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Special Issue "Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture Ⅱ"

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

Deadline for manuscript submissions: 31 July 2023 | Viewed by 7597

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Special Issue Editors

Dr. Jinling Zhao

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Guest Editor

National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230601, China
Interests: hyperspectral image processing; remote sensing monitoring and forecasting of agricultural pests and diseases; remote sensing analysis of land use/land cover; spatial pattern analysis of agricultural landscape
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Chuanjian Wang

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Guest Editor

School of Internet, Anhui University, Hefei 230039, China
Interests: remote sensing image classification; remote sensing image segmentation; remote sensing change detection; remote sensing time series; video analysis and processing

Special Issue Information

Dear Colleagues,

The agriculture system is facing a variety of stresses, such as cultivated land decrease/degradation/damage, diseases and insect pests, drought, heat, cold, frost, flooding, excessive fertilization, and environmental pollution, due to ever-increasing human interference and ongoing climate change. It is incredibly necessary to accurately and rapidly identify and quantify these stresses to support decision making. The rapid development of hyperspectral and multispectral imaging (HSI and MSI) techniques has greatly facilitated classification, monitoring, identification, diagnosis, and assessment in agriculture. Nevertheless, there are still many urgent and critical issues that need to be addressed, such as small-sample classification, spectral dimensionality reduction, sensitive spectral band selection, multiple stress identification, growth condition monitoring, disaster early warning, etc. This Special Issue focuses on exchanging knowledge and promoting development related to precision agriculture based on HSI and MSI techniques, thus facilitating their applications and demonstrations.

Dr. Jinling Zhao
Prof. Dr. Chuanjian Wang
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. 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 2200 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

hyperspectral imaging
multispectral remote sensing
crop monitoring and classification
growth monitoring
nutrient diagnosis
land cover/land use
field crops
dimensionality reduction
feature extraction
vegetation indices
sensitive features

Published Papers (7 papers)

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Research

Open AccessArticle

Investigation of the Detectability of Corn Smut Fungus (Ustilago maydis DC. Corda) Infection Based on UAV Multispectral Technology

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Agronomy 2023, 13(6), 1499; https://doi.org/10.3390/agronomy13061499 - 30 May 2023

Viewed by 435

Abstract

Corn smut fungus (Ustilago maydis [DC.] Corda) is a globally widespread pathogen affecting both forage and sweet maize hybrids, with higher significance in sweet maize. Remote sensing technologies demonstrated favorable results for disease monitoring on the field scale. The study focused on the changes in vegetation index (VI) values influenced by the pathogen. In this study, four hybrids, two forage maize and two sweet maize hybrids were examined. Artificial infection was carried out at three different doses: a low (2500 sporidium number/mL), medium (5000 sporidium number/mL) and high dose (10,000 sporidium number/mL) with a non-infected control plot for each hybrid. The experimental plots were monitored using a multispectral UAV sensor of five monochrome channels on three different dates, i.e., 7, 14 and 21 days after infection. Five different indices (NDVI, GNDVI, ENDVI, LCI, and NDRE) were determined in Quantum GIS 3.20. The obtained results demonstrated that the infection had a significant effect on the VI values in sweet maize hybrids. A high-dose infection in the Dessert R 73 hybrid resulted in significantly lower values compared to the non-infected hybrids in three indices (NDVI, LCI and GNDVI). In the case of the NOA hybrids, GNDVI and ENDVI were able to show significant differences between the values of the infection levels. Full article

(This article belongs to the Special Issue Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture Ⅱ)

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Open AccessArticle

Application of UAV RGB Images and Improved PSPNet Network to the Identification of Wheat Lodging Areas

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Agronomy 2023, 13(5), 1309; https://doi.org/10.3390/agronomy13051309 - 06 May 2023

Viewed by 443

Abstract

As one of the main disasters that limit the formation of wheat yield and affect the quality of wheat, lodging poses a great threat to safety production. Therefore, an improved PSPNet (Pyramid Scene Parsing Network) integrating the Normalization-based Attention Module (NAM) (NAM-PSPNet) was applied to the high-definition UAV RGB images of wheat lodging areas at the grain-filling stage and maturity stage with the height of 20 m and 40 m. First, based on the PSPNet network, the lightweight neural network MobileNetV2 was used to replace ResNet as the feature extraction backbone network. The deep separable convolution was used to replace the standard convolution to reduce the amount of model parameters and calculations and then improve the extraction speed. Secondly, the pyramid pool structure of multi-dimensional feature fusion was constructed to obtain more detailed features of UAV images and improve accuracy. Then, the extracted feature map was processed by the NAM to identify the less significant features and compress the model to reduce the calculation. The U-Net, SegNet and DeepLabv3+ were selected as the comparison models. The results show that the extraction effect at the height of 20 m and the maturity stage is the best. For the NAM-PSPNet, the MPA (Mean Pixel Accuracy), MIoU (Mean Intersection over Union), Precision, Accuracy and Recall is, respectively, 89.32%, 89.32%, 94.95%, 94.30% and 95.43% which are significantly better than the comparison models. It is concluded that NAM-PSPNet has better extraction performance for wheat lodging areas which can provide the decisionmaking basis for severity estimation, yield loss assessment, agricultural operation, etc. Full article

