Detection Of Rust Research Articles

Improved early detection of wheat stripe rust through integration pigments and pigment-related spectral indices quantified from UAV hyperspectral imagery

Improved early detection of wheat stripe rust through integration pigments and pigment-related spectral indices quantified from UAV hyperspectral imagery

Integrating deep learning for visual question answering in Agricultural Disease Diagnostics: Case Study of Wheat Rust

This paper presents a novel approach to agricultural disease diagnostics through the integration of Deep Learning (DL) techniques with Visual Question Answering (VQA) systems, specifically targeting the detection of wheat rust. Wheat rust is a pervasive and destructive disease that significantly impacts wheat production worldwide. Traditional diagnostic methods often require expert knowledge and time-consuming processes, making rapid and accurate detection challenging. We drafted a new, WheatRustDL2024 dataset (7998 images of healthy and infected leaves) specifically designed for VQA in the context of wheat rust detection and utilized it to retrieve the initial weights on the federated learning server. This dataset comprises high-resolution images of wheat plants, annotated with detailed questions and answers pertaining to the presence, type, and severity of rust infections. Our dataset also contains images collected from various sources and successfully highlights a wide range of conditions (different lighting, obstructions in the image, etc.) in which a wheat image may be taken, therefore making a generalized universally applicable model. The trained model was federated using Flower. Following extensive analysis, the chosen central model was ResNet. Our fine-tuned ResNet achieved an accuracy of 97.69% on the existing data. We also implemented the BLIP (Bootstrapping Language-Image Pre-training) methods that enable the model to understand complex visual and textual inputs, thereby improving the accuracy and relevance of the generated answers. The dual attention mechanism, combined with BLIP techniques, allows the model to simultaneously focus on relevant image regions and pertinent parts of the questions. We also created a custom dataset (WheatRustVQA) with our augmented dataset containing 1800 augmented images and their associated question-answer pairs. The model fetches an answer with an average BLEU score of 0.6235 on our testing partition of the dataset. This federated model is lightweight and can be seamlessly integrated into mobile phones, drones, etc. without any hardware requirement. Our results indicate that integrating deep learning with VQA for agricultural disease diagnostics not only accelerates the detection process but also reduces dependency on human experts, making it a valuable tool for farmers and agricultural professionals. This approach holds promise for broader applications in plant pathology and precision agriculture and can consequently address food security issues.

Use of artificial intelligence in the detection of coffee rust: An exploratory systematic review

Coffee rust, caused by the fungus Hemileia vastatrix, is a fungal disease that affects coffee production and quality, so its early detection is crucial to prevent massive outbreaks and protect production. This article analyzes the most effective factors, the algorithms used, the accuracy of the models, and the challenges in the detection of coffee rust, through an exploratory systematic review of 35 empirical articles obtained from Scopus, IEEE Xplore and SciELO. The review identifies that the most determinant factors for detection include humidity, temperature and the presence of shade. The most commonly used algorithms are Convolutional Neural Networks (CNN), Support Vector Machines (SVM) and Random Forest, highlighting CNN for its ability to process and analyze images with an accuracy of 99.57%, followed by Artificial Neural Networks (ANN) with 98% and SVM with 96%. However, it is concluded that challenges remain such as the need for high quality labeled datasets, variability in environmental conditions and implementation costs. This study provides a comprehensive overview of recent advances and areas for improvement in coffee rust detection, providing information for researchers, practitioners and decision makers in the agricultural sector.

