Detection Of Rust Research Articles

Detection and Classification of Ring, Rust and Yellow Sugarcane Leaf Diseases

Agriculture is an important sector in Economic and Social life. Crop disease detection is an emerging field in India. We can minimize the diseases infection on sugarcane leaf by detecting and grading the leaf disease in early stages. In this paper, we are detecting and recognize Sugar cane leaf diseases by using grey scale and color image processing and analyze the efficacy by comparing both. In grey scale processing, we presented Gradient Magnitude, Otsu method, Morphological Operations and Normalization to extract the Region of interest (ROI) i.e., disease part. In color processing initially converted RGB to L*a*b format, later K-means clustering and edge detection operations are applied on L*a*b image format. The features of Grey scale & color processed image are extracted and feed to Support Vector Machine (SVM) classifier which classifies ring, rust & yellow spot sugarcane leaf diseases. The Sugarcane leaf diseases are classified successfully with an average accuracy of 84% & 92% for grey scale & color features respectively.

A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection from High-Resolution Hyperspectral UAV Images

Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in unmanned aerial vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images.

Wheat Leaf Rust Detection at an Early Stage with Atomic Force Microscopy (AFM)

Wheat Leaf Rust Detection at an Early Stage with Atomic Force Microscopy (AFM)

Early Visual Detection of Wheat Stripe Rust Using Visible/Near-Infrared Hyperspectral Imaging.

Wheat stripe rust is one of the most important and devastating diseases in wheat production. In order to detect wheat stripe rust at an early stage, a visual detection method based on hyperspectral imaging is proposed in this paper. Hyperspectral images of wheat leaves infected by stripe rust for 15 consecutive days were collected, and their corresponding chlorophyll content (SPAD value) were measured using a handheld SPAD-502 chlorophyll meter. The spectral reflectance of the samples were then extracted from the hyperspectral images, using image segmentation based on a leaf mask. The effective wavebands were selected by the loadings of principal component analysis (PCA-loadings) and the successive projections algorithm (SPA). Next, the regression model of the SPAD values in wheat leaves was established, based on the back propagation neural network (BPNN), using the full spectra and the selected effective wavelengths as inputs, respectively. The results showed that the PCA-loadings–BPNN model had the best performance, which modeling accuracy (RC2) and validation accuracy (RP2) were 0.921 and 0.918, respectively, and the RPD was 3.363. The number of effective wavelengths extracted by this model accounted for only 3.12% of the total number of wavelengths, thus simplifying the models and improving the rate of operation greatly. Finally, the optimal models were used to estimate the SPAD of each pixel within the wheat leaf images, to generate spatial distribution maps of chlorophyll content. The visualized distribution map showed that wheat leaves infected by stripe rust could be identified six days after inoculation, and at least three days before the appearance of visible symptoms, which provides a new method for the early detection of wheat stripe rust.

RPN-FCN based Rust detection on power equipment

This paper proposes a novel RPN-FCN based rust detection approach. The RPN-FCN generates region proposals with RPN and performs full convolution for semantic segmentation of rust. The experimental result demonstrate that this approach improves the accuracy of rust detection compared with other neural networks.

Wheat leaf rust detection at canopy scale under different LAI levels using machine learning techniques

Accurate diagnosis of wheat leaf rust is of high interest for precision farming. Spectral data have been increasingly employed to detect this disease at leaf or canopy scales; however, less attention has been paid to the variations of leaf area index (LAI). Therefore, in this study, identification of wheat leaf rust was investigated at canopy scale and under different LAI levels, namely high, medium and low. Four machine learning (ML) methods including ν-support vector regression (ν-SVR), boosted regression trees (BRT), random forests regression (RFR) and Gaussian process regression (GPR) were built to estimate disease severity (DS) levels at canopy scale, where the reflectance data were measured in field situ by using a spectroradiometer, in which records spectra from 350 to 2500 nm. Results showed that ν-SVR outperformed the other ML methods at all three LAI levels with the R2 measures all being around 0.99. The results, particularly, showed that the performances of the ML methods were improved with increasing LAI value, where RFR reported the worst R2 value of 0.79 (RMSE = 8.5%) at low LAI level. The variable importance obtained using BRT showed three distinct regions of wavelengths that were appropriate across different LAI levels. The results of this research confirmed that hyperspectral signature can be reliably considered to identify wheat leaf rust disease at different LAI levels. Moreover, performances of several spectral vegetation indices (SVIs) were compared with those of the ML techniques. The results showed that the SVIs were consistently outperformed by the ML methods, particularly at low LAI level in which the SVIs were adversely affected. Nevertheless, all the SVIs, except for the RVSI, performed moderately well at high and medium LAI levels.

