scholarly journals Development of Spectral Disease Indices for Southern Corn Rust Detection and Severity Classification

2020 ◽  
Vol 12 (19) ◽  
pp. 3233
Author(s):  
Ran Meng ◽  
Zhengang Lv ◽  
Jianbing Yan ◽  
Gengshen Chen ◽  
Feng Zhao ◽  
...  
Keyword(s):  
Severity Index ◽  
Plant Diseases ◽  
Reflectance Spectra ◽  
Support Vector ◽  
Leaf Water Content ◽  
Leaf Reflectance ◽  
Measurement Capability ◽  
Southern Corn Rust ◽  
Severity Classification

Southern Corn Rust (SCR) is one of the most destructive diseases in corn production, significantly affecting corn quality and yields globally. Field-based fast, nondestructive diagnosis of SCR is critical for smart agriculture applications to reduce pesticide use and ensure food safety. The development of spectral disease indices (SDIs), based on in situ leaf reflectance spectra, has proven to be an effective method in detecting plant diseases in the field. However, little is known about leaf spectral signatures that can assist in the accurate diagnosis of SCR, and no SDIs-based model has been reported for the field-based SCR monitoring. Here, to address those issues, we developed SDIs-based monitoring models to detect SCR-infected leaves and classify SCR damage severity. In detail, we first collected in situ leaf reflectance spectra (350–2500 nm) of healthy and infected corn plants with three severity levels (light, medium, and severe) using a portable spectrometer. Then, the RELIEF-F algorithm was performed to select the most discriminative features (wavelengths) and two band normalized differences for developing SDIs (i.e., health index and severity index) in SCR detection and severity classification, respectively. The leaf reflectance spectra, most sensitive to SCR detection and severity classification, were found in the 572 nm, 766 nm, and 1445 nm wavelength and 575 nm, 640 nm, and 1670 nm wavelength, respectively. These spectral features were associated with leaf pigment and leaf water content. Finally, by employing a support vector machine (SVM), the performances of developed SCR-SDIs were assessed and compared with 38 stress-related vegetation indices (VIs) identified in the literature. The SDIs-based models developed in this study achieved an overall accuracy of 87% and 70% in SCR detection and severity classification, 1.1% and 8.3% higher than the other best VIs-based model under study, respectively. Our results thus suggest that the SCR-SDIs is a promising tool for fast, nondestructive diagnosis of SCR in the field over large areas. To our knowledge, this study represents one of the first few efforts to provide a theoretical basis for remote sensing of SCR at field and larger scales. With the increasing use of unmanned aerial vehicles (UAVs) with hyperspectral measurement capability, more studies should be conducted to expand our developed SCR-SDIs for SCR monitoring at different study sites and growing stages in the future.

scholarly journals Evaluating the Performance of Hyperspectral Leaf Reflectance to Detect Water Stress and Estimation of Photosynthetic Capacities

2021 ◽  
Vol 13 (11) ◽  
pp. 2160
Author(s):  
Jing-Jing Zhou ◽  
Ya-Hao Zhang ◽  
Ze-Min Han ◽  
Xiao-Yang Liu ◽  
Yong-Feng Jian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Stress ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Reflectance Spectra ◽  
Support Vector ◽  
Hyperspectral Reflectance ◽  
Leaf Reflectance ◽  
Photosynthetic Traits

Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees by conducting a greenhouse experiment. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards.

scholarly journals Experimental determination of reflectance spectra of Antarctic krill (Euphausia superba) in the Scotia Sea

2021 ◽  
pp. 1-13
Author(s):  
Anna Belcher ◽  
Sophie Fielding ◽  
Andrew Gray ◽  
Lauren Biermann ◽  
Gabriele Stowasser ◽  
...  
Keyword(s):  
Antarctic Krill ◽  
Euphausia Superba ◽  
Reflectance Spectra ◽  
Spectral Signature ◽  
Absorption Feature ◽  
Scotia Sea ◽  
Surface Ocean ◽  
Measuring Surface ◽  
Ground Truthing

