remotesensing-logo

Journal Browser

Journal Browser

Precision Agriculture Using Hyperspectral Images

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 56145

Special Issue Editors

Institute of BioEconomy, National Research Council (CNR-IBE), Florence, Italy
Interests: precision agriculture; crop monitoring for nutrients, water stress, and disease; hyperspectral imaging in crop fields; crop production; crop physiology

Special Issue Information

Dear Colleagues,

Precision agriculture can be defined as the application of real-time, reliable information to optimize the use of resources and the management of farming practices, minimizing environmental impacts. In this view, remote sensing represents an important part of a precision agriculture management system, with hyperspectral imaging as a powerful remote sensing tool. This topic has been intensively developed in the last 30 years, and the huge literature is a clear indication that it attracts great interest across different scientific communities (agronomy, Earth observation, natural science, etc.). The rapid evolution of technologies associated with hyperspectral imaging (electronics, information and communication technologies, data acquisition, data analysis) makes it difficult to get a clear and up-to-date picture of the novel opportunities that arise from hyperspectral data analysis in agriculture.

In the above scenario, we call for papers for publication in the Special Issue “Precision Agriculture Using Hyperspectral Images” on recent experimental research or cases studies with discussions on specific topics such as soil property and fertility sensing, crop yield estimation, crop stress detection, weed mapping, herbicide drift detection, statistical and computational methods for hyperspectral data analysis, insect/pest infestation identification using different sensors, and methodologies from ground, UAV, airborne, and satellite platforms.

Dr. Giovanni Avola
Dr. Alessandro Matese
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. Remote Sensing 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 2700 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 sensors
  • soil property and fertility sensing
  • stress detection
  • crop monitoring for yield, nutrients, water stress, and disease
  • insect/pest infestation identification
  • statistical and computational methods for hyperspectral data analysis
  • ground, UAV, airborne, and satellite platforms

Published Papers (14 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research, Review

5 pages, 195 KiB  
Editorial
An Overview of the Special Issue on “Precision Agriculture Using Hyperspectral Images”
by Giovanni Avola, Alessandro Matese and Ezio Riggi
Remote Sens. 2023, 15(7), 1917; https://doi.org/10.3390/rs15071917 - 03 Apr 2023
Cited by 5 | Viewed by 1583
Abstract
In precision agriculture systems, remote sensing has played an essential role in crop and environment monitoring, and hyperspectral imaging is a particularly effective tool in this regard [...] Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)

Research

Jump to: Editorial, Review

14 pages, 1485 KiB  
Article
Hyperspectral Reflectance and Machine Learning Approaches for the Detection of Drought and Root–Knot Nematode Infestation in Cotton
by Purushothaman Ramamoorthy, Sathishkumar Samiappan, Martin J. Wubben, John P. Brooks, Amrit Shrestha, Rajendra Mohan Panda, K. Raja Reddy and Raju Bheemanahalli
Remote Sens. 2022, 14(16), 4021; https://doi.org/10.3390/rs14164021 - 18 Aug 2022
Cited by 5 | Viewed by 2032
Abstract
Upland cotton encounters biotic and abiotic stresses during the growing season, which significantly affects the genetic potential of stress tolerance and productivity. The root-knot nematode (RKN) (Meloidogyne incognita) is a soilborne roundworm affecting cotton production. The occurrence of abiotic stress (drought [...] Read more.
Upland cotton encounters biotic and abiotic stresses during the growing season, which significantly affects the genetic potential of stress tolerance and productivity. The root-knot nematode (RKN) (Meloidogyne incognita) is a soilborne roundworm affecting cotton production. The occurrence of abiotic stress (drought stress, DS) can alter the plant–disease (RKN) interactions by enhancing host plant sensitivity. Experiments were conducted for two years under greenhouse conditions to investigate the effect of RKN and DS and their combination using nematode-resistant (Rk-Rn-1) and nematode susceptible (M8) cotton genotypes. These genotypes were subjected to four treatments: control (100% irrigation with no nematodes), RKN (100% irrigation with nematodes), DS (50% irrigation with no nematodes), and DS + RKN (50% irrigation with nematodes). We measured treatments-induced changes in cotton (i) leaf reflectance between 350 and 2500 nm; and (ii) physiology and biomass-related traits for diagnosing plant health under combined biotic and abiotic stresses. We used a maximum likelihood classification model of hyperspectral data with different dimensionality reduction techniques to learn RKN and DS stressors on two cotton genotypes. The results indicate (i) the RKN stress can be detected at an early stage of 10 days after infestation; (ii) RKN, DS, and DS + RKN can be detected with an accuracy of over 98% using bands from 350–1000 nm and 350–2500 nm. The genotypes ‘Rk-Rn-1’and ‘M8’ showed differential responses to DS, RKN, and DS + RKN. With a few exceptions, all three stressors reduced the pigments, physiology, and biomass traits and the magnitude of reduction was higher in ‘M8’ than ‘Rk-Rn-1’. Observed impact of stressors on plant growth followed DS + RKN > DS > RKN. Similarly, leaf reflectance properties exhibited a significant difference between individual stress treatments indicating that the hyperspectral sensor data can be used to discriminate RKN-infected plants from drought-stressed plants. Thus, our study reveals that hyperspectral and physiological changes in response to RKN and DS could help diagnose plant health before visual symptoms. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Figure 1

