• Journal of Korea Multimedia Society
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• v.25 no.11
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• pp.1653-1671
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• 2022

Crop diseases affect crop production, more than 30 billion USD globally. We proposed a classification study of crop species and diseases using deep learning algorithms for corn, cucumber, pepper, and strawberry. Our study has three steps of species classification, disease detection, and disease classification, which is noteworthy for using captured images without additional processes. We designed deep learning approach of deep learning convolutional neural networks based on Mask R-CNN model to classify crop species. Inception and Resnet models were presented for disease detection and classification sequentially. For classification, we trained Mask R-CNN network and achieved loss value of 0.72 for crop species classification and segmentation. For disease detection, InceptionV3 and ResNet101-V2 models were trained for nodes of crop species on 1,500 images of normal and diseased labels, resulting in the accuracies of 0.984, 0.969, 0.956, and 0.962 for corn, cucumber, pepper, and strawberry by InceptionV3 model with higher accuracy and AUC. For disease classification, InceptionV3 and ResNet 101-V2 models were trained for nodes of crop species on 1,500 images of diseased label, resulting in the accuracies of 0.995 and 0.992 for corn and cucumber by ResNet101 with higher accuracy and AUC whereas 0.940 and 0.988 for pepper and strawberry by Inception.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1250-1257
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• 2020

The early detection of diseases is important in agriculture because diseases are major threats of reducing crop yield for farmers. The shape and color of plant leaf are changed differently according to the disease. So we can detect and estimate the disease by inspecting the visual feature in leaf. This study presents a vision-based leaf classification method for detecting the diseases of tomato crop. ResNet-50 model was used to extract the visual feature in leaf and classify the disease of tomato crop, since the model showed the higher accuracy than the other ResNet models with different depths. We propose a new ensemble approach using several DCNN classifiers that have the same structure but have been trained at different ranges in the DCNN layers. Experimental result achieved accuracy of 97.19% for PlantVillage dataset. It validates that the proposed method effectively classify the disease of tomato crop.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.1-7
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• 2020

Early detection and classification of crop diseases play significant role to help farmers to reduce disease spread and to increase agricultural productivity. Recently, many researchers have used deep learning techniques like convolutional neural network (CNN) classifier for crop disease inspection with dataset of crop leaf images (e.g., PlantVillage dataset). These researches present over 90% of classification accuracy for crop diseases, but they have ability to detect only the pre-trained diseases. This paper proposes an efficient disease inspection CNN model for new crops not used in the pre-trained model. First, we present a benchmark crop disease classifier (CDC) for the crops in PlantVillage dataset using VGG16. Then we build a modified crop disease classifier (mCDC) to inspect diseases for untrained crops. The performance evaluation results show that the proposed model outperforms the benchmark classifier.

• The Plant Pathology Journal
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• v.39 no.4
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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.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.1
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• pp.53-56
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• 2001

Barley Yellow Dwarf Virus (BYDV), an aphid-borne luteovirus, is a major plant pathogenic disease causing a huge economic loss in the grain production of a wide range of Gramineae species throughout the world. It has been recently reported that BYDV also occurred frequently in wheat field of Korea. Here, we performed to develop the detection and classification methods of BYDV strains that were accomplished by reverse transcription-polymerase chain reaction (RT-PCR). Since there are high variations among BYDV strains, three pairs of primers were designed to detect BYDV strains such as PAV (Vic-PAV and CN-PAV) and MAV (primer A) simultaneously, specifically Vic-PAV(primer B), and MAV (primer C) based on the genomic RNA sequences of BYDV strains previously published. The validity of the primers was confirmed using several BYDV strains obtained from CIMMYT. Though three BYDV strains were able to be detected using primer A, PCR products were not distinguished between two PAV strains. It was possible to separate them with a restriction enzyme, EcoRI, whose restriction site was present in the amplified DNA fragment from Vic-PAV, but not from CN-PAV.

