selection and vegetation indices. Meanwhile, optimized feature selection algorithms [...] Read more.
Oilseed rape yield critically reflects varietal superiority. Rapid field-scale estimation enables efficient high-throughput breeding. This study evaluates unmanned aerial vehicle (UAV) hyperspectral imagery’s potential for yield prediction at the pod stage by utilizing wavelength selection and vegetation indices. Meanwhile, optimized feature selection algorithms identified effective wavelengths (EWs) and vegetation indices (VIs) for yield estimation. The optimal yield estimation models based on EWs and VIs were established, respectively, by using multiple linear regression (MLR), partial least squares regression (PLSR), extreme learning machine (ELM), and a least squares support vector machine (LS-SVM). The main results were as follows: (i) The yield prediction of oilseed rape using EWs showed better prediction and robustness compared to the full-spectral model. In particular, the competitive adaptive reweighted sampling–extreme learning machine (CARS-ELM) model (Rpre = 0.8122, RMSEP = 170.4 kg/hm2) achieved the best prediction performance. (ii) The ELM model (Rpre = 0.7674 and RMSEP = 187.6 kg/hm2), using 14 combined VIs, showed excellent performance. These results indicate that the remote sensing image data obtained from the UAV hyperspectral remote sensing system can be used to enable the high-throughput acquisition of oilseed rape yield information in the field. This study provides technical guidance for the crop yield estimation and high-throughput detection of breeding information. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)
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21 pages, 32288 KiB  
Article
Identification of Interannual Variation Frequency of Cropland Cropping Intensity Based on Remote Sensing Spatiotemporal Fusion and Crop Phenological Rhythm: A Case Study of Zhenjiang, Jiangsu
by Yaohui Zhu, Qingzhen Zhu, Yuanyuan Gao, Liyuan Zhang, Aichen Wang, Yongyun Zhu, Chunshan Wang, Bo Liu, Fa Zhao, Peiying Li, Xinhua Wei and Qi Song
Agriculture 2025, 15(9), 1004; https://doi.org/10.3390/agriculture15091004 - 6 May 2025
Viewed by 209
Abstract
The scientific evaluation of cropland resource utilization efficiency is crucial for ensuring food security and promoting sustainable agricultural development. At present, the research on the utilization of cropland resources primarily focuses on the multiple cropping index and cropping intensity, but these data are [...] Read more.
The scientific evaluation of cropland resource utilization efficiency is crucial for ensuring food security and promoting sustainable agricultural development. At present, the research on the utilization of cropland resources primarily focuses on the multiple cropping index and cropping intensity, but these data are insufficient to reveal long-term trends and potential future changes in crop production. To fill this knowledge gap, this study took Zhenjiang City, Jiangsu Province, as a case study and proposed a method to determine the distribution and spatiotemporal change frequency of single- and double-season cropping patterns using spatiotemporal fusion and crop phenological rhythm. By combining Sentinel-2 NDVI and MOD13Q1 satellite data, a dataset with 10 m resolution was developed to show the interannual distribution frequency of the three cropping patterns in the study area. The accuracy evaluation revealed that the interannual cropping intensity distribution frequency of the three cropping patterns exhibited good verification accuracy, with an average overall accuracy and Kappa coefficient of 81.53% and 0.68, respectively. This study provides essential support for government agencies to assess future food production potential and develop policies for improving cropland use efficiency. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)
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20 pages, 11001 KiB  
Article
Investigation of Peanut Leaf Spot Detection Using Superpixel Unmixing Technology for Hyperspectral UAV Images
by Qiang Guan, Shicheng Qiao, Shuai Feng and Wen Du
Agriculture 2025, 15(6), 597; https://doi.org/10.3390/agriculture15060597 - 11 Mar 2025
Viewed by 531
Abstract
Leaf spot disease significantly impacts peanut growth. Timely, effective, and accurate monitoring of leaf spot severity is crucial for high-yield and high-quality peanut production. Hyperspectral technology from unmanned aerial vehicles (UAVs) is widely employed for disease detection in agricultural fields, but the low [...] Read more.
