Current Research on Hyperspectral and Multispectral Imaging and Their Applications in Precision Agriculture

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

Deadline for manuscript submissions: closed (1 September 2022) | Viewed by 17159

Special Issue Editors


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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
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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral remote sensing; multispectral remote sensing; precision agriculture; data processing; data assimilation; pests and diseases; habitat monitoring; risk forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Agriculture systems are facing a variety of stresses, such as 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, early disaster 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
Dr. Yingying Dong
Guest Editors

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Keywords

  • hyperspectral imaging
  • multispectral remote sensing
  • crop monitoring and classification
  • growth monitoring
  • nutrient diagnosis
  • geophysical parameters
  • field crops
  • dimensionality reduction
  • feature extraction
  • vegetation indices
  • sensitive features

Published Papers (7 papers)

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Research

14 pages, 5939 KiB  
Article
Extraction of Winter-Wheat Planting Areas Using a Combination of U-Net and CBAM
by Jinling Zhao, Juan Wang, Haiming Qian, Yuanyuan Zhan and Yu Lei
Agronomy 2022, 12(12), 2965; https://doi.org/10.3390/agronomy12122965 - 25 Nov 2022
Cited by 4 | Viewed by 2268
Abstract
Winter wheat is one of the most important food crops in China, and it is of great significance to ensure national food security. The accurate extraction of wheat-growing areas is a prerequisite for growth assessments, stress monitoring, and yield assessments. In this study, [...] Read more.
Winter wheat is one of the most important food crops in China, and it is of great significance to ensure national food security. The accurate extraction of wheat-growing areas is a prerequisite for growth assessments, stress monitoring, and yield assessments. In this study, GF-6 (8 m resolution) and Sentinel-2 (10 m resolution) remote sensing images were used to create datasets for the accurate extraction of winter-wheat growing areas by improving the U-Net model. First, U-Net was used as the base network to extract features, and then the convolutional block attention module (CBAM) was embedded in the basic convolutional units in the coding and decoding layers of the network to enhance or suppress the features to improve the feature-expression capability of the model, and to finally complete the end-to-end winter-wheat planting-area extraction. SegNet, DeepLabV3+, and U-Net-CBAM were selected as the comparison models, and they were tested using the test set in the Sentinel-2 dataset. The precision of the U-Net-CBAM model trained on the GF-6 dataset was 84.92%, the MIoU was 77.1%, the recall was 88.28%, the overall precision (OA) was 91.64%, and the F1 was 86.45%. For training on Sentinel-2 dataset, those values were: 90.06% for precision, 83.18% for MIoU, 90.78% for recall, 93.93% for OA, and 90.52% for F1, which showed significantly better results than those of the comparison models, indicating that U-Net-CBAM improved the accuracy of winter-wheat area extraction. It also showed that the segmentation performance of the training and test sets from different datasets was much lower than the segmentation performance from the same dataset. Full article
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16 pages, 3298 KiB  
Article
Wheat Yield Estimation Using Remote Sensing Indices Derived from Sentinel-2 Time Series and Google Earth Engine in a Highly Fragmented and Heterogeneous Agricultural Region
by Hajar Saad El Imanni, Abderrazak El Harti and Lahcen El Iysaouy
Agronomy 2022, 12(11), 2853; https://doi.org/10.3390/agronomy12112853 - 15 Nov 2022
Cited by 12 | Viewed by 3264
Abstract
In Morocco, monitoring and estimation of wheat yield at the regional and national scales are critical issues for national food security. The recent Sentinel-2 imagery offers potential for managing grain production systems on a field and regional level. The present study was planned [...] Read more.