Performance Evaluation of VGG models in Detection of Wheat Rust

The agricultural sector is the backbone of Indian economy and social development but due to lack of awareness towards crop management, a large number of crops get wasted each year. Automated Systems are required for this purpose. This paper tries to highlight the efficiency of two existing models of deep learning, VGG16 and VGG19 for proper detection of wheat rust disease in the infected wheat crop. These two models use convolutional neural networks for image classification and which can be used to design an intelligent system which can easily detect wheat rust in crop images. This paper basically presents the comparative analysis of the accuracy and efficiency along with usability to select the best model for systems that can be used for crop safety.

Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery

The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle at an altitude of 16–24 m with a ground resolution of 1–1.5 cm/pixel. An automated yellow rust detection system is developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage (45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales.

Surface Rust Detection Using Ultrasonic Waves in a Cylindrical Geometry by Finite Element Simulation

Detection of early-stage corrosion on slender steel members is crucial for preventing buckling failures of steel structures. An active photoacoustic fiber optic sensor (FOS) system is reported herein for the early-stage steel corrosion detection of steel plates and rebars using surface ultrasonic waves. The objective of this study is to investigate a potential method for detecting surface corrosion/rust of steel rods using numerically simulated surface ultrasonic waves. The finite element method (FEM) was applied in the simulation of propagating ultrasonic waves on steel rod models. The pitch-catch mode of damage detection was adopted, in which one source (transmitter) and one sensor (receiver) were considered. In this research, radial displacements at the receiver were simulated and analyzed by short-time Fourier transform (STFT) for detecting, locating, and quantifying surface rust located between the transmitter and the receiver. From our time domain and frequency domain analyses, it was found that the presence, location, and dimensions (length, width, and depth) of surface rust can be estimated by ultrasonic wave propagation.

Predicting the climatic risk of myrtle rust during its first year in New Zealand

After the first detection of myrtle rust (Austropuccinia psidii) on mainland New Zealand in May 2017, the Ministry for Primary Industries sought information about how weather conditions would affect regional and seasonal risk of disease establishment to help plan the incursion response. Using internationally published information, a pathogen-process model was developed to predict infection, latent period and sporulation in relation to weather variables (temperature, relative humidity and solar radiation). This Myrtle Rust Process Model (MRPM) was implemented by the National Institute of Water and Atmospheric Research Limited using numerical weather model data to produce weekly maps of potential risk. Predicted risk was greatest in northern North Island and decreased further south, but was still substantial in coastal areas of the north-western South Island during summer and autumn. Risk was low in southern coastal areas of the South Island and the lowest risk occurred in mountainous areas, particularly in the South Island. Retrospective analysis of surveillance data showed that the MRPM accurately predicted geographic risk and it is currently in use for tactical planning of incursion surveillance and organism management.

New Spectral Index for Detecting Wheat Yellow Rust Using Sentinel-2 Multispectral Imagery.

Yellow rust is one of the most destructive diseases for winter wheat and has led to a significant decrease in winter wheat quality and yield. Identifying and monitoring yellow rust is of great importance for guiding agricultural production over large areas. Compared with traditional crop disease discrimination methods, remote sensing technology has proven to be a useful tool for accomplishing such a task at large scale. This study explores the potential of the Sentinel-2 Multispectral Instrument (MSI), a newly launched satellite with refined spatial resolution and three red-edge bands, for discriminating between yellow rust infection severities (i.e., healthy, slight, and severe) in winter wheat. The corresponding simulative multispectral bands for the Sentinel-2 sensor were calculated by the sensor’s relative spectral response (RSR) function based on the in situ hyperspectral data acquired at the canopy level. Three Sentinel-2 spectral bands, including B4 (Red), B5 (Re1), and B7 (Re3), were found to be sensitive bands using the random forest (RF) method. A new multispectral index, the Red Edge Disease Stress Index (REDSI), which consists of these sensitive bands, was proposed to detect yellow rust infection at different severity levels. The overall identification accuracy for REDSI was 84.1% and the kappa coefficient was 0.76. Moreover, REDSI performed better than other commonly used disease spectral indexes for yellow rust discrimination at the canopy scale. The optimal threshold method was adopted for mapping yellow rust infection at regional scales based on realistic Sentinel-2 multispectral image data to further assess REDSI’s ability for yellow rust detection. The overall accuracy was 85.2% and kappa coefficient was 0.67, which was found through validation against a set of field survey data. This study suggests that the Sentinel-2 MSI has the potential for yellow rust discrimination, and the newly proposed REDSI has great robustness and generalized ability for yellow rust detection at canopy and regional scales. Furthermore, our results suggest that the above remote sensing technology can be used to provide scientific guidance for monitoring and precise management of crop diseases and pests.