27 pages, 6220 KiB  
Article
Prototyping Crop Traits Retrieval Models for CHIME: Dimensionality Reduction Strategies Applied to PRISMA Data
by Ana B. Pascual-Venteo, Enrique Portalés, Katja Berger, Giulia Tagliabue, Jose L. Garcia, Adrián Pérez-Suay, Juan Pablo Rivera-Caicedo and Jochem Verrelst
Remote Sens. 2022, 14(10), 2448; https://doi.org/10.3390/rs14102448 - 19 May 2022
Cited by 17 | Viewed by 2576
Abstract
In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, however, dealing with spectral collinearity imposes an [...] Read more.
In preparation for new-generation imaging spectrometer missions and the accompanying unprecedented inflow of hyperspectral data, optimized models are needed to generate vegetation traits routinely. Hybrid models, combining radiative transfer models with machine learning algorithms, are preferred, however, dealing with spectral collinearity imposes an additional challenge. In this study, we analyzed two spectral dimensionality reduction methods: principal component analysis (PCA) and band ranking (BR), embedded in a hybrid workflow for the retrieval of specific leaf area (SLA), leaf area index (LAI), canopy water content (CWC), canopy chlorophyll content (CCC), the fraction of absorbed photosynthetic active radiation (FAPAR), and fractional vegetation cover (FVC). The SCOPE model was used to simulate training data sets, which were optimized with active learning. Gaussian process regression (GPR) algorithms were trained over the simulations to obtain trait-specific models. The inclusion of PCA and BR with 20 features led to the so-called GPR-20PCA and GPR-20BR models. The 20PCA models encompassed over 99.95% cumulative variance of the full spectral data, while the GPR-20BR models were based on the 20 most sensitive bands. Validation against in situ data obtained moderate to optimal results with normalized root mean squared error (NRMSE) from 13.9% (CWC) to 22.3% (CCC) for GPR-20PCA models, and NRMSE from 19.6% (CWC) to 29.1% (SLA) for GPR-20BR models. Overall, the GPR-20PCA slightly outperformed the GPR-20BR models for all six variables. To demonstrate mapping capabilities, both models were tested on a PRecursore IperSpettrale della Missione Applicativa (PRISMA) scene, spectrally resampled to Copernicus Hyperspectral Imaging Mission for the Environment (CHIME), over an agricultural test site (Jolanda di Savoia, Italy). The two strategies obtained plausible spatial patterns, and consistency between the two models was highest for FVC and LAI (R2=0.91, R2=0.86) and lowest for SLA mapping (R2=0.53). From these findings, we recommend implementing GPR-20PCA models as the most efficient strategy for the retrieval of multiple crop traits from hyperspectral data streams. Hence, this workflow will support and facilitate the preparations of traits retrieval models from the next-generation operational CHIME. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Figure 1