(This article belongs to the Special Issue Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture Ⅱ)

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Open AccessArticle

Synchronous Retrieval of LAI and Cab from UAV Remote Sensing: Development of Optimal Estimation Inversion Framework

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Agronomy 2023, 13(4), 1119; https://doi.org/10.3390/agronomy13041119 - 14 Apr 2023

Viewed by 638

Abstract

UAV (unmanned aerial vehicle) remote sensing provides the feasibility of high-throughput phenotype nondestructive acquisition at the field scale. However, accurate remote sensing of crop physicochemical parameters from UAV optical measurements still needs to be further studied. For this purpose, we put forward a crop phenotype inversion framework based on the optimal estimation (OE) theory in this paper, originating from UAV low-altitude hyperspectral/multispectral data. The newly developed unified linearized vector radiative transfer model (UNL-VRTM), combined with the classical PROSAIL model, is used as the forward model, and the forward model was verified by the wheat canopy reflectance data, collected using the FieldSpec Handheld in Qi County, Henan Province. To test the self-consistency of the OE-based framework, we conducted forward simulations for the UAV multispectral sensors (DJI P4 Multispectral) with different observation geometries and aerosol loadings, and a total of 801 sets of validation data were obtained. In addition, parameter sensitivity analysis and information content analysis were performed to determine the contribution of crop parameters to the UAV measurements. Results showed that: (1) the forward model has a strong coupling between vegetation canopy and atmosphere environment, and the modeling process is reasonable. (2) The OE-based inversion framework can make full use of the available radiometric spectral information and had good convergence and self-consistency. (3) The UAV multispectral observations can support the synchronous retrieval of LAI (leaf area index) and Cab (chlorophyll a and b content) based on the proposed algorithm. The proposed inversion framework is expected to be a new way for phenotypic parameter extraction of crops in field environments and had some potential and feasibility for UAV remote sensing. Full article

(This article belongs to the Special Issue Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture Ⅱ)

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Open AccessArticle

Estimation of Fv/Fm in Spring Wheat Using UAV-Based Multispectral and RGB Imagery with Multiple Machine Learning Methods

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Agronomy 2023, 13(4), 1003; https://doi.org/10.3390/agronomy13041003 - 29 Mar 2023

Cited by 1 | Viewed by 3234

Abstract

The maximum quantum efficiency of photosystem II (Fv/Fm) is a widely used indicator of photosynthetic health in plants. Remote sensing of Fv/Fm using MS (multispectral) and RGB imagery has the potential to enable high-throughput screening of plant health in agricultural and ecological applications. This study aimed to estimate Fv/Fm in spring wheat at an experimental base in Hanghou County, Inner Mongolia, from 2020 to 2021. RGB and MS images were obtained at the wheat flowering stage using a Da-Jiang Phantom 4 multispectral drone. A total of 51 vegetation indices were constructed, and the measured Fv/Fm of wheat on the ground was obtained simultaneously using a Handy PEA plant efficiency analyzer. The performance of 26 machine learning algorithms for estimating Fv/Fm using RGB and multispectral imagery was compared. The findings revealed that a majority of the multispectral vegetation indices and approximately half of the RGB vegetation indices demonstrated a strong correlation with Fv/Fm, as evidenced by an absolute correlation coefficient greater than 0.75. The Gradient Boosting Regressor (GBR) was the optimal estimation model for RGB, with the important features being RGBVI and ExR. The Huber model was the optimal estimation model for MS, with the important feature being MSAVI2. The Automatic Relevance Determination (ARD) was the optimal estimation model for the combination (RGB + MS), with the important features being SIPI, ExR, and VEG. The highest accuracy was achieved using the ARD model for estimating Fv/Fm with RGB + MS vegetation indices on the test sets (Test set MAE = 0.019, MSE = 0.001, RMSE = 0.024, R² = 0.925, RMSLE = 0.014, MAPE = 0.026). The combined analysis suggests that extracting vegetation indices (SIPI, ExR, and VEG) from RGB and MS remote images by UAV as input variables of the model and using the ARD model can significantly improve the accuracy of Fv/Fm estimation at flowering stage. This approach provides new technical support for rapid and accurate monitoring of Fv/Fm in spring wheat in the Hetao Irrigation District. Full article

(This article belongs to the Special Issue Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture Ⅱ)

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Open AccessArticle

Identification of Soybean Planting Areas Combining Fused Gaofen-1 Image Data and U-Net Model

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Agronomy 2023, 13(3), 863; https://doi.org/10.3390/agronomy13030863 - 15 Mar 2023