ResLMFFNet: a real-time semantic segmentation network for precision agriculture

Lightweight multiscale-feature-fusion network (LMFFNet), a proficient real-time CNN architecture, adeptly achieves a balance between inference time and accuracy. Capturing the intricate details of precision agriculture target objects in remote sensing images requires deep SEM-B blocks in the LMFFNet model design. However, employing numerous SEM-B units leads to instability during backward gradient flow. This work proposes the novel residual-LMFFNet (ResLMFFNet) model for ensuring smooth gradient flow within SEM-B blocks. By incorporating residual connections, ResLMFFNet achieves improved accuracy without affecting the inference speed and the number of trainable parameters. The results of the experiments demonstrate that this architecture has achieved superior performance compared to other real-time architectures across diverse precision agriculture applications involving UAV and satellite images. Compared to LMFFNet, the ResLMFFNet architecture enhances the Jaccard Index values by 2.1% for tree detection, 1.4% for crop detection, and 11.2% for wheat-yellow rust detection. Achieving these remarkable accuracy levels involves maintaining almost identical inference time and computational complexity as the LMFFNet model. The source code is available on GitHub: https://github.com/iremulku/Semantic-Segmentation-in-Precision-Agriculture.

Tackling unbalanced datasets for yellow and brown rust detection in wheat.

This study evaluates the efficacy of hyperspectral data for detecting yellow and brown rust in wheat, employing machine learning models and the SMOTE (Synthetic Minority Oversampling Technique) augmentation technique to tackle unbalanced datasets. Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Naïve Bayes (GNB) models were assessed. Overall, SVM and RF models showed higher accuracies, particularly when utilizing SMOTE-enhanced datasets. The RF model achieved 70% accuracy in detecting yellow rust without data alteration. Conversely, for brown rust, the SVM model outperformed others, reaching 63% accuracy with SMOTE applied to the training set. This study highlights the potential of spectral data and machine learning (ML) techniques in plant disease detection. It emphasizes the need for further research in data processing methodologies, particularly in exploring the impact of techniques like SMOTE on model performance.

Early Detection of Myrtle Rust on Pōhutukawa Using Indices Derived from Hyperspectral and Thermal Imagery

Myrtle rust is a very damaging disease, caused by the fungus Austropuccinia psidii, which has recently arrived in New Zealand and threatens the iconic tree species pōhutukawa (Metrosideros excelsa). Canopy-level hyperspectral and thermal images were taken repeatedly within a controlled environment, from 49 inoculated (MR treatment) and 26 uninoculated (control treatment) pōhutukawa plants. Measurements were taken prior to inoculation and six times post-inoculation over a 14-day period. Using indices extracted from these data, the objectives were to (i) identify the key thermal and narrow-band hyperspectral indices (NBHIs) associated with the pre-visual and early expression of myrtle rust and (ii) develop a classification model to detect the disease. The number of symptomatic plants increased rapidly from three plants at 3 days after inoculation (DAI) to all 49 MR plants at 8 DAI. NBHIs were most effective for pre-visual and early disease detection from 3 to 6 DAI, while thermal indices were more effective for detection of disease following symptom expression from 7 to 14 DAI. Using results compiled from an independent test dataset, model performance using the best thermal indices and NBHIs was excellent from 3 DAI to 6 DAI (F1 score 0.81–0.85; accuracy 73–80%) and outstanding from 7 to 14 DAI (F1 score 0.92–0.93; accuracy 89–91%).

A Comparative Analysis of Deep Learning Parameters for Enhanced Detection of Yellow Rust in Wheat

A Comparative Analysis of Deep Learning Parameters for Enhanced Detection of Yellow Rust in Wheat

Nanofilm-enhanced electrochemical DNA sensing: a breakthrough for yellow rust detection in wheat