In Morocco, monitoring and estimation of wheat yield at the regional and national scales are critical issues for national food security. The recent Sentinel-2 imagery offers potential for managing grain production systems on a field and regional level. The present study was planned based on a time series of six remote sensing indices and Multiple Linear Regression (MLR) methods for real-time estimation of wheat yield using the Google Earth Engine (GEE) platform in a highly heterogeneous and fragmented agricultural region, such as the Tadla Irrigated Perimeter (TIP). First, the spatial distribution of wheat in the TIP region was mapped by performing Random Forest (RF) classification of Sentinel 2 images. Following that, using MLR models, the wheat yield of nine sampled fields was estimated for the different phenological stages of wheat. The yield measured in-situ was the independent variable of the regressions. The dependent variables included the remote sensing indices derived from Sentinel-2. The remote sensing index and the phenological period of the greatest model were investigated to estimate and map the wheat yield in the entire study area. The RF generated the wheat mapping of the study area with an overall accuracy (OA) of 93.82%. Furthermore, the coefficient of determination (R2) of the tested MLR was from 0.53 to 0.89, while the Root Mean Square Error (RMSE) varied from 4.29 to 7.78 q ha−1. The best model was the one that uses the Green Normalized Difference Vegetation Index (GNDVI) in the tillering and maturity stages. Full article
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13 pages, 3012 KiB  
Article
Rapeseed Variety Recognition Based on Hyperspectral Feature Fusion
by Fan Liu, Fang Wang, Xiaoqiao Wang, Guiping Liao, Zaiqi Zhang, Yuan Yang and Yangmiao Jiao
Agronomy 2022, 12(10), 2350; https://doi.org/10.3390/agronomy12102350 - 29 Sep 2022
Cited by 4 | Viewed by 1088
Abstract
As an important oil crop, rapeseed contributes to the food security of the world. In recent years, agronomists have cultivated many new varieties, which has increased human nutritional needs. Variety recognition is of great importance for yield improvement and quality breeding. In view [...] Read more.
As an important oil crop, rapeseed contributes to the food security of the world. In recent years, agronomists have cultivated many new varieties, which has increased human nutritional needs. Variety recognition is of great importance for yield improvement and quality breeding. In view of the low efficiency and damage of traditional methods, in this paper, we develop a noninvasive model for the recognition of rapeseed varieties based on hyperspectral feature fusion. Three types of hyperspectral image features, namely, the multifractal feature, color characteristics, and trilateral parameters, are fused together to identify 11 rapeseed species. An optimal feature is selected using a simple rule, and then the three kinds of features are fused. The support vector machine kernel method is employed as a classifier. The average recognition rate reaches 96.35% and 93.71% for distinguishing two species and 11 species, respectively. The abundance test model demonstrates that our model possesses robustness. The high recognition rate is almost independent of the number of modeling samples and classifiers. This result can provide some practical experience and method guidance for the rapid recognition of rapeseed varieties. Full article
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13 pages, 2001 KiB  
Article
Improvement of Wheat Growth Information by Fusing UAV Visible and Thermal Infrared Images
by Jun Yu, Chengquan Zhou and Jinling Zhao
Agronomy 2022, 12(9), 2087; https://doi.org/10.3390/agronomy12092087 - 01 Sep 2022
Cited by 1 | Viewed by 1125
Abstract
To improve and enrich the wheat growth information, visible and thermal infrared (TIR) remote sensing images were simultaneously acquired by an unmanned aerial vehicle (UAV). A novel algorithm was proposed for fusing the visible and TIR images combing the intensity hue saturation (IHS) [...] Read more.
To improve and enrich the wheat growth information, visible and thermal infrared (TIR) remote sensing images were simultaneously acquired by an unmanned aerial vehicle (UAV). A novel algorithm was proposed for fusing the visible and TIR images combing the intensity hue saturation (IHS) transform and regional variance matching (RVM). After registering the two images, IHS transform was first conducted to derive the Intensities of two images. Wavelet transform was then applied to the Intensities for obtaining the coefficients of low- and high-frequency sub-bands. The fusion rules of the fused image were developed based on regional correlation of wavelet decomposition coefficients. More specifically, the coefficients of low-frequency sub-bands were calculated by averaging the coefficients of two images. Regional variance was used to generate the coefficients of high-frequency sub-bands using the