Detection and discrimination of pests and diseases in winter wheat based on spectral indices and kernel discriminant analysis

Kernel discriminant analysis (KDA) can be used as a feasible strategy for identifying plant stresses, especially for detection of pests and diseases, considering the highly nonlinear distribution of hyperspectral absorption features that respond to biophysical variations in plants caused by foliar lesions. However, traditional computation of the kernel projection features produced by hyperspectral data is always affected by redundant information among the numerous wavelengths, subsequently leading to dimension disaster. In order to alleviate this problem, the aim of this study is to propose a spectral vegetation indices-based kernel discriminant approach (SVIKDA) for the detection and classification of yellow rust, aphid, and powdery mildew in winter wheat at the leaf and canopy level. Leaf and canopy level hyperspectral reflectance datasets were measured with a total of 314 and 187 samples, respectively. Fourteen Spectral Vegetation Indices (SVIs) related to foliar biophysical variations were employed as the input sample space; then, by using correlation analysis and independent t-tests, redundant information among SVIs was removed. Subsequently, a Gaussian kernel function was utilized to cast discriminant analysis into a nonlinear framework. Finally, using 5-fold cross validation, performance and transferability of this approach were evaluated. Our results revealed that the SVIKDA outperformed conventional linear discriminant approach on detection and classification among healthy wheat leaves and leaves infected with yellow rust, aphids, and powdery mildew. At the leaf level, the classification returned the overall accuracies (OA) of 82.9%, 89.2%, 87.9% for three occurrence levels, i.e. slight, moderate, and severe (Kappa>0.85). Depending on the types and severities of infestations, the classification accuracy was between 76% and 95%; At the canopy level, the multiple classifications between healthy leaves and leaves with damages from the three different infestations still achieved an accuracy greater than 87% (Kappa=0.84). In addition, this approach was also successfully applied in disease index (DI) estimation for a specific infestation at the leaf level, and optimal DI estimation returned high coefficients of determination (R2>0.7). Furthermore, compared with the commonly used automatic classification algorithm, the SVIKDA achieved an accurate classification without losing the pathological basis of input variables. The results suggest that this method has reliable transferability and great robustness in detecting and discriminating pests and diseases for guiding precision plant protection.

Evaluation of wavelet spectral features in pathological detection and discrimination of yellow rust and powdery mildew in winter wheat with hyperspectral reflectance data

Hyperspectral absorption features are important indicators of characterizing plant biophysical variables for the automatic diagnosis of crop diseases. Continuous wavelet analysis has proven to be an advanced hyperspectral analysis technique for extracting absorption features; however, specific wavelet features (WFs) and their relationship with pathological characteristics induced by different infestations have rarely been summarized. The aim of this research is to determine the most sensitive WFs for identifying specific pathological lesions from yellow rust and powdery mildew in winter wheat, based on 314 hyperspectral samples measured in field experiments in China in 2002, 2003, 2005, and 2012. The resultant WFs could be used as proxies to capture the major spectral absorption features caused by infestation of yellow rust or powdery mildew. Multivariate regression analysis based on these WFs outperformed conventional spectral features in disease detection; meanwhile, a Fisher discrimination model exhibited considerable potential for generating separable clusters for each infestation. Optimal classification returned an overall accuracy of 91.9% with a Kappa of 0.89. This paper also emphasizes the WFs and their relationship with pathological characteristics in order to provide a foundation for the further application of this approach in monitoring winter wheat diseases at the regional scale.

Application of Near-Infrared Spectroscopy to Quantitatively Determine Relative Content of Puccnia striiformis f. sp. tritici DNA in Wheat Leaves in Incubation Period

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a devastating wheat disease worldwide. Potential application of near-infrared spectroscopy (NIRS) in detection of pathogen amounts in latently Pst-infected wheat leaves was investigated for disease prediction and control. A total of 300 near-infrared spectra were acquired from the Pst-infected leaf samples in an incubation period, and relative contents of Pst DNA in the samples were obtained using duplex TaqMan real-time PCR arrays. Determination models of the relative contents of Pst DNA in the samples were built using quantitative partial least squares (QPLS), support vector regression (SVR), and a method integrated with QPLS and SVR. The results showed that the kQPLS-SVR model built with a ratio of training set to testing set equal to 3 : 1 based on the original spectra, when the number of the randomly selected wavelength points was 700, the number of principal components was 8, and the number of the built QPLS models was 5, was the best. The results indicated that quantitative detection of Pst DNA in leaves in the incubation period could be implemented using NIRS. A novel method for determination of latent infection levels of Pst and early detection of stripe rust was provided.