22 pages, 17538 KiB  
Article
Evaluation of Hybrid Models to Estimate Chlorophyll and Nitrogen Content of Maize Crops in the Framework of the Future CHIME Mission
by Gabriele Candiani, Giulia Tagliabue, Cinzia Panigada, Jochem Verrelst, Valentina Picchi, Juan Pablo Rivera Caicedo and Mirco Boschetti
Remote Sens. 2022, 14(8), 1792; https://doi.org/10.3390/rs14081792 - 08 Apr 2022
Cited by 18 | Viewed by 3140
Abstract
In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This mission will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, [...] Read more.
In the next few years, the new Copernicus Hyperspectral Imaging Mission (CHIME) is foreseen to be launched by the European Space Agency (ESA). This mission will provide an unprecedented amount of hyperspectral data, enabling new research possibilities within several fields of natural resources, including the “agriculture and food security” domain. In order to efficiently exploit this upcoming hyperspectral data stream, new processing methods and techniques need to be studied and implemented. In this work, the hybrid approach (HYB) and its variant, featuring sampling dimensionality reduction through active learning heuristics (HAL), were applied to CHIME-like data to evaluate the retrieval of crop traits, such as chlorophyll and nitrogen content at both leaf (LCC and LNC) and canopy level (CCC and CNC). The results showed that HYB was able to provide reliable estimations at canopy level (R2 = 0.79, RMSE = 0.38 g m2 for CCC and R2 = 0.84, RMSE = 1.10 g m2 for CNC) but failed at leaf level. The HAL approach improved retrieval accuracy at canopy level (best metric: R2 = 0.88 and RMSE = 0.21 g m2 for CCC; R2 = 0.93 and RMSE = 0.71 g m2 for CNC), providing good results also at leaf level (best metrics: R2 = 0.72 and RMSE = 3.31 μg cm2 for LCC; R2 = 0.56 and RMSE = 0.02 mg cm2 for LNC). The promising results obtained through the hybrid approach support the feasibility of an operational retrieval of chlorophyll and nitrogen content, e.g., in the framework of the future CHIME mission. However, further efforts are required to investigate the approach across different years, sites and crop types in order to improve its transferability to other contexts. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Graphical abstract

26 pages, 7819 KiB  
Article
Toward Automated Machine Learning-Based Hyperspectral Image Analysis in Crop Yield and Biomass Estimation
by Kai-Yun Li, Raul Sampaio de Lima, Niall G. Burnside, Ele Vahtmäe, Tiit Kutser, Karli Sepp, Victor Henrique Cabral Pinheiro, Ming-Der Yang, Ants Vain and Kalev Sepp
Remote Sens. 2022, 14(5), 1114; https://doi.org/10.3390/rs14051114 - 24 Feb 2022
Cited by 19 | Viewed by 5012
Abstract
The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological [...] Read more.
The incorporation of autonomous computation and artificial intelligence (AI) technologies into smart agriculture concepts is becoming an expected scientific procedure. The airborne hyperspectral system with its vast area coverage, high spectral resolution, and varied narrow-band selection is an excellent tool for crop physiological characteristics and yield prediction. However, the extensive and redundant three-dimensional (3D) cube data processing and computation have made the popularization of this tool a challenging task. This research integrated two important open-sourced systems (R and Python) combined with automated hyperspectral narrowband vegetation index calculation and the state-of-the-art AI-based automated machine learning (AutoML) technology to estimate yield and biomass, based on three crop categories (spring wheat, pea and oat mixture, and spring barley with red clover) with multifunctional cultivation practices in northern Europe and Estonia. Our study showed the estimated capacity of the empirical AutoML regression model was significant. The best coefficient of determination (R2) and normalized root mean square error (NRMSE) for single variety planting wheat were 0.96 and 0.12 respectively; for mixed peas and oats, they were 0.76 and 0.18 in the booting to heading stage, while for mixed legumes and spring barley, they were 0.88 and 0.16 in the reproductive growth stages. In terms of straw mass estimation, R2 was 0.96, 0.83, and 0.86, and NRMSE was 0.12, 0.24, and 0.33 respectively. This research contributes to, and confirms, the use of the AutoML framework in hyperspectral image analysis to increase implementation flexibility and reduce learning costs under a variety of agricultural resource conditions. It delivers expert yield and straw mass valuation two months in advance before harvest time for decision-makers. This study also highlights that the hyperspectral system provides economic and environmental benefits and will play a critical role in the construction of sustainable and intelligent agriculture techniques in the upcoming years. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Figure 1

