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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: 25 February 2025 | Viewed by 2373

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 (3 papers)

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Research

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 575
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 472
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 [email protected], 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
Viewed by 737
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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