Abstract Antarctic krill are the dominant metazoan in the Southern Ocean in terms of biomass; however, their wide and patchy distribution means that estimates of their biomass are still uncertain. Most currently employed methods do not sample the upper surface layers, yet historical records indicate that large surface swarms can change the water colour. Ocean colour satellites are able to measure the surface ocean synoptically and should theoretically provide a means for detecting and measuring surface krill swarms. Before we can assess the feasibility of remote detection, more must be known about the reflectance spectra of krill. Here, we measure the reflectance spectral signature of Antarctic krill collected in situ from the Scotia Sea and compare it to that of in situ water. Using a spectroradiometer, we measure a strong absorption feature between 500 and 550 nm, which corresponds to the pigment astaxanthin, and high reflectance in the 600–700 nm range due to the krill's red colouration. We find that the spectra of seawater containing krill is significantly different from seawater only. We conclude that it is tractable to detect high-density swarms of krill remotely using platforms such as optical satellites and unmanned aerial vehicles, and further steps to carry out ground-truthing campaigns are now warranted.

scholarly journals LSSVM-Based Rock Failure Criterion and Its Application in Numerical Simulation

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Changxing Zhu ◽  
Hongbo Zhao ◽  
Zhongliang Ru
Keyword(s):  
Rock Mass ◽  
Failure Criterion ◽  
Nonlinear Problems ◽  
Failure Criteria ◽  
Rock Failure ◽  
Support Vector ◽  
Failure Behavior ◽  
Circular Tunnel ◽  
Vector Machines

A rock failure criterion is very important for the prediction of the failure of rocks or rock masses in rock mechanics and engineering. Least squares support vector machines (LSSVM) are a powerful tool for addressing complex nonlinear problems. This paper describes a LSSVM-based rock failure criterion for analyzing the deformation of a circular tunnel under differentin situstresses without assuming a function form. First, LSSVM was used to represent the nonlinear relationship between the mechanical properties of rock and the failure behavior of the rock in order to construct a rock failure criterion based on experimental data. Then, this was used in a hypothetical numerical analysis of a circular tunnel to analyze the mechanical behavior of the rock mass surrounding the tunnel. The Mohr-Coulomb and Hoek-Brown failure criteria were also used to analyze the same case, and the results were compared; these clearly indicate that LSSVM can be used to establish a rock failure criterion and to predict the failure of a rock mass during excavation of a circular tunnel.

Classification of plant diseases using machine and deep learning

2021 ◽  
Vol 11 (1) ◽  
pp. 491-508
Author(s):  
Monika Lamba ◽  
Yogita Gigras ◽  
Anuradha Dhull
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Plant Disease ◽  
Nearest Neighbor ◽  
Early Recognition ◽  
Activation Function ◽  
Plant Diseases ◽  
Support Vector ◽  
Specificity And Sensitivity ◽  
Proposed Model

Abstract Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN (k-nearest neighbor), Naïve Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) is supposed to possess tremendous potential in enhancing the accuracy. This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets to solve binary and multiclass subcategories of plant diseases. Using the proposed model, results achieved are better, obtaining 99.4% accuracy and 99.9% sensitivity for binary class and 99.2% accuracy for multiclass. It is proven that the proposed model outperforms other approaches, namely LibSVM, SMO (sequential minimal optimization), and DL with activation function softmax and softsign in terms of F-measure, recall, MCC (Matthews correlation coefficient), specificity and sensitivity.

scholarly journals Using Optical Water Types to Monitor Changes in Optically Complex Inland and Coastal Waters

2019 ◽  
Vol 11 (19) ◽  
pp. 2297 ◽  
Author(s):  
Kristi Uudeberg ◽  
Ilmar Ansko ◽  
Getter Põru ◽  
Ave Ansper ◽  
Anu Reinart
Keyword(s):  
Remote Sensing ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Suspended Sediments ◽  
Reflectance Spectra ◽  
Water Type ◽  
Colored Dissolved Organic Matter ◽  
Water Color ◽  
Sentinel 2

The European Space Agency’s Copernicus satellites Sentinel-2 and Sentinel-3 provide observations with high spectral, spatial, and temporal resolution which can be used to monitor inland and coastal waters. Such waters are optically complex, and the water color may vary from completely clear to dark brown. The main factors influencing water color are colored dissolved organic matter, phytoplankton, and suspended sediments. Recently, there has been a growing interest in the use of the optical water type (OWT) classification in the remote sensing of ocean color. Such classification helps to clarify relationships between different properties inside a certain class and quantify variation between classes. In this study, we present a new OWT classification based on the in situ measurements of reflectance spectra for boreal region lakes and coastal areas without extreme optical conditions. This classification divides waters into five OWT (Clear, Moderate, Turbid, Very Turbid, and Brown) and shows that different OWTs have different remote sensing reflectance spectra and that each OWT is associated with a specific bio-optical condition. Developed OWTs are distinguishable by both the MultiSpectral Instrument (MSI) and the Ocean and Land Color Instrument (OLCI) sensors, and the accuracy of the OWT assignment was 95% for both the MSI and OLCI bands. To determine OWT from MSI images, we tested different atmospheric correction (AC) processors, namely ACOLITE, C2RCC, POLYMER, and Sen2Cor and for OLCI images, we tested AC processors ALTNNA, C2RCC, and L2. The C2RCC AC processor was the most accurate and reliable for use with MSI and OLCI images to estimate OWTs.

