• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.51-62
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• 2021

Plant disease is one of the issues that can create losses in the production and economy of the agricultural sector. Early detection of this disease for finding solutions and treatments is still a challenge in the sustainable agriculture field. Currently, image processing techniques and machine learning methods have been applied to detect plant diseases successfully. However, the effectiveness of these methods still needs to be improved, especially in multiclass plant diseases classification. In this paper, a convolutional neural network with a batch normalization-based deep learning approach for classifying plant diseases is used to develop an automatic diagnostic assistance system for leaf diseases. The significance of using deep learning technology is to make the system be end-to-end, automatic, accurate, less expensive, and more convenient to detect plant diseases from their leaves. For evaluating the proposed model, an experiment is conducted on a public dataset contains 20654 images with 15 plant diseases. The experimental validation results on 20% of the dataset showed that the model is able to classify the 15 plant diseases labels with 96.4% testing accuracy and 0.168 testing loss. These results confirmed the applicability and effectiveness of the proposed model for the plant disease detection task.

• The Plant Pathology Journal
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• 제39권4호
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• pp.319-334
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• 2023

Plant disease is an important factor affecting crop yield. With various types and complex conditions, plant diseases cause serious economic losses, as well as modern agriculture constraints. Hence, rapid, accurate, and early identification of crop diseases is of great significance. Recent developments in deep learning, especially convolutional neural network (CNN), have shown impressive performance in plant disease classification. However, most of the existing datasets for plant disease classification are a single background environment rather than a real field environment. In addition, the classification can only obtain the category of a single disease and fail to obtain the location of multiple different diseases, which limits the practical application. Therefore, the object detection method based on CNN can overcome these shortcomings and has broad application prospects. In this study, an annotated apple leaf disease dataset in a real field environment was first constructed to compensate for the lack of existing datasets. Moreover, the Faster R-CNN and YOLOv3 architectures were trained to detect apple leaf diseases in our dataset. Finally, comparative experiments were conducted and a variety of evaluation indicators were analyzed. The experimental results demonstrate that deep learning algorithms represented by YOLOv3 and Faster R-CNN are feasible for plant disease detection and have their own strong points and weaknesses.

• Journal of Forest and Environmental Science
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• 제40권1호
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• pp.1-8
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• 2024

Recently, intensive research has been conducted to develop innovative methods for diagnosing plant diseases based on hyperspectral technologies. Hyperspectral analysis is a new subject that combines optical spectroscopy and image analysis methods, which makes it possible to simultaneously evaluate both physiological and morphological parameters. Among the physiological and morphological parameters are classifying healthy and diseased plants, assessing the severity of the disease, differentiating the types of pathogens, and identifying the symptoms of biotic stresses at early stages, including during the incubation period, when the symptoms are not visible to the human eye. Plant diseases cause significant economic losses in agriculture around the world as the symptoms of diseases usually appear when the plants are infected severely. Early detection, quantification, and identification of plant diseases are crucial for the targeted application of plant protection measures in crop production. Hence, this can be done by possible applications of hyperspectral sensors and platforms on different scales for disease diagnosis. Further, the main areas of application of hyperspectral sensors in the diagnosis of plant diseases are considered, such as detection, differentiation, and identification of diseases, estimation of disease severity, and phenotyping of disease resistance of genotypes. This review provides a deeper understanding, of basic principles and implementation of hyperspectral sensors that can measure pathogen-induced changes in plant physiology. Hence, it brings together critically assessed reports and evaluations of researchers who have adopted the use of this application. This review concluded with an overview that hyperspectral sensors, as a non-invasive system of measurement can be adopted in early detection, identification, and possible solutions to farmers as it would empower prior intervention to help moderate against decrease in yield and/or total crop loss.

• The Plant Pathology Journal
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• 제25권4호
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• pp.328-332
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• 2009

Diseases caused by Pepper yellow leaf curl virus infection is considered to be emerging plant diseases in Indonesia in the last five years. One key factor for disease management is the availability of accurate detection of the virus in plants. Polyclonal antibody for Pepper yellow leaf curl Indonesia virus-Bogor (PYLCIV-Bgr) was produced for detection of the virus using I-ELISA and DIBA methods. The antibody was able to detect PYLCIV-Bgr from infected plants up to dilution 1/16,384 and cross reaction was not observed with Cucumber mosaic virus (CMV), Tobacco mosaic virus (TMV), and Chilli veinal mottle virus (ChiVMV). Positive reaction was readily detected in membrane containing Begomovirus samples from Yogyakarta (Kaliurang and Kulonprogo) and West Java (Bogor and Segunung). Infection of PYLCIV-Bgr in chillipepper, tomato, and Ageratum conyzoides was also confirmed using polyclonal antibody for PYLCIV-Bgr in DIBA. Polyclonal antibody for PYLCIV-Bgr is suggested to be included in disease management approach due to its good detection level.