This study showcases the development of a genosensor utilizing a nanoscale NiO thin film. The genosensor is constructed on a glass substrate coated with tin-doped indium oxide (ITO) and is designed for the specific detection of DNA sequences associated with Puccinia striiformis f. sp. tritici (Pst), the causal agent of wheat yellow rust. The detection process relies on the utilization of methylene blue (MB) as an electrochemical indicator, with NiO acting as the matrix and the electrochemical measurement system serving as the transducer. Various single-stranded DNA oligonucleotide sequences related to Pst pathogenesis are employed as probes to enable sensing. The electrochemical response of the nanoscale bioelectrode is characterized and studied using two distinct electrochemical techniques, cyclic voltammetry (CV) and differential pulse voltammetry (DPV), in conjunction with a potentiostat. The detection ranges spans from 40 pg μl−1 to 115 ng μl−1, demonstrating a linear correlation with exceptional precision. The absence of DNA-based biosensors for the detection of Puccinia striiformis f. sp. tritici (Pst) has prompted the need for a new method to address the limitations associated with previously reported technologies. Although surface plasmon resonance (SPR) immunoassays have been reported for Pst detection, the development of DNA-based biosensors specifically tailored for Pst detection remains unexplored. Introducing a novel method aims to overcome the challenges and shortcomings of existing techniques, providing a new approach to detect and combat the devastating effects of Pst on wheat crops. By leveraging the advantages of DNA-based biosensors, such as their sensitive and precise detection capabilities, this new method seeks to enhance the accuracy and efficiency of Pst detection, ultimately contributing to the development of effective strategies for disease management and crop protection. The developed nanoscale electrochemical DNA sensor offers outstanding sensitivity, extended shelf life, and reliable recovery, effectively minimizing the likelihood of obtaining erroneous results. A significant highlight of this study is the first-time utilization of conserved sequences associated with pathogenesis in selected Pst strains for the development of a nanoscale genosensor.

Development of a Model for Detection and Grading of Stem Rust in Wheat Using Deep Learning

Development of a Model for Detection and Grading of Stem Rust in Wheat Using Deep Learning

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties’ response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.

A fast Fourier convolutional deep neural network for accurate and explainable discrimination of wheat yellow rust and nitrogen deficiency from Sentinel-2 time series data.

Accurate and timely detection of plant stress is essential for yield protection, allowing better-targeted intervention strategies. Recent advances in remote sensing and deep learning have shown great potential for rapid non-invasive detection of plant stress in a fully automated and reproducible manner. However, the existing models always face several challenges: 1) computational inefficiency and the misclassifications between the different stresses with similar symptoms; and 2) the poor interpretability of the host-stress interaction. In this work, we propose a novel fast Fourier Convolutional Neural Network (FFDNN) for accurate and explainable detection of two plant stresses with similar symptoms (i.e. Wheat Yellow Rust And Nitrogen Deficiency). Specifically, unlike the existing CNN models, the main components of the proposed model include: 1) a fast Fourier convolutional block, a newly fast Fourier transformation kernel as the basic perception unit, to substitute the traditional convolutional kernel to capture both local and global responses to plant stress in various time-scale and improve computing efficiency with reduced learning parameters in Fourier domain; 2) Capsule Feature Encoder to encapsulate the extracted features into a series of vector features to represent part-to-whole relationship with the hierarchical structure of the host-stress interactions of the specific stress. In addition, in order to alleviate over-fitting, a photochemical vegetation indices-based filter is placed as pre-processing operator to remove the non-photochemical noises from the input Sentinel-2 time series. The proposed model has been evaluated with ground truth data under both controlled and natural conditions. The results demonstrate that the high-level vector features interpret the influence of the host-stress interaction/response and the proposed model achieves competitive advantages in the detection and discrimination of yellow rust and nitrogen deficiency on Sentinel-2 time series in terms of classification accuracy, robustness, and generalization.