Expert system for coffee rust detection based on supervised learning and graph pattern matching

Diseases in agricultural production systems represent one of the main reasons of losses and poor-quality products. For coffee production, experts in this area suggest that weather conditions and crop physical properties are the main variables that determine the development of coffee rust. This paper proposes an extraction of rules to detect coffee rust from induction of decision trees and expert knowledge. In order to obtain a model with greater expressiveness and interpretability, a graph-based representation is proposed. Finally, the extracted rules are evaluated using an expert system supported on graph pattern matching.

Automatic Wheat Leaf Rust Detection and Grading Diagnosis via Embedded Image Processing System

Wheat leaf rust is one of the major fungal diseases that makes severe drop in the wheat production. With the desire for rapid and accurate identification of disease, real-time access to the degree of that and timely measures, a wheat leaf diseases detection system based on embedded image recognition technology is designed. The system adopted the ARM9 processor with the embedded Linux platform as the main body, and the program is developed in the Qt integrated environment. At first, the captured clear disease picture of wheat leaf rust was transformed into G single channel gray image of RGB model. Second, based on exploration Sobel operator method, vertical edges detection were implemented on the gray image, eliminate the background of image and extracts binary feature point set of disease spot. Third, the noisy points in the point set is further filtered out by flood filling algorithm. Finally, the area ratio of disease spots and leaf is calculated to get the precise diagnosis of disease level. It has been verified that the recognition rate reaches 96.2% and accuracy rated reaches 92.3% with the method of image processing, which is approximately equal to the result of human vision. This system can be used as agricultural robot to inspect in the field, realizing the intelligentization in detection, identification, diagnosis and classification of crop disease.

Expert system for coffee rust detection based on supervised learning and graph pattern matching

Diseases in agricultural production systems represent one of the main reasons of losses and poor-quality products. For coffee production, experts in this area suggest that weather conditions and crop physical properties are the main variables that determine the development of coffee rust. This paper proposes an extraction of rules to detect coffee rust from induction of decision trees and expert knowledge. In order to obtain a model with greater expressiveness and interpretability, a graph-based representation is proposed. Finally, the extracted rules are evaluated using an expert system supported on graph pattern matching.

Možnosti metod NUS in Udarec-odmev za kontrolo korozije jekla v betonu

The corrosion of steel elements in reinforced concrete can cause considerable damage to civil-engineering structures. Early detection of rust is therefore very important. The aim of this paper is to evaluate the possibility of using nonlinear ultrasonic spectroscopy with a single exciting harmonic frequency and the impact-echo method for monitoring the corrosion of concrete-covered steel. For the research we manufactured concrete beams, reinforced with a standard reinforcing-steel bar passing through their centre. After concrete curing and drying, the samples were exposed to a 20 % carbon dioxide atmosphere. After the concrete pH decreased below 9.6 throughout the sample volume, the samples were immersed into a 5 % water solution of NaCl and subsequently placed into a drying oven. Measurements were carried out before the carbonation of the concrete, after it, and then after every 20th cycle of the accelerated degradation by chlorides. Nonlinear-ultrasonic-spectroscopy (NUS) methods are based on the fact that crack-induced nonlinearity makes a sensitive material-impairment indicator. The impact-echo method uses short-time mechanical impulses applied to the surface of a test sample, producing elastic waves. These waves spread throughout the sample and reflect from the surface but also from the microcracks and unobservable defects inside the sample. To verify the correctness of the NUS and impact-echo-method results, additional measurements were carried out (a confocal scanning microscope). It was proved that both methods could be used for monitoring the corrosion of concrete-covered steel.

Detection of Wheat Stem Rust Races TTHSK and PTKTK in the Ug99 Race Group in Kenya in 2014

Detection of Wheat Stem Rust Races TTHSK and PTKTK in the Ug99 Race Group in Kenya in 2014

Detection of Rust Caused by Tranzschelia mexicana on Prunus salicifolia in the United States

Detection of Rust Caused by <i>Tranzschelia mexicana</i> on <i>Prunus salicifolia</i> in the United States