21 pages, 3557 KiB  
Article
Spectral Comparison of UAV-Based Hyper and Multispectral Cameras for Precision Viticulture
by Salvatore Filippo Di Gennaro, Piero Toscano, Matteo Gatti, Stefano Poni, Andrea Berton and Alessandro Matese
Remote Sens. 2022, 14(3), 449; https://doi.org/10.3390/rs14030449 - 18 Jan 2022
Cited by 31 | Viewed by 5336
Abstract
Analysis of the spectral response of vegetation using optical sensors for non-destructive remote monitoring represents a key element for crop monitoring. Considering the wide presence on the market of unmanned aerial vehicle (UAVs) based commercial solutions, the need emerges for clear information on [...] Read more.
Analysis of the spectral response of vegetation using optical sensors for non-destructive remote monitoring represents a key element for crop monitoring. Considering the wide presence on the market of unmanned aerial vehicle (UAVs) based commercial solutions, the need emerges for clear information on the performance of these products to guide the end-user in their choice and utilization for precision agriculture applications. This work aims to compare two UAV based commercial products, represented by DJI P4M and SENOP HSC-2 for the acquisition of multispectral and hyperspectral images, respectively, in vineyards. The accuracy of both cameras was evaluated on 6 different targets commonly found in vineyards, represented by bare soil, bare-stony soil, stony soil, soil with dry grass, partially grass covered soil and canopy. Given the importance of the radiometric calibration, four methods for multispectral images correction were evaluated, taking in account the irradiance sensor equipped on the camera (M1–M2) and the use of an empirical line model (ELM) based on reference reflectance panels (M3–M4). In addition, different DJI P4M exposure setups were evaluated. The performance of the cameras was evaluated by means of the calculation of three widely used vegetation indices (VIs), as percentage error (PE) with respect to ground truth spectroradiometer measurements. The results highlighted the importance of reference panels for the radiometric calibration of multispectral images (M1–M2 average PE = 21.8–100.0%; M3–M4 average PE = 11.9–29.5%). Generally, the hyperspectral camera provided the best accuracy with a PE ranging between 1.0% and 13.6%. Both cameras showed higher performance on the pure canopy pixel target, compared to mixed targets. However, this issue can be easily solved by applying widespread segmentation techniques for the row extraction. This work provides insights to assist end-users in the UAV spectral monitoring to obtain reliable information for the analysis of spatio-temporal variability within vineyards. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Graphical abstract

19 pages, 3733 KiB  
Article
Assessing Grapevine Nutrient Status from Unmanned Aerial System (UAS) Hyperspectral Imagery
by Robert Chancia, Terry Bates, Justine Vanden Heuvel and Jan van Aardt
Remote Sens. 2021, 13(21), 4489; https://doi.org/10.3390/rs13214489 - 08 Nov 2021
Cited by 12 | Viewed by 3015
Abstract
This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal [...] Read more.
This study aimed to identify the optimal sets of spectral bands for monitoring multiple grapevine nutrients in vineyards. We used spectral data spanning 400–2500 nm and leaf samples from 100 Concord grapevine canopies, lab-analyzed for six key nutrient values, to select the optimal bands for the nutrient regression models. The canopy spectral data were obtained with unmanned aerial systems (UAS), using push-broom imaging spectrometers (hyperspectral sensors). The novel use of UAS-based hyperspectral imagery to assess the grapevine nutrient status fills the gap between in situ spectral sampling and UAS-based multispectral imaging, avoiding their inherent trade-offs between spatial and spectral resolution. We found that an ensemble feature ranking method, utilizing six different machine learning feature selection methods, produced similar regression results as the standard PLSR feature selection and regression while generally selecting fewer wavelengths. We identified a set of biochemically consistent bands (606, 641, and 1494 nm) to predict the nitrogen content with an RMSE of 0.17% (using leave-one-out cross-validation) in samples with nitrogen contents ranging between 2.4 and 3.6%. Further studying is needed to confirm the relevance and consistency of the wavelengths selected for each nutrient model, but ensemble feature selection showed promise in identifying stable sets of wavelengths for assessing grapevine nutrient contents from canopy spectra. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Graphical abstract