weighted template of a 3 × 3 pixel window. The inverse wavelet transform was used to create the new Intensity for the fused image using the low- and high-frequency coefficients. Finally, the inverse IHS transform consisting of the new Intensity, the Hue of visible image, and the Saturation of TIR image was adopted to change the IHS space to red–green–blue (RGB) color space. The fusion effects were validated by the visible and TIR images of winter wheat at the jointing stage and the middle and late grain-filling stage. Meanwhile, IHS and RV were also comparatively evaluated for validating our proposed method. The proposed algorithm can fully consider the correlation of wavelet coefficients in local regions. It overcomes the shortcomings (e.g., block phenomenon, color distortion) of traditional image fusion methods to obtain smooth, detailed and high-resolution images. Full article
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17 pages, 19098 KiB  
Article
Estimation of Leaf Area Index and Above-Ground Biomass of Winter Wheat Based on Optimal Spectral Index
by Zijun Tang, Jinjin Guo, Youzhen Xiang, Xianghui Lu, Qian Wang, Haidong Wang, Minghui Cheng, Han Wang, Xin Wang, Jiaqi An, Ahmed Abdelghany, Zhijun Li and Fucang Zhang
Agronomy 2022, 12(7), 1729; https://doi.org/10.3390/agronomy12071729 - 21 Jul 2022
Cited by 22 | Viewed by 3438
Abstract
Leaf area index (LAI) and above-ground biomass are both vital indicators for evaluating crop growth and development, while rapid and non-destructive estimation of crop LAI and above-ground biomass is of considerable significance for crop field management. Owing to the advantages of repeatable and [...] Read more.
Leaf area index (LAI) and above-ground biomass are both vital indicators for evaluating crop growth and development, while rapid and non-destructive estimation of crop LAI and above-ground biomass is of considerable significance for crop field management. Owing to the advantages of repeatable and high-throughput observations, spectral technology provides a feasible method for obtaining LAI and above-ground biomass of crops. In the present study, the spectral, LAI and above-ground biomass data of winter wheat were collected, and 7 species (14 in total) were calculated based on the original and first-order differential spectrum correlation spectral indices with LAI. Then, the correlation matrix method was used for correlation with LAI. The optimal wavelength combination was extracted, and the results were calculated as the optimal spectral index related to LAI. The calculation process of the optimal spectral index related to above-ground biomass was the same as that aforementioned. Finally, the optimal spectral index was divided into three groups of model input variables, winter wheat LAI and above-ground biomass estimation models were constructed using support vector machine (SVM), random forest (RF) and a back propagation neural network (BPNN), and the models were verified. The results show that the correlation coefficient between the highest of the optimal spectral indices, the LAI, and the above-ground biomass of winter wheat exceeded 0.6, and the correlation was good. The methods for establishing the optimal estimation models for LAI and above-ground biomass of winter wheat are all modeling methods in which the input variables are the combination of the first-order differential spectral index (combination 2) and RF. The R2 of the LAI estimation model validation set was 0.830, the RMSE was 0.276, and the MRE was 6.920; the R2 of the above-ground biomass estimation model validation set was 0.682, RMSE was 235.016, MRE was 4.336, and the accuracies of both models were high. The present research results can provide a theoretical basis for crop monitoring based on spectral technology and provide an application reference for the rapid estimation of crop growth parameters. Full article
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14 pages, 3828 KiB  
Article
Study on the Forming Mechanism of the High-Density Spot of Locust Coupled with Habitat Dynamic Changes and Meteorological Conditions Based on Time-Series Remote Sensing Images
by Jing Guo, Longlong Zhao, Wenjiang Huang, Yingying Dong and Yun Geng
Agronomy 2022, 12(7), 1610; https://doi.org/10.3390/agronomy12071610 - 04 Jul 2022
Cited by 1 | Viewed by 1290
Abstract
The outbreak of the Asian migratory locust (Locusta migratoria migratoria) (AML) can deal a great blow to agriculture and grassland farming. The emergence of high-density locusts facilitates the outbreak of locusts. Understanding the forming mechanism of the high-density spot of locust [...] Read more.
