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Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 24035

Special Issue Editors

Dr. Paolo Spagnolo

E-Mail Website
Guest Editor
National Reseach Council of Italy (CNR), Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”, Pozzuoli, Italy
Interests: multimedia signal processing; image processing and understanding; image feature extraction and selection; neural network classifiers; object classification and tracking
Special Issues, Collections and Topics in MDPI journals
Dr. Pier Luigi Mazzeo

E-Mail Website
Guest Editor
National Reseach Council of Italy (CNR), ISASI Institute of Applied Sciences & Intelligent Systems, 80078 Pozzuoli, Italy
Interests: multimedia signal processing; image processing and understanding; image feature extraction and selection; neural network classifiers; object classification and tracking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes. The rapid development of remote sensing has made it possible to study environmental processes and changes in agriculture and also to provide important assistance in relevant practices. The goal of this Special Issue is to collect the latest developments in the application fields of precision agriculture and fire preventions. Both these two contexts were traditionally on-field tests for computer vision-based algorithms and methodologies. With the growing availability of hyperspectral sensors—that are more effective compared to multispectral remote ones—the approach to fire prevention and precision agriculture is quite different, providing an unexpected and powerful support to workers. Papers on the latest research challenges, case studies and on-field applications, limitations, and advantages of different platforms and sensors as well as future perspectives are welcomed.

Dr. Pier Luigi Mazzeo
Dr. Paolo Spagnolo
Guest Editors

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Keywords

  • Hyperspectral imaging
  • Remote sensing
  • Precision agriculture
  • Fire prevention
  • Spectral analysis
  • UAV applications
  • Smoke detection

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Published Papers (7 papers)