• Research in Plant Disease
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• v.17 no.3
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• pp.265-271
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• 2011

Seven phytoplasma diseases have been occurred on floricultural crops in Korea : Ph-ch1 and Ph-ch2 of chrysanthemum, Ph-lily of lily, petunia flat stem-Korean (PFS-K) of petunia, poinsettia branch inducing- Korean (PoiBI-K) of poinsettia, statis witches' broom-Korean (SWB-K) of statis and azalea witches broom (AWB). Classification of the seven phytoplasmal diseases based on 16S ribosomal RNA (rRNA) sequences showed that floricultural crop phytoplasma disease were widespread in order of aster yellow (AY), stolbur and X-disease in Korea. In phenotypic characters, the fasciation was occurred in both monocotyledon plant of lily and dicotyledon plants of petunia and poinsettia. Besides, the fascination was occurred in Ph-lily of stolbur, petunia PFS-K of AY and PoiBI-K of X-disease. This result indicated that phytoplasma classification based on 16S rRNA and symptoms are not consistently related. The comparison of 16S rRNA sequence of the seven floricultural crop phytoplasma with five tree phytoplasmal diseases of jujube witches' broom, paulownia witches' broom, wild jujube witches' broom, mulberry dwarf, golden rain phytoplasma occurred in Korea showed as high as 88.5-99.9% homology. Among them, especially mulberry dwarf showed the highest homology with the seven floricultural crop phytoplasms. Based on this result, floricultural crop phytoplasmas were assumed to be transmitted by insect vectors from tree phytoplasmas in Korea.

• Journal of Forest and Environmental Science
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• v.40 no.1
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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.

• Korean Journal of Agricultural Science
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• v.49 no.1
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• pp.121-128
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• 2022

Fusarium wilt disease caused by Fusarium species is a major problem affecting cultivated cucurbit plants worldwide. Fusarium species are well-known soil-borne pathogenic fungi that cause Fusarium wilt disease in several cucurbit plants. In this study, we aimed to identify and classify pathogenic Fusarium species from cultivated Korean cucurbit plants, specifically watermelon and cucumber. Thirty-six Fusarium isolates from different regions of Korea were obtained from the National Institute of Horticulture and Herbal Science Germplasm collection. Each isolate was morphologically and molecularly identified using an internal transcribed spacer of ribosomal DNA, elongation factor-1α, and the beta-tubulin gene marker sequence. Fusarium species that infect the cucurbit plant family could be divided into three groups: Fusarium oxysporum (F. oxysporum), Fusarium solani (F. solani), and Fusarium equiseti (F. equieti). Among the 36 isolates examined, six were non-pathogenic (F. equiseti: 15-127, F. oxysporum: 14-129, 17-557, 17-559, 18-369, F. solani: 12-155), whereas 30 isolates were pathogenic. Five of the F. solani isolates (11-117, 14-130, 17-554, 17-555, 17-556) were found to be highly pathogenic to both watermelon and cucumber plants, posing a great threat to cucurbit production in Korea. The identification of several isolates of F. equiseti and F. oxysporum, which are both highly pathogenic to bottle gourd, may indicate waning resistance to Fusarium species infection.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.30-43
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• 2018

Many studies have been carried out to retrieve images using colors, shapes, and textures which are characteristic of images. In addition, there is also progress in research related to the disease images of the crop. In this paper, to be a help to identify the disease occurred in crops grown in the agricultural field, we propose a similarity-based crop disease search system using the diseases image of horticulture crops. The proposed system improves the similarity retrieval performance compared to existing ones through the combination descriptor without using a single descriptor and applied the weight based calculation method to provide users with highly readable similarity search results. In this paper, a total of 13 Descriptors were used in combination. We used to retrieval of disease of six crops using a combination Descriptor, and a combination Descriptor with the highest average accuracy for each crop was selected as a combination Descriptor for the crop. The retrieved result were expressed as a percentage using the calculation method based on the ratio of disease names, and calculation method based on the weight. The calculation method based on the ratio of disease name has a problem in that number of images used in the query image and similarity search was output in a first order. To solve this problem, we used a calculation method based on weight. We applied the test image of each disease name to each of the two calculation methods to measure the classification performance of the retrieval results. We compared averages of retrieval performance for two calculation method for each crop. In cases of red pepper and apple, the performance of the calculation method based on the ratio of disease names was about 11.89% on average higher than that of the calculation method based on weight, respectively. In cases of chrysanthemum, strawberry, pear, and grape, the performance of the calculation method based on the weight was about 20.34% on average higher than that of the calculation method based on the ratio of disease names, respectively. In addition, the system proposed in this paper, UI/UX was configured conveniently via the feedback of actual users. Each system screen has a title and a description of the screen at the top, and was configured to display a user to conveniently view the information on the disease. The information of the disease searched based on the calculation method proposed above displays images and disease names of similar diseases. The system's environment is implemented for use with a web browser based on a pc environment and a web browser based on a mobile device environment.