18 pages, 4212 KiB  
Article
Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning
by Lifei Wei, Kun Wang, Qikai Lu, Yajing Liang, Haibo Li, Zhengxiang Wang, Run Wang and Liqin Cao
Remote Sens. 2021, 13(15), 2917; https://doi.org/10.3390/rs13152917 - 24 Jul 2021
Cited by 39 | Viewed by 4359
Abstract
Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal flexibility, which open a new way to accurate agricultural monitoring. [...] Read more.
Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal flexibility, which open a new way to accurate agricultural monitoring. To extract crop types from the airborne hyperspectral images, we propose a fine classification method based on multi-feature fusion and deep learning. In this research, the morphological profiles, GLCM texture and endmember abundance features are leveraged to exploit the spatial information of the hyperspectral imagery. Then, the multiple spatial information is fused with the original spectral information to generate classification result by using the deep neural network with conditional random field (DNN+CRF) model. Specifically, the deep neural network (DNN) is a deep recognition model which can extract depth features and mine the potential information of data. As a discriminant model, conditional random field (CRF) considers both spatial and contextual information to reduce the misclassification noises while keeping the object boundaries. Moreover, three multiple feature fusion approaches, namely feature stacking, decision fusion and probability fusion, are taken into account. In the experiments, two airborne hyperspectral remote sensing datasets (Honghu dataset and Xiong’an dataset) are used. The experimental results show that the classification performance of the proposed method is satisfactory, where the salt and pepper noise is decreased, and the boundary of the ground object is preserved. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Graphical abstract

24 pages, 3935 KiB  
Article
Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer Data
by Gregor Perich, Helge Aasen, Jochem Verrelst, Francesco Argento, Achim Walter and Frank Liebisch
Remote Sens. 2021, 13(12), 2404; https://doi.org/10.3390/rs13122404 - 19 Jun 2021
Cited by 11 | Viewed by 4068
Abstract
Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring [...] Read more.
Nitrogen (N) is one of the key nutrients supplied in agricultural production worldwide. Over-fertilization can have negative influences on the field and the regional level (e.g., agro-ecosystems). Remote sensing of the plant N of field crops presents a valuable tool for the monitoring of N flows in agro-ecosystems. Available data for validation of satellite-based remote sensing of N is scarce. Therefore, in this study, field spectrometer measurements were used to simulate data of the Sentinel-2 (S2) satellites developed for vegetation monitoring by the ESA. The prediction performance of normalized ratio indices (NRIs), random forest regression (RFR) and Gaussian processes regression (GPR) for plant-N-related traits was assessed on a diverse real-world dataset including multiple crops, field sites and years. The plant N traits included the mass-based N measure, N concentration in the biomass (Nconc), and an area-based N measure approximating the plant N uptake (NUP). Spectral indices such as normalized ratio indices (NRIs) performed well, but the RFR and GPR methods outperformed the NRIs. Key spectral bands for each trait were identified using the RFR variable importance measure and the Gaussian processes regression band analysis tool (GPR-BAT), highlighting the importance of the short-wave infrared (SWIR) region for estimation of plant Nconc—and to a lesser extent the NUP. The red edge (RE) region was also important. The GPR-BAT showed that five bands were sufficient for plant N trait and leaf area index (LAI) estimation and that a surplus of bands effectively reduced prediction performance. A global sensitivity analysis (GSA) was performed on all traits simultaneously, showing the dominance of the LAI in the mixed remote sensing signal. To delineate the plant-N-related traits from this signal, regional and/or national data collection campaigns producing large crop spectral libraries (CSL) are needed. An improved database will likely enable the mapping of N at the agro-ecosystem level or for use in precision farming by farmers in the future. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Graphical abstract

19 pages, 4959 KiB  
Article
Modeling of Diurnal Changing Patterns in Airborne Crop Remote Sensing Images
by Dongdong Ma, Tanzeel U. Rehman, Libo Zhang, Hideki Maki, Mitchell R. Tuinstra and Jian Jin
Remote Sens. 2021, 13(9), 1719; https://doi.org/10.3390/rs13091719 - 29 Apr 2021
Cited by 12 | Viewed by 2624
Abstract
Airborne remote sensing technologies have been widely applied in field crop phenotyping. However, the quality of current remote sensing data suffers from significant diurnal variances. The severity of the diurnal issue has been reported in various plant phenotyping studies over the last four [...] Read more.
Airborne remote sensing technologies have been widely applied in field crop phenotyping. However, the quality of current remote sensing data suffers from significant diurnal variances. The severity of the diurnal issue has been reported in various plant phenotyping studies over the last four decades, but there are limited studies on the modeling of the diurnal changing patterns that allow people to precisely predict the level of diurnal impacts. In order to comprehensively investigate the diurnal variability, it is necessary to collect time series field images with very high sampling frequencies, which has been difficult. In 2019, Purdue agricultural (Ag) engineers deployed their first field visible to near infrared (VNIR) hyperspectral gantry platform, which is capable of repetitively imaging the same field plots every 2.5 min. A total of 8631 hyperspectral images of the same field were collected for two genotypes of corn plants from the vegetative stage V4 to the reproductive stage R1 in the 2019 growing season. The analysis of these images showed that although the diurnal variability is very significant for almost all the image-derived phenotyping features, the diurnal changes follow stable patterns. This makes it possible to predict the imaging drifts by modeling the changing patterns. This paper reports detailed diurnal changing patterns for several selected plant phenotyping features such as Normalized Difference Vegetation Index (NDVI), Relative Water Content (RWC), and single spectrum bands. For example, NDVI showed a repeatable V-shaped diurnal pattern, which linearly drops by 0.012 per hour before the highest sun angle and increases thereafter by 0.010 per hour. The different diurnal changing patterns in different nitrogen stress treatments, genotypes and leaf stages were also compared and discussed. With the modeling results of this work, Ag remote sensing users will be able to more precisely estimate the deviation/change of crop feature predictions caused by the specific imaging time of the day. This will help people to more confidently decide on the acceptable imaging time window during a day. It can also be used to calibrate/compensate the remote sensing result against the time effect. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Graphical abstract