Deep learning methods and integrated digital image processing techniques for detecting and evaluating wheat stripe rust disease

In recent years, signal processing and deep learning convergence has sparked transformative synergies across various domains, including image and speech recognition, natural language processing, autonomous systems, and healthcare diagnostics. This fusion capitalizes on the strength of signal processing in extracting meaningful features from raw data and the prowess of deep learning in unraveling intricate patterns, driving innovation and research into uncharted territories. This paper explores literature spanning the past three years to illuminate the dynamic landscape of scholarly endeavors that leverage the integration of signal processing techniques within deep learning architectures. The resulting paradigm shift magnifies the precision and efficiency of applications in computer vision, speech and audio processing, natural language comprehension, and interdisciplinary domains like healthcare. Notable advances include synergizing wavelet transformations with convolutional neural networks (CNNs) for enhanced image classification accuracy, integrating spectrogram-based features with deep learning architectures for improved speech-to-text accuracy, and pioneering the fusion of wavelet packet decomposition into recurrent architectures for sentiment analysis. Moreover, the paper delves into developing and evaluating a U-Net neural network model for image segmentation, investigating its performance under varying training conditions using metrics such as confusion matrices, heat maps, and precision-recall curves. The comprehensive survey identifies research gaps, notably within the context of wheat rust detection, and emphasizes the need for tailored innovations to enhance accuracy and efficiency. Overall, the synthesis of signal processing techniques with deep learning architectures propels innovation, poised to address complex challenges across diverse domains

Robust Coffee Rust Detection Using UAV-Based Aerial RGB Imagery

Timely detection of pests and diseases in crops is essential to mitigate severe damage and economic losses, especially in the context of climate change. This paper describes a method for detecting the presence of coffee leaf rust (CLR) using two databases: RoCoLe and a database obtained from an unmanned aerial vehicle (UAV) equipped with an RGB camera. The developed method follows a two-stage approach. In the first stage, images are processed using ImageJ software, while, in the second phase, Python is used to implement morphological filters and the Hough transform for rust identification. The algorithm’s performance is evaluated using the chi-square test, and its discriminatory capacity is assessed through the generation of a Receiver Operating Characteristic (ROC) curve. Additionally, Cohen’s kappa method is used to assess the agreement among observers, while Kendall’s rank correlation coefficient (KRCC) measures the correlation between the criteria of the observers and the classifications generated by the method. The results demonstrate that the developed method achieved an efficiency of 97% in detecting coffee rust in the RoCoLe dataset and over 93.5% in UAV images. These findings suggest that the developed method has the potential to be implemented in the future on a UAV for rust detection.

Previsual and Early Detection of Myrtle Rust on Rose Apple Using Indices Derived from Thermal Imagery and Visible-to-Short-Infrared Spectroscopy.

Myrtle rust, caused by the fungus Austropuccinia psidii, is a serious disease, which affects many Myrtaceae species. Commercial nurseries that propagate Myrtaceae species are prone to myrtle rust and require a reliable method that allows previsual and early detection of the disease. This study uses time-series thermal imagery and visible-to-short-infrared spectroscopy measurements acquired over 10 days from 81 rose apple plants (Syzygium jambos) that were either inoculated with myrtle rust or maintained disease-free. Using these data, the objectives were to (i)quantify the accuracy of models using thermal indices and narrowband hyperspectral indices (NBHI) for previsual and early detection of myrtle rust using data from older resistant green leaves and young susceptible red leaves and (ii) identify the most important NBHI and thermal indices for disease detection. Using predictions made on a validation dataset, models using indices derived from thermal imagery were able to perfectly (F1 score = 1.0; accuracy = 100%) distinguish control from infected plants previsually one day before symptoms appeared (1 DBS) and for all stages after early symptoms appeared. Compared with control plants, plants with myrtle rust had lower and more variable normalized canopy temperature, which was associated with higher stomatal conductance and transpiration. Using NBHI derived from green leaves, excellent previsual classification was achieved 3 DBS, 2 DBS, and 1 DBS (F1 score range = 0.89 to 0.94). The accurate characterization of myrtle rust during previsual and early stages of disease development suggests that a robust detection methodology could be developed within a nursery setting. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Precise in-field molecular diagnostics of crop diseases by smartphone-based mutation-resolved pathogenic RNA analysis