Leaf spot disease significantly impacts peanut growth. Timely, effective, and accurate monitoring of leaf spot severity is crucial for high-yield and high-quality peanut production. Hyperspectral technology from unmanned aerial vehicles (UAVs) is widely employed for disease detection in agricultural fields, but the low spatial resolution of imagery affects accuracy. In this study, peanuts with varying levels of leaf spot disease were detected using hyperspectral images from UAVs. Spectral features of crops and backgrounds were extracted using simple linear iterative clustering (SLIC), the homogeneity index, and k-means clustering. Abundance estimation was conducted using fully constrained least squares based on a distance strategy (D-FCLS), and crop regions were extracted through threshold segmentation. Disease severity was determined based on the average spectral reflectance of crop regions, utilizing classifiers such as XGBoost, the MLP, and the GA-SVM. Results indicate that crop spectra extracted using the superpixel-based unmixing method effectively captured spectral variability, leading to more accurate disease detection. By optimizing threshold values, a better balance between completeness and the internal variability of crop regions was achieved, allowing for the precise extraction of crop regions. Compared to other unmixing methods and manual visual interpretation techniques, the proposed method achieved excellent results, with an overall accuracy of 89.08% and a Kappa coefficient of 85.42% for the GA-SVM classifier. This method provides an objective, efficient, and accurate solution for detecting peanut leaf spot disease, offering technical support for field management with promising practical applications. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)
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24 pages, 6275 KiB  
Article
Evaluation and Early Detection of Downy Mildew of Lettuce Using Hyperspectral Imagery
by Songtao Ban, Minglu Tian, Dong Hu, Mengyuan Xu, Tao Yuan, Xiuguo Zheng, Linyi Li and Shiwei Wei
Agriculture 2025, 15(5), 444; https://doi.org/10.3390/agriculture15050444 - 20 Feb 2025
Cited by 2 | Viewed by 770
Abstract
This study combines hyperspectral imaging technology with biochemical parameter analysis to facilitate the disease severity evaluation and early detection of lettuce downy mildew. The results reveal a significant negative correlation between the disease index (DI) and the levels of flavonoids (r = [...] Read more.
This study combines hyperspectral imaging technology with biochemical parameter analysis to facilitate the disease severity evaluation and early detection of lettuce downy mildew. The results reveal a significant negative correlation between the disease index (DI) and the levels of flavonoids (r = −0.523) and anthocyanins (r = −0.746), indicating the role of these secondary metabolites in enhancing plant resistance. Analysis of hyperspectral data identified that spectral regions (410–503 nm, 510–615 nm, and 630–690 nm) and vegetation indices like PRI and ARI2 were highly correlated with DI, flavonoids, and anthocyanins, providing potential spectral indicators for disease assessment and early detection. Moreover, regression models developed using Partial Least Squares (PLS), Random Forest (RF), and Convolutional Neural Network (CNN) algorithms demonstrated high accuracy and reliability in predicting DI, flavonoids, and anthocyanins, with the highest R2 of 0.857, 0.910, and 0.963, respectively. The classification model using PLS, RF, and CNN successfully detected early physiological changes in lettuce within 24 h post-infection (highest accuracy = 0.764), offering an effective tool for early disease detection. The key spectral parameters in the PLS-DA model, like PRI, also demonstrated strong correlations with DI. These findings provide a scientific basis and practical tools for managing lettuce downy mildew and resistance breeding while laying a foundation for broader applications of hyperspectral imaging in plant pathology. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)
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19 pages, 5781 KiB  
Article
UAV-Multispectral Based Maize Lodging Stress Assessment with Machine and Deep Learning Methods
by Minghu Zhao, Dashuai Wang, Qing Yan, Zhuolin Li and Xiaoguang Liu
Agriculture 2025, 15(1), 36; https://doi.org/10.3390/agriculture15010036 - 26 Dec 2024
Viewed by 941
Abstract
Maize lodging is a prevalent stress that can significantly diminish corn yield and quality. Unmanned aerial vehicles (UAVs) remote sensing is a practical means to quickly obtain lodging information at field scale, such as area, severity, and distribution. However, existing studies primarily use [...] Read more.