17 pages, 5592 KiB  
Article
A Neural Network Method for Classification of Sunlit and Shaded Components of Wheat Canopies in the Field Using High-Resolution Hyperspectral Imagery
by Pouria Sadeghi-Tehran, Nicolas Virlet and Malcolm J. Hawkesford
Remote Sens. 2021, 13(5), 898; https://doi.org/10.3390/rs13050898 - 27 Feb 2021
Cited by 10 | Viewed by 2690
Abstract
(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. [...] Read more.
(1) Background: Information rich hyperspectral sensing, together with robust image analysis, is providing new research pathways in plant phenotyping. This combination facilitates the acquisition of spectral signatures of individual plant organs as well as providing detailed information about the physiological status of plants. Despite the advances in hyperspectral technology in field-based plant phenotyping, little is known about the characteristic spectral signatures of shaded and sunlit components in wheat canopies. Non-imaging hyperspectral sensors cannot provide spatial information; thus, they are not able to distinguish the spectral reflectance differences between canopy components. On the other hand, the rapid development of high-resolution imaging spectroscopy sensors opens new opportunities to investigate the reflectance spectra of individual plant organs which lead to the understanding of canopy biophysical and chemical characteristics. (2) Method: This study reports the development of a computer vision pipeline to analyze ground-acquired imaging spectrometry with high spatial and spectral resolutions for plant phenotyping. The work focuses on the critical steps in the image analysis pipeline from pre-processing to the classification of hyperspectral images. In this paper, two convolutional neural networks (CNN) are employed to automatically map wheat canopy components in shaded and sunlit regions and to determine their specific spectral signatures. The first method uses pixel vectors of the full spectral features as inputs to the CNN model and the second method integrates the dimension reduction technique known as linear discriminate analysis (LDA) along with the CNN to increase the feature discrimination and improves computational efficiency. (3) Results: The proposed technique alleviates the limitations and lack of separability inherent in existing pre-defined hyperspectral classification methods. It optimizes the use of hyperspectral imaging and ensures that the data provide information about the spectral characteristics of the targeted plant organs, rather than the background. We demonstrated that high-resolution hyperspectral imagery along with the proposed CNN model can be powerful tools for characterizing sunlit and shaded components of wheat canopies in the field. The presented method will provide significant advances in the determination and relevance of spectral properties of shaded and sunlit canopy components under natural light conditions. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Graphical abstract