Molecular diagnostics for crop diseases can guide the precise application of pesticides, thereby reducing pesticide usage while improving crop yield, but tools are lacking. Here, we report an in-field molecular diagnostic tool that uses a cheap colorimetric paper and a smartphone, allowing multiplexed, low-cost, rapid detection of crop pathogens. Rapid nucleic acid amplification-free detection of pathogenic RNA is achieved by combining toehold-mediated strand displacement with a metal ion-mediated urease catalysis reaction. We demonstrate multiplexed detection of six wheat pathogenic fungi and an early detection of wheat stripe rust. When coupled with a microneedle for rapid nucleic acid extraction and a smartphone app for results analysis, the sample-to-result test can be completed in ~10 min in the field. Importantly, by detecting fungal RNA and mutations, the approach allows to distinguish viable and dead pathogens and to sensitively identify mutation-carrying fungicide-resistant isolates, providing fundamental information for precision crop disease management.

Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina

Early crop disease detection is one of the most important tasks in plant protection. The purpose of this work was to evaluate the early wheat leaf rust detection possibility using hyperspectral remote sensing. The first task of the study was to choose tools for processing and analyze hyperspectral remote sensing data. The second task was to analyze the wheat leaf biochemical profile by chromatographic and spectrophotometric methods. The third task was to discuss a possible relationship between hyperspectral remote sensing data and the results from the wheat leaves, biochemical profile analysis. The work used an interdisciplinary approach, including hyperspectral remote sensing and data processing methods, as well as spectrophotometric and chromatographic methods. As a result, (1) the VIS-NIR spectrometry data analysis showed a high correlation with the hyperspectral remote sensing data; (2) the most important wavebands for disease identification were revealed (502, 466, 598, 718, 534, 766, 694, 650, 866, 602, 858 nm). An early disease detection accuracy of 97–100% was achieved from fourth dai (day/s after inoculation) using SVM.

The First Study of White Rust Disease Recognition by Using Deep Neural Networks and Raspberry Pi Module Application in Chrysanthemum

Growth factors affect farm owners, environmental conditions, nutrient adaptation, and resistance to chrysanthemum diseases. Healthy chrysanthemum plants can overcome all these factors and provide farms owners with a lot of income. Chrysanthemum white rust disease is a common disease that occurs worldwide; if not treated promptly, the disease spreads to the entire leaf surface, causing the plant’s leaves to burn, turn yellow, and fall prematurely, reducing the photosynthetic performance of the plant and the appearance of the flower branches. In Korea, chrysanthemum white rust disease most often occurs during the spring and autumn seasons, when temperature varies during the summer monsoon, and when ventilation is poor in the winter. Deep neural networks were used to determine healthy and unhealthy plants. We applied the Raspberry Pi 3 module to recognize white rust and test four neural network models. The five main deep neural network processes utilized for a dataset of non-diseased and white rust leaves include: (1) data collection; (2) data partitioning; (3) feature extraction; (4) feature engineering; and (5) prediction modeling based on the train–test loss of 35 epochs within 20 min using Linux. White rust recognition is performed for comparison using four models, namely, DenseNet121, ResNet50, VGG-19, and MobileNet v2. The qualitative white rust detection system is achieved using a Raspberry Pi 3 module. All models accomplished an accuracy of over 94%, and MobileNet v2 achieved the highest accuracy, precision, and recall at over 98%. In the precision comparison, DenseNet121 obtained the second highest recognition accuracy of 97%, whereas ResNet50 and VGG-19 achieved slightly lower accuracies at 95% and 94%, respectively. Qualitative results were obtained using the Raspberry Pi 3 module to assess the performance of the four models. All models had accuracies of over 91%, with ResNet50 obtaining a value of 91%, VGG19 reaching a value of 93%, and DenseNet121 reaching 95%. The highest accuracy rate was 97% (MobileNet v2). MobileNet v2 was validated as the most effective model to recognize white rust in chrysanthemums using the Raspberry Pi 3 system. Raspberry Pi 3 module was considered, in conjunction with the MobileNet v2 model, to be the best application system. MobileNet v2 and Raspberry Pi require a low cost for the recognition of chrysanthemum white rust and the diagnosis of chrysanthemum plant health conditions, reducing the risk of white rust disease and minimizing costs and efforts while improving floral production. Chrysanthemum farmers should consider applying the Raspberry Pi module for detecting white rust, protecting healthy plant growth, and increasing yields with low-cost.