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Research

18 pages, 4251 KiB  
Article
A Deep Learning Framework for Processing and Classification of Hyperspectral Rice Seed Images Grown under High Day and Night Temperatures
by Víctor Díaz-Martínez, Jairo Orozco-Sandoval, Vidya Manian, Balpreet K. Dhatt and Harkamal Walia
Sensors 2023, 23(9), 4370; https://doi.org/10.3390/s23094370 - 28 Apr 2023
Cited by 11 | Viewed by 2653
Abstract
A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube [...] Read more.
A framework combining two powerful tools of hyperspectral imaging and deep learning for the processing and classification of hyperspectral images (HSI) of rice seeds is presented. A seed-based approach that trains a three-dimensional convolutional neural network (3D-CNN) using the full seed spectral hypercube for classifying the seed images from high day and high night temperatures, both including a control group, is developed. A pixel-based seed classification approach is implemented using a deep neural network (DNN). The seed and pixel-based deep learning architectures are validated and tested using hyperspectral images from five different rice seed treatments with six different high temperature exposure durations during day, night, and both day and night. A stand-alone application with Graphical User Interfaces (GUI) for calibrating, preprocessing, and classification of hyperspectral rice seed images is presented. The software application can be used for training two deep learning architectures for the classification of any type of hyperspectral seed images. The average overall classification accuracy of 91.33% and 89.50% is obtained for seed-based classification using 3D-CNN for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The DNN gives an average accuracy of 94.83% and 91% for five different treatments at each exposure duration and six different high temperature exposure durations for each treatment, respectively. The accuracies obtained are higher than those presented in the literature for hyperspectral rice seed image classification. The HSI analysis presented here is on the Kitaake cultivar, which can be extended to study the temperature tolerance of other rice cultivars. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention)
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18 pages, 4808 KiB  
Article
Graph Convolutional Network Using Adaptive Neighborhood Laplacian Matrix for Hyperspectral Images with Application to Rice Seed Image Classification
by Jairo Orozco, Vidya Manian, Estefania Alfaro, Harkamal Walia and Balpreet K. Dhatt
Sensors 2023, 23(7), 3515; https://doi.org/10.3390/s23073515 - 27 Mar 2023
Cited by 5 | Viewed by 2140
Abstract
Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information along with the spectral signature of the pixels is proposed for improving graph convolutional network [...] Read more.
Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information along with the spectral signature of the pixels is proposed for improving graph convolutional network classification of hyperspectral images. The spatial-spectral information is integrated into the adjacency matrix and processed by a single-layer graph convolutional network. The algorithm employs an adaptive neighborhood selection criteria conditioned by the class it belongs to. Compared to fixed window-based feature extraction, this method proves effective in capturing the spectral and spatial features with variable pixel neighborhood sizes. The experimental results from the Indian Pines, Houston University, and Botswana Hyperion hyperspectral image datasets show that the proposed AN-GCN can significantly improve classification accuracy. For example, the overall accuracy for Houston University data increases from 81.71% (MiniGCN) to 97.88% (AN-GCN). Furthermore, the AN-GCN can classify hyperspectral images of rice seeds exposed to high day and night temperatures, proving its efficacy in discriminating the seeds under increased ambient temperature treatments. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention)
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15 pages, 2660 KiB  
Article
Determination of Bio-Based Fertilizer Composition Using Combined NIR and MIR Spectroscopy: A Model Averaging Approach
by Khan Wali, Haris Ahmad Khan, Mark Farrell, Eldert J. Van Henten and Erik Meers
Sensors 2022, 22(15), 5919; https://doi.org/10.3390/s22155919 - 8 Aug 2022
Cited by 5 | Viewed by 2938
Abstract
Application of bio-based fertilizers is considered a practical solution to enhance soil fertility and maintain soil quality. However, the composition of bio-based fertilizers needs to be quantified before their application to the soil. Non-destructive techniques such as near-infrared (NIR) and mid-infrared (MIR) are [...] Read more.
Application of bio-based fertilizers is considered a practical solution to enhance soil fertility and maintain soil quality. However, the composition of bio-based fertilizers needs to be quantified before their application to the soil. Non-destructive techniques such as near-infrared (NIR) and mid-infrared (MIR) are generally used to quantify the composition of bio-based fertilizers in a speedy and cost-effective manner. However, the prediction performances of these techniques need to be quantified before deployment. With this motive, this study investigates the potential of these techniques to characterize a diverse set of bio-based fertilizers for 25 different properties including nutrients, minerals, heavy metals, pH, and EC. A partial least square model with wavelength selection is employed to estimate each property of interest. Then a model averaging, approach is tested to examine if combining model outcomes of NIR with MIR could improve the prediction performances of these sensors. In total, 17 of the 25 elements could be predicted to have a good performance status using individual spectral methods. Combining model outcomes of NIR with MIR resulted in an improvement, increasing the number of properties that could be predicted from 17 to 21. Most notably the improvement in prediction performance was observed for Cd, Cr, Zn, Al, Ca, Fe, S, Cu, Ec, and Na. It was concluded that the combined use of NIR and MIR spectral methods can be used to monitor the composition of a diverse set of bio-based fertilizers. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention)
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20 pages, 2569 KiB  
Article
Low-Rank and Sparse Matrix Recovery for Hyperspectral Image Reconstruction Using Bayesian Learning
by Yanbin Zhang, Long-Ting Huang, Yangqing Li, Kai Zhang and Changchuan Yin
Sensors 2022, 22(1), 343; https://doi.org/10.3390/s22010343 - 4 Jan 2022
Cited by 3 | Viewed by 2233
Abstract
In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method. The proposed method exploits spatial and spectral correlations in HSI data using [...] Read more.
In order to reduce the amount of hyperspectral imaging (HSI) data transmission required through hyperspectral remote sensing (HRS), we propose a structured low-rank and joint-sparse (L&S) data compression and reconstruction method. The proposed method exploits spatial and spectral correlations in HSI data using sparse Bayesian learning and compressive sensing (CS). By utilizing a simultaneously L&S data model, we employ the information of the principal components and Bayesian learning to reconstruct the hyperspectral images. The simulation results demonstrate that the proposed method is superior to LRMR and SS&LR methods in terms of reconstruction accuracy and computational burden under the same signal-to-noise tatio (SNR) and compression ratio. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention)
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16 pages, 5653 KiB  
Article
Classification of Aflatoxin B1 Concentration of Single Maize Kernel Based on Near-Infrared Hyperspectral Imaging and Feature Selection
by Quan Zhou, Wenqian Huang, Dong Liang and Xi Tian
Sensors 2021, 21(13), 4257; https://doi.org/10.3390/s21134257 - 22 Jun 2021
Cited by 12 | Viewed by 2831
Abstract
A rapid and nondestructive method is greatly important for the classification of aflatoxin B1 (AFB1) concentration of single maize kernel to satisfy the ever-growing needs of consumers for food safety. A novel method for classification of AFB1 concentration of single maize kernel was [...] Read more.
A rapid and nondestructive method is greatly important for the classification of aflatoxin B1 (AFB1) concentration of single maize kernel to satisfy the ever-growing needs of consumers for food safety. A novel method for classification of AFB1 concentration of single maize kernel was developed on the basis of the near-infrared (NIR) hyperspectral imaging (1100–2000 nm). Four groups of AFB1 samples with different concentrations (10, 20, 50, and 100 ppb) and one group of control samples were prepared, which were preprocessed with Savitzky–Golay (SG) smoothing and first derivative (FD) algorithms for their raw NIR spectra. A key wavelength selection method, combining the variance and order of average spectral intensity, was proposed on the basis of pretreated spectra. Moreover, principal component analysis (PCA) was conducted to reduce the dimensionality of hyperspectral data. Finally, a classification model for AFB1 concentrations was developed through linear discriminant analysis (LDA), combined with five key wavelengths and the first three PCs. The results show that the proposed method achieved an ideal performance for classifying AFB1 concentrations in a single maize kernel with overall accuracy, with an F1-score and Kappa values of 95.56%, 0.9554, and 0.9444, respectively, as well as the test accuracy yield of 88.67% for independent validation samples. The combinations of variance and order of average spectral intensity can be used for key wavelength selection which, combined with PCA, can achieve an ideal dimensionality reduction effect for model development. The findings of this study have positive significance for the classification of AFB1 concentration of maize kernels. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention)
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28 pages, 56408 KiB  
Article
Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy
by Giacomo Lazzeri, William Frodella, Guglielmo Rossi and Sandro Moretti
Sensors 2021, 21(12), 3982; https://doi.org/10.3390/s21123982 - 9 Jun 2021
Cited by 25 | Viewed by 5284
Abstract
Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively [...] Read more.
Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention)
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13 pages, 4830 KiB  
Article
A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field
by Le Wang, Lirong Xiang, Lie Tang and Huanyu Jiang
Sensors 2021, 21(2), 507; https://doi.org/10.3390/s21020507 - 13 Jan 2021
Cited by 39 | Viewed by 4450
Abstract
Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be [...] Read more.
Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting. Full article
(This article belongs to the Special Issue Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention)
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