Cited by 1 | Viewed by 523

Abstract

It is of great significance to accurately identify soybean planting areas for ensuring agricultural and industrial production. High-resolution satellite remotely sensed imagery has greatly facilitated the effective extraction of soybean planting areas but novel methods are required to further improve the identification accuracy. Two typical planting areas of Linhu Town and Baili Town in Northern Anhui Province, China, were selected to explore the accurate extraction method. The 10 m multispectral and 2 m panchromatic Gaofen-1 (GF-1) image data were first fused to produce training, test, and validation data sets after the min–max standardization and data augmentation. The deep learning U-Net model was then adopted to perform the accurate extraction of soybean planting areas. Two vital influencing factors on the accuracies of the U-Net model, including cropping size and training epoch, were compared and discussed. Specifically, three cropping sizes of 128 × 128, 256 × 256, and 512 × 512 px, and 20, 40, 60, 80, and 100 training epochs were compared to optimally determine the values of the two parameters. To verify the extraction effect of the U-Net model, comparison experiments were also conducted based on the SegNet and DeepLabv3+. The results show that U-Net achieves the highest Accuracy of 92.31% with a Mean Intersection over Union (mIoU) of 81.35%, which is higher than SegNet with an improvement of nearly 4% in Accuracy and 10% on mIoU. In addition, the mIoU has been also improved by 8.89% compared with DeepLabv3+. This study provides an effective and easily operated approach to accurately derive soybean planting areas from satellite images. Full article

(This article belongs to the Special Issue Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture Ⅱ)

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Open AccessArticle

CA-BIT: A Change Detection Method of Land Use in Natural Reserves

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Agronomy 2023, 13(3), 635; https://doi.org/10.3390/agronomy13030635 - 23 Feb 2023

Viewed by 575

Abstract

Natural reserves play a leading role in safeguarding national ecological security. Remote sensing change detection (CD) technology can identify the dynamic changes of land use and warn of ecological risks in natural reserves in a timely manner, which can provide technical support for the management of natural reserves. We propose a CD method (CA-BIT) based on the improved bitemporal image transformer (BIT) model to realize the change detection of remote sensing data of Anhui Natural Reserves in 2018 and 2021. Resnet34-CA is constructed through the combination of Resnet34 and a coordinate attention mechanism to effectively extract high-level semantic features. The BIT module is also used to efficiently enhance the original semantic features. Compared with the overall accuracy of the existing deep learning-based CD methods, that of CA-BIT is 98.34% on the natural protected area CD datasets and 99.05% on LEVIR_CD. Our method can effectively satisfy the need of CD of different land categories such as construction land, farmland, and forest land. Full article

(This article belongs to the Special Issue Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture Ⅱ)

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Open AccessArticle

Estimation of Relative Chlorophyll Content in Spring Wheat Based on Multi-Temporal UAV Remote Sensing

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Agronomy 2023, 13(1), 211; https://doi.org/10.3390/agronomy13010211 - 10 Jan 2023

Cited by 1 | Viewed by 1171

Abstract

Relative chlorophyll content (SPAD) is an important index for characterizing the nitrogen nutrient status of plants. Continuous, rapid, nondestructive, and accurate estimation of SPAD values in wheat after heading stage can positively impact subsequent nitrogen fertilization management strategies, which regulate grain filling and yield quality formation. In this study, the estimation of SPAD of leaf relative chlorophyll content in spring wheat was conducted at the experimental base in Wuyuan County, Inner Mongolia in 2021. Multispectral images of different nitrogen application levels at 7, 14, 21, and 28 days after the wheat heading stage were acquired by DJI P4M UAV. A total of 26 multispectral vegetation indices were constructed, and the measured SPAD values of wheat on the ground were obtained simultaneously using a handheld chlorophyll meter. Four machine learning algorithms, including deep neural networks (DNN), partial least squares (PLS), random forest (RF), and Adaptive Boosting (Ada) were used to construct SPAD value estimation models at different time from heading growth stages. The model’s progress was evaluated by the coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAPE). The results showed that the optimal SPAD value estimation models for different periods of independent reproductive growth stages of wheat were different, with PLS as the optimal estimation model at 7 and 14 days after heading, RF as the optimal estimation model at 21 days after heading, and Ada as the optimal estimation model at 28 d after heading. The highest accuracy was achieved using the PLS model for estimating SPAD values at 14 d after heading (training set R² = 0.767, RMSE = 3.205, MAPE = 0.060, and R² = 0.878, RMSE = 2.405, MAPE = 0.045 for the test set). The combined analysis concluded that selecting multiple vegetation indices as input variables of the model at 14 d after heading stage and using the PLS model can significantly improve the accuracy of SPAD value estimation, provides a new technical support for rapid and accurate monitoring of SPAD values in spring wheat. Full article

(This article belongs to the Special Issue Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture Ⅱ)

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