16 pages, 1387 KiB  
Article
Detection of Defoliation Injury in Peanut with Hyperspectral Proximal Remote Sensing
by José Pinto, Scott Powell, Robert Peterson, David Rosalen and Odair Fernandes
Remote Sens. 2020, 12(22), 3828; https://doi.org/10.3390/rs12223828 - 21 Nov 2020
Cited by 8 | Viewed by 2901
Abstract
Remote sensing can be applied to optimize efficiency in pest detection, as an insect sampling tool. This efficiency can result in more precise recommendations for decision making in pest management. Pest detection with remote sensing is often feasible because plant biotic stress caused [...] Read more.
Remote sensing can be applied to optimize efficiency in pest detection, as an insect sampling tool. This efficiency can result in more precise recommendations for decision making in pest management. Pest detection with remote sensing is often feasible because plant biotic stress caused by herbivory triggers a defensive physiological response in plants, which generally results in changes to leaf reflectance. Therefore, the key objective of this study was to use hyperspectral proximal remote sensing and gas exchange parameters to characterize peanut leaf responses to herbivory by Stegasta bosqueella (Lepidoptera: Gelechiidae) and Spodoptera cosmioides (Lepidoptera: Noctuidae), two major pests in South American peanut (Arachis hypogaea) production. The experiment was conducted in a randomized complete block design with a 2 × 3 factorial scheme (two lepidopterous species and 3 categories of injury). The injury treatments were: (1) natural infestation by third instars of S. bosqueella, (2) natural infestation by third instars of S. cosmioides, and (3) simulation of injury with scissors to mimic larval injury. We verified that peanut leaf reflectance is different between herbivory by the two larval species, but similar among real and simulated defoliation. Similarly, we observed differences in photosynthetic rate, stomatal conductance, transpiration, and photosynthetic water use efficiency only between species but not between real and simulated larval defoliation. Our results provide information that is essential for the development of sampling and economic thresholds of S. bosqueella and S. cosmioides on the peanut. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Graphical abstract

Review

Jump to: Editorial, Research

24 pages, 3266 KiB  
Review
Plant Viral Disease Detection: From Molecular Diagnosis to Optical Sensing Technology—A Multidisciplinary Review
by Yeniu Mickey Wang, Bertram Ostendorf, Deepak Gautam, Nuredin Habili and Vinay Pagay
Remote Sens. 2022, 14(7), 1542; https://doi.org/10.3390/rs14071542 - 23 Mar 2022
Cited by 20 | Viewed by 6694
Abstract
Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with [...] Read more.
Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with a large number of samples. Recent advances in optical remote sensing offer tremendous potential for non-destructive diagnostics of plant viral diseases at large spatial scales. This review provides an overview of traditional diagnostic methods followed by a comprehensive description of optical sensing technology, including camera systems, platforms, and spectral data analysis to detect plant viral diseases. The paper is organized along six multidisciplinary sections: (1) Impact of plant viral disease on plant physiology and consequent phenotypic changes, (2) direct diagnostic methods, (3) traditional indirect detection methods, (4) optical sensing technologies, (5) data processing techniques and modelling for disease detection, and (6) comparison of the costs. Finally, the current challenges and novel ideas of optical sensing for detecting plant viruses are discussed. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Graphical abstract

17 pages, 922 KiB  
Review
Multi-Sensors Remote Sensing Applications for Assessing, Monitoring, and Mapping NPK Content in Soil and Crops in African Agricultural Land
by Khalil Misbah, Ahmed Laamrani, Keltoum Khechba, Driss Dhiba and Abdelghani Chehbouni
Remote Sens. 2022, 14(1), 81; https://doi.org/10.3390/rs14010081 - 24 Dec 2021
Cited by 14 | Viewed by 5938
Abstract
Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper [...] Read more.
Demand for agricultural products is increasing as population continues to grow in Africa. To attain a higher crop yield while preserving the environment, appropriate management of macronutrients (i.e., nitrogen (N), phosphorus (P) and potassium (K)) and crops are of critical prominence. This paper aims to review the state of art of the use of remote sensing in soil agricultural applications, especially in monitoring NPK availability for widely grown crops in Africa. In this study, we conducted a substantial literature review of the use of airborne imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing spectral information, and advances of these applications in farming practices by the African scientific community. Here we aimed to identify knowledge gaps in this field and challenges related to the acquisition, processing, and analysis of hyperspectral imagery for soil agriculture investigations. To do so, publications over the past 10 years (i.e., 2008–2021) in hyperspectral imaging technology and applications in monitoring macronutrients status for crops were reviewed. In this study, the imaging platforms and sensors, as well as the different methods of processing encountered across the literature, were investigated and their benefit for NPK assessment were highlighted. Furthermore, we identified and selected particular spectral regions, bands, or features that are most sensitive to describe NPK content (both in crop and soil) that allowed to characterize NPK. In this review, we proposed a hyperspectral data-based research protocol to quantify variability of NPK in soil and crop at the field scale for the sake of optimizing fertilizers application. We believe that this review will contribute promoting the adoption of hyperspectral technology (i.e., imaging and spectroscopy) for the optimization of soil NPK investigation, mapping, and monitoring in many African countries. Full article
(This article belongs to the Special Issue Precision Agriculture Using Hyperspectral Images)
Show Figures

Figure 1

Back to TopTop