Maize lodging is a prevalent stress that can significantly diminish corn yield and quality. Unmanned aerial vehicles (UAVs) remote sensing is a practical means to quickly obtain lodging information at field scale, such as area, severity, and distribution. However, existing studies primarily use machine learning (ML) methods to qualitatively analyze maize lodging (lodging and non-lodging) or estimate the maize lodging percentage, while there is less research using deep learning (DL) to quantitatively estimate maize lodging parameters (type, severity, and direction). This study aims to introduce advanced DL algorithms into the maize lodging classification task using UAV-multispectral images and investigate the advantages of DL compared with traditional ML methods. This study collected a UAV-multispectral dataset containing non-lodging maize and lodging maize with different lodging types, severities, and directions. Additionally, 22 vegetation indices (VIs) were extracted from multispectral data, followed by spatial aggregation and image cropping. Five ML classifiers and three DL models were trained to classify the maize lodging parameters. Finally, we compared the performance of ML and DL models in evaluating maize lodging parameters. The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. However, the maize lodging classification performance of DL models is significantly better than that of ML methods. Specifically, Swin-T performs better than ResNet-50 and ConvNeXt-T, with an OA reaching 96.02% and a Kappa coefficient of 0.9574. This can be attributed to the fact that Swin-T can more effectively extract detailed information that accurately characterizes maize lodging traits from UAV-multispectral data. This study demonstrates that combining DL with UAV-multispectral data enables a more comprehensive understanding of maize lodging type, severity, and direction, which is essential for post-disaster rescue operations and agricultural insurance claims. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)
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18 pages, 8018 KiB  
Article
STBNA-YOLOv5: An Improved YOLOv5 Network for Weed Detection in Rapeseed Field
by Tao Tao and Xinhua Wei
Agriculture 2025, 15(1), 22; https://doi.org/10.3390/agriculture15010022 - 25 Dec 2024
Viewed by 924
Abstract
Rapeseed is one of the primary oil crops; yet, it faces significant threats from weeds. The ideal method for applying herbicides would be selective variable spraying, but the primary challenge lies in automatically identifying weeds. To address the issues of dense weed identification, [...] Read more.
Rapeseed is one of the primary oil crops; yet, it faces significant threats from weeds. The ideal method for applying herbicides would be selective variable spraying, but the primary challenge lies in automatically identifying weeds. To address the issues of dense weed identification, frequent occlusion, and varying weed sizes in rapeseed fields, this paper introduces a STBNA-YOLOv5 weed detection model and proposes three enhanced algorithms: incorporating a Swin Transformer encoder block to bolster feature extraction capabilities, utilizing a BiFPN structure coupled with a NAM attention mechanism module to efficiently harness feature information, and incorporating an adaptive spatial fusion module to enhance recognition sensitivity. Additionally, the random occlusion technique and weed category image data augmentation method are employed to diversify the dataset. Experimental results demonstrate that the STBNA-YOLOv5 model outperforms detection models such as SDD, Faster-RCNN, YOLOv3, DETR, and EfficientDet in terms of Precision, F1-score, and mAP@0.5, achieving scores of 0.644, 0.825, and 0.908, respectively. For multi-target weed detection, the study presents detection results under various field conditions, including sunny, cloudy, unobstructed, and obstructed. The results indicate that the weed detection model can accurately identify both rapeseed and weed species, demonstrating high stability. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)
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18 pages, 15492 KiB  
Article
D3-YOLOv10: Improved YOLOv10-Based Lightweight Tomato Detection Algorithm Under Facility Scenario
by Ao Li, Chunrui Wang, Tongtong Ji, Qiyang Wang and Tianxue Zhang
Agriculture 2024, 14(12), 2268; https://doi.org/10.3390/agriculture14122268 - 11 Dec 2024
Cited by 3 | Viewed by 1253
Abstract
Accurate and efficient tomato detection is one of the key techniques for intelligent automatic picking in the area of precision agriculture. However, under the facility scenario, existing detection algorithms still have challenging problems such as weak feature extraction ability for occlusion conditions and [...] Read more.
Accurate and efficient tomato detection is one of the key techniques for intelligent automatic picking in the area of precision agriculture. However, under the facility scenario, existing detection algorithms still have challenging problems such as weak feature extraction ability for occlusion conditions and different fruit sizes, low accuracy on edge location, and heavy model parameters. To address these problems, this paper proposed D3-YOLOv10, a lightweight YOLOv10-based detection framework. Initially, a compact dynamic faster network (DyFasterNet) was developed, where multiple adaptive convolution kernels are aggregated to extract local effective features for fruit size adaption. Additionally, the deformable large kernel attention mechanism (D-LKA) was designed for the terminal phase of the neck network by adaptively adjusting the receptive field to focus on irregular tomato deformations and occlusions. Then, to further improve detection boundary accuracy and convergence, a dynamic FM-WIoU regression loss with a scaling factor was proposed. Finally, a knowledge distillation scheme using semantic frequency prompts was developed to optimize the model for lightweight deployment in practical applications. We evaluated the proposed framework using a self-made tomato dataset and designed a two-stage category balancing method based on diffusion models to address the sample class-imbalanced issue. The experimental results demonstrated that the D3-YOLOv10 model achieved an mAP0.5 of 91.8%, with a substantial reduction of 54.0% in parameters and 64.9% in FLOPs, compared to the benchmark model. Meanwhile, the detection speed of 80.1 FPS more effectively meets the demand for real-time tomato detection. This study can effectively contribute to the advancement of smart agriculture research on the detection of fruit targets. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)
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