• Journal of Microbiology and Biotechnology
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• 제20권1호
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• pp.193-201
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• 2010

A specific and rapid real-time PCR assay for detecting Ralstonia solanacearum in horticultural soil and plant tissues was developed in this study. The specific primers RSF/RSR were designed based on the upstream region of the UDP-3-O-acyl-GlcNAc deacetylase gene from R. solanacearum, and a PCR product of 159 bp was amplified specifically from 28 strains of R. solanacearum, which represent all genetically diverse AluI types and all 6 biovars, but not from any other nontarget species. The detection limit of $10^2\;CFU/g$ tomato stem and horticultural soil was achieved in this real-time PCR assay. The high sensitivity and specificity observed with field samples as well as with artificially infected samples suggested that this method might be a useful tool for detection and quantification of R. solanacearum in precise forecast and diagnosis.

• The Plant Pathology Journal
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• 제36권3호
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• pp.204-217
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• 2020

In nature, plants are always under the threat of pests and diseases. Pathogenic bacteria are one of the major pathogen types to cause diseases in diverse plants, resulting in negative effects on plant growth and crop yield. Chemical bactericides and antibiotics have been used as major approaches for controlling bacterial plant diseases in the field or greenhouse. However, the appearance of resistant bacteria to common antibiotics and bactericides as well as their potential negative effects on environment and human health demands bacteriologists to develop alternative control agents. Bacteriophages, the viruses that can infect and kill only target bacteria very specifically, have been demonstrated as potential agents, which may have no negative effects on environment and human health. Many bacteriophages have been isolated against diverse plant-pathogenic bacteria, and many studies have shown to efficiently manage the disease development in both controlled and open conditions such as greenhouse and field. Moreover, the specificity of bacteriophages to certain bacterial species has been applied to develop detection tools for the diagnosis of plant-pathogenic bacteria. In this paper, we summarize the promising results from greenhouse or field experiments with bacteriophages to manage diseases caused by plant-pathogenic bacteria. In addition, we summarize the usage of bacteriophages for the specific detection of plant-pathogenic bacteria.

• 식물병연구
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• 제20권3호
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• pp.173-181
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• 2014

The early and accurate detection of plant viruses is an essential component to control those. Because the globalization of trade by free trade agreement (FTA) and the rapid climate change promote the country-to-country transfer of viruses and their hosts and vectors, diagnosis of viral diseases is getting more important. Because symptoms of viral diseases are not distinct with great variety and are confused with those of abiotic stresses, symptomatic diagnosis may not be appropriate. From the last three decades, enzyme-linked immunosorbent assays (ELISAs), developed based on serological principle, have been widely used. However, ELISAs to detect plant viruses decrease due to some limitations such as availability of antibody for target virus, cost to produce antibody, requirement of large volume of sample, and time to complete ELISAs. Many advanced techniques allow overcoming demerits of ELISAs. Since the polymerase chain reaction (PCR) developed as a technique to amplify target DNA, PCR evolved to many variants with greater sensitivity than ELISAs. Many systems of plant virus detection are reviewed here, which includes immunological-based detection system, PCR techniques, and hybridization-based methods such as microarray. Some of techniques have been used in practical, while some are still under developing to get the level of confidence for actual use.

• 대한수의학회지
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• 제53권1호
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• pp.25-28
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• 2013

The aim of this study was to applicate and evaluate a SYBR Green real-time PCR for the specific detection of Salmonella spp. Specificity of the PCR method was confirmed with 48 Salmonella spp. and 5 non-Salmonella strains using invA gene primer. The average threshold cycle ($C_T$) of Salmonella spp. was $11.83{\pm}0.78$ while non-Salmonella spp. was $30.86{\pm}1.19$. Correlation coefficients of standard curves constructed using $C_T$ versus copy number of Salmonella Enteritidis ATCC 13076 showed good linearity ($R^2=0.993$; slope = 3.563). Minimum level of detection with the method was > $10^2$ colony forming units (CFU)/mL. These results suggested that the SYBR Green real-time PCR might be applicable for the specific detection of Salmonella spp. isolates.

• Journal of Information Processing Systems
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• 제17권6호
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• pp.1115-1126
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• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• The Plant Pathology Journal
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• 제35권2호
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• pp.172-177
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• 2019

A duplex PCR method was developed for simultaneous detection and identification of tobacco root rot pathogens Phytophthora nicotianae and Thielaviopsis basicola. The specific primers for P. nicotianae were developed based on its internal transcribed spacer (ITS) regions of ribosomal gene, ras gene and hgd gene, while the specific primers for T. basicola were designed based on its ITS regions and ${\beta}$-tubulin gene. The specificity of the primers was determined using isolates of P. nicotianae, T. basicola and control samples. The results showed that the target pathogens could be detected from diseased tobacco plants by a combination of the specific primers. The sensitivity limitation was $100fg/{\mu}l$ of pure genomic DNA of the pathogens. This new assay can be applied to screen out target pathogens rapidly and reliably in one PCR and will be an important tool for the identification and precise early prediction of these two destructive diseases of tobacco.