The outbreak of the Asian migratory locust (Locusta migratoria migratoria) (AML) can deal a great blow to agriculture and grassland farming. The emergence of high-density locusts facilitates the outbreak of locusts. Understanding the forming mechanism of the high-density spot of locust (HDSL) is very important for locust monitoring and control. To achieve this goal, this paper took Nong’an County, which used to form an HDSL in 2017, as the study area. Firstly, based on the habitat classification system, support vector machine (SVM), random forest (RF), and maximum likelihood (ML) methods were employed to explore the best classification method for locust habitats. Then, the optimal method was applied to monitor habitat dynamic changes from 2014 to 2017 in the HDSL in Nong’an. Finally, the HDSL forming mechanism was clarified coupled with habitat dynamic changes and meteorological data. The results showed that the SVM method was the optimal method, with an accuracy of 95.28%, which is higher than the RF and ML methods by 0.25% and 8.52%, respectively. The annual increased barren land and sufficient reeds provided adequate suitable habitats for the breeding of AML. From 2014 to 2016, the temperatures during the overwintering and hatching periods were higher than the 2010–2018 average, and the precipitation during the spawning period was lower than the 2010–2018 average. The precipitation during the growing period in 2017 was 30.8 mm less than the average from 2010 to 2018. All these characteristics were conducive to the reproduction of locusts. We concluded that the suitable habitat and meteorological conditions increased the locust quantity yearly, resulting in the formation of HDSL. These results are instrumental for monitoring potential high-risk outbreak areas, which is important to improve locust control and ensure food security. Full article
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15 pages, 1709 KiB  
Article
Recognition of Areca Leaf Yellow Disease Based on PlanetScope Satellite Imagery
by Jiawei Guo, Yu Jin, Huichun Ye, Wenjiang Huang, Jinling Zhao, Bei Cui, Fucheng Liu and Jiajian Deng
Agronomy 2022, 12(1), 14; https://doi.org/10.3390/agronomy12010014 - 23 Dec 2021
Cited by 5 | Viewed by 3273
Abstract
Areca yellow leaf disease is a major attacker of the planting and production of arecanut. The continuous expansion of arecanut (Areca catechu L.) planting areas in Hainan has placed a great need to strengthen the monitoring of this disease. At present, there [...] Read more.
Areca yellow leaf disease is a major attacker of the planting and production of arecanut. The continuous expansion of arecanut (Areca catechu L.) planting areas in Hainan has placed a great need to strengthen the monitoring of this disease. At present, there is little research on the monitoring of areca yellow leaf disease. PlanetScope imagery can achieve daily global coverage at a high spatial resolution (3 m) and is thus suitable for the high-precision monitoring of plant pest and disease. In this paper, PlanetScope images were employed to extract spectral features commonly used in disease, pest and vegetation growth monitoring for primary models. In this paper, 13 spectral features commonly used in vegetation growth and pest monitoring were selected to form the initial feature space, followed by the implementation of the Correlation Analysis (CA) and independent t-testing to optimize the feature space. Then, the Random Forest (RF), Backward Propagation Neural Network (BPNN) and AdaBoost algorithms based on feature space optimization to construct double-classification (healthy, diseased) monitoring models for the areca yellow leaf disease. The results indicated that the green, blue and red bands, and plant senescence reflectance index (PSRI) and enhanced vegetation index (EVI) exhibited highly significant differences and strong correlations with healthy and diseased samples. The RF model exhibits the highest overall recognition accuracy for areca yellow leaf disease (88.24%), 2.95% and 20.59% higher than the BPNN and AdaBoost models, respectively. The commission and omission errors were lowest with the RF model for both healthy and diseased samples. This model also exhibited the highest Kappa coefficient at 0.765. Our results exhibit the feasible application of PlanetScope imagery for the regional large-scale monitoring of areca yellow leaf disease, with the RF method identified as the most suitable for this task. Our study provides a reference for the monitoring, a rapid assessment of the area affected and the management planning of the disease in the agricultural and forestry industries. Full article
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