scholarly journals Estimation of Rice Height and Biomass Using Multitemporal SAR Sentinel-1 for Camargue, Southern France

2018 ◽  
Vol 10 (9) ◽  
pp. 1394 ◽  
Author(s):  
Emile Ndikumana ◽  
Dinh Ho Tong Minh ◽  
Hai Dang Nguyen ◽  
Nicolas Baghdadi ◽  
Dominique Courault ◽  
...  
Keyword(s):  
Random Forest ◽  
Speckle Noise ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Dual Polarization ◽  
Southern France ◽  
Biophysical Parameter ◽  
Radar Images ◽  
Dry Biomass

The research and improvement of methods to be used for crop monitoring are currently major challenges, especially for radar images due to their speckle noise nature. The European Space Agency’s (ESA) Sentinel-1 constellation provides synthetic aperture radar (SAR) images coverage with a 6-day revisit period at a high spatial resolution of pixel spacing of 20 m. Sentinel-1 data are considerably useful, as they provide valuable information of the vegetation cover. The objective of this work is to study the capabilities of multitemporal radar images for rice height and dry biomass retrievals using Sentinel-1 data. To do this, we train Sentinel-1 data against ground measurements with classical machine learning techniques (Multiple Linear Regression (MLR), Support Vector Regression (SVR) and Random Forest (RF)) to estimate rice height and dry biomass. The study is carried out on a multitemporal Sentinel-1 dataset acquired from May 2017 to September 2017 over the Camargue region, southern France. The ground in-situ measurements were made in the same period to collect rice height and dry biomass over 11 rice fields. The images were processed in order to produce a radar stack in C-band including dual-polarization VV (Vertical receive and Vertical transmit) and VH (Vertical receive and Horizontal transmit) data. We found that non-parametric methods (SVR and RF) had a better performance over the parametric MLR method for rice biophysical parameter retrievals. The accuracy of rice height estimation showed that rice height retrieval was strongly correlated to the in-situ rice height from dual-polarization, in which Random Forest yielded the best performance with correlation coefficient R 2 = 0.92 and the root mean square error (RMSE) 16% (7.9 cm). In addition, we demonstrated that the correlation of Sentinel-1 signal to the biomass was also very high in VH polarization with R 2 = 0.9 and RMSE = 18% (162 g·m − 2 ) (with Random Forest method). Such results indicate that the highly qualified Sentinel-1 radar data could be well exploited for rice biomass and height retrieval and they could be used for operational tasks.

scholarly journals In-Situ Monitoring and Diagnosing for Fused Filament Fabrication Process Based on Vibration Sensors

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2589 ◽  
Author(s):  
Yongxiang Li ◽  
Wei Zhao ◽  
Qiushi Li ◽  
Tongcai Wang ◽  
Gong Wang
Keyword(s):  
Product Quality ◽  
Real Time ◽  
Case Studies ◽  
Complex Geometry ◽  
Identification Accuracy ◽  
In Situ Monitoring ◽  
Support Vector ◽  
Fused Filament Fabrication ◽  
Vibration Sensors

Fused filament fabrication (FFF) is one of the most widely used additive manufacturing (AM) technologies and it has great potential in fabricating prototypes with complex geometry. For high quality manufacturing, monitoring the products in real time is as important as maintaining the FFF machine in the normal state. This paper introduces an approach that is based on the vibration sensors and data-driven methods for in-situ monitoring and diagnosing the FFF process. The least squares support vector machine (LS-SVM) algorithm has been applied for identifying the normal and filament jam states of the FFF machine, besides fault diagnosis in real time. The identification accuracy for the case studies explored here using LS-SVM is greater than 90%. Furthermore, to ensure the product quality during the FFF process, the back-propagation neural network (BPNN) algorithm has been used to monitor and diagnose the quality defects, as well as the warpage and material stack caused by abnormal leakage for the products in-situ. The diagnosis accuracy for the case studies explored here using BPNN is greater than 95%. Results from the experiments show that the proposed approach can accurately recognize the machine failures and quality defects during the FFF process, thus effectively assuring the product quality.

Sign in / Sign up

Export Citation Format

Share Document