A new anti-vibration hammer rust detection algorithm based on improved YOLOv7

Anti-vibration hammer corrosion seriously endangers the safe operation of transmission lines. To better improve the detection effect of anti-vibration hammer corrosion on transmission lines under complex background, in this paper we propose an anti-vibration hammer corrosion detection algorithm based on improved YOLOv7. Firstly, a dataset was established for anti-vibration hammer corrosion detection. After that, a color space transformation was carried out through an HSV color model to highlight the corrosion features. BiFPN multi-scale feature fusion was then introduced to fully utilize the feature maps at different scales, so as to get a weighted feature fusion of weights. Finally, a GSConv module was added to the YOLOv7 network to avoid spatial information loss in the transmission process as far as possible. It can accelerate the inference speed and increase the model accuracy as well. The test results show that the improved algorithm improves the detection accuracy by 1.6% and inference speed by 17% on average compared to the original network.

Field ready: Development of a rapid LAMP‐based colorimetric assay for the causal agent of white pine blister rust, Cronartium ribicola

AbstractThe invasive fungal pathogen Cronartium ribicola causes white pine blister rust which is considered one of the most destructive diseases of five‐needle (white) pines in North America. The disease has a life cycle that requires two hosts: white pines and Ribes spp., although other non‐Ribes species, including Castilleja and Pedicularis, have been demonstrated as alternate hosts as well. Detection of this disease can be difficult because of the ephemeral nature of sporulation on pine hosts with ambiguity in other symptoms, and the alternate hosts for C. ribicola can also be an alternate host for other pine rust species. We used the previously published C. ribicola genome and species‐specific real‐time PCR assay to develop a field‐ready loop‐mediated isothermal amplification (LAMP) specific colorimetric assay for this pathogen. Specificity results across regionally identified pine rust pathogens showed the assay is highly specific to C. ribicola and can detect as little as 40 pg of pathogen DNA. We also developed a simple DNA extraction method that works with several tissue types (bark/phloem, aeciospores, and urediniospores/telia) to prepare the DNA samples for the LAMP assay. The DNA extraction and LAMP assay take ~70 min to complete and require a relatively small investment in equipment. This tool enables quick and efficient detection of white pine blister rust.

Non-invasive rust detection of steel plates determined through interfacial modulus

Initial methods to detect rust in pipelines have been conducted through invasive probes and sectioning off parts of the facility as the plant is running. These methods greatly increase the costs overall. The need for a feasible solution to this issue lies in the detection of rust formation through a non-invasive method. This study’s objective is to measure rust formation through droplet motion on the outer layer of pipelines. Multiple experiments are conducted using carbon steel sheets whose bottom layer has been exposed to acid for different durations of time. As rust formation in the metal is a voltaic phenomenon, it would mean that the acid corrosion of the bottom layer would adversely affect the top layer of the substrate. Consequentially, droplet motion and the droplet’s contour would change in different corrosive scenarios which we could then detect with novel parameters in our lab. One such parameter is the Interfacial Modulus (GS), which describes the initial resistance of the solid’s outer layer towards the liquid. We can understand this parameter with the aid of the novel device, known as the Centrifugal Adhesion Balance (CAB). As we cause the drop to slide across the substrate at constant normal force condition, we observe the difference in the contour of the drop as it slides across the substrate. The real-time change in contact angles at each edge of the drop, along with its change in external lateral force, causes a change in the GS values, which varies in different corrosive scenarios.