• Title/Summary/Keyword: plant classification learning

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A Case Study on the Application of Plant Classification Learning for 4th Grade Elementary School Using Machine Learning in Online Learning (온라인 학습에서 머신러닝을 활용한 초등 4학년 식물 분류 학습의 적용 사례 연구)

  • Shin, Won-Sub;Shin, Dong-Hoon
    • Journal of Korean Elementary Science Education
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    • v.40 no.1
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    • pp.66-80
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    • 2021
  • This study is a case study that applies plant classification learning using machine learning to fourth graders in elementary school in online learning situations. In this study, a plant classification learning education program associated with 2015 revision science curriculum was developed by applying the Artificial Intelligence biological classification teaching Learning model. The study participants were 31 fourth graders who agreed to participate voluntarily. Plant classification learning using machine learning was applied six hours for three weeks. The results of this study are as follows. First, as a result of image analysis on artificial intelligence, participants were mainly aware of artificial intelligence as mechanical (27%), human (23%) and household goods (23%). Second, an artificial intelligence recognition survey by semantic discrimination found that artificial intelligence was recognized as smart, good, accurate, new, interesting, necessary, and diverse. Third, there was a difference between men and women in perception and emotion of artificial intelligence, and there was no difference in perception of the ability of artificial intelligence. Fourth, plant classification learning using machine learning in this study influenced changes in artificial intelligence perception. Fifth, plant classification learning using machine learning in this study had a positive effect on reasoning ability.

Classification Method of Plant Leaf using DenseNet (DenseNet을 활용한 식물 잎 분류 방안 연구)

  • Park, Young Min;Gang, Su Myung;Chae, Ji Hun;Lee, Joon Jae
    • Journal of Korea Multimedia Society
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    • v.21 no.5
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    • pp.571-582
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    • 2018
  • Recently, development of deep learning has shown better image classification result than human. According to recent research, a hidden layer of deep learning is deeper, and a preservation of extracted features shows good results. However, in the case of general images, the extracted features are clear and easy to sort. This study aims to classify plant leaf images. This plant leaf image has high similarity in each image. Since plant leaf images have high similarity not only between images of different species but also within the same species, classification accuracy is not increased by simply extending the hidden layer or connecting the layers. Therefore, in this paper, we tried to improve the hidden layer of the algorithm called DenseNet which shows the recent excellent classification results, and compare the results of several different modified layers. The proposed method makes it possible to classify plant leaf images collected in a natural environment more easily and accurately than conventional methods. This results in good classification of plant leaf image data including unnecessary noise obtained in a natural environment.

Deep Learning-based system for plant disease detection and classification (딥러닝 기반 작물 질병 탐지 및 분류 시스템)

  • YuJin Ko;HyunJun Lee;HeeJa Jeong;Li Yu;NamHo Kim
    • Smart Media Journal
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    • v.12 no.7
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    • pp.9-17
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    • 2023
  • Plant diseases and pests affect the growth of various plants, so it is very important to identify pests at an early stage. Although many machine learning (ML) models have already been used for the inspection and classification of plant pests, advances in deep learning (DL), a subset of machine learning, have led to many advances in this field of research. In this study, disease and pest inspection of abnormal crops and maturity classification were performed for normal crops using YOLOX detector and MobileNet classifier. Through this method, various plant pest features can be effectively extracted. For the experiment, image datasets of various resolutions related to strawberries, peppers, and tomatoes were prepared and used for plant pest classification. According to the experimental results, it was confirmed that the average test accuracy was 84% and the maturity classification accuracy was 83.91% in images with complex background conditions. This model was able to effectively detect 6 diseases of 3 plants and classify the maturity of each plant in natural conditions.

An Analysis of Plant Diseases Identification Based on Deep Learning Methods

  • Xulu Gong;Shujuan Zhang
    • 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.

An Study on Plant Classification System as Described in the Science Textbook of Elementary and Secondary School (초.중등학교 과학교과서(생물영역)의 식물 분류 체계에 관한 연구)

  • Yeau, Sung-Hee
    • Journal of The Korean Association For Science Education
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    • v.18 no.4
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    • pp.635-641
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    • 1998
  • The purpose of this study was to suggest a new direction of learning method in botany based on the analysis of a classification system and concepts in Science(Biology) textbooks of elementary and secondary school. Elementary and secondary school textbooks of Biology have been analyzed for plant classification system and concepts. Findings are summarized as belows. 1. In textbook of elementary school, the organization of life is grouped into Plantae and Animalia. Learning contents of plant are divided by the size and habitat. However, this system of classification might cause false concepts. Therefore, learning contents should be organized as whether they are flowering plants or not. 2. In a textbook of middle school, the organization of life is grouped into Plantae and Animalia. For a textbook of high school, it is grouped into three kingdoms; Plantae, Animalia and Prorista. With the idea of new age of Life Science, we should change the standards to 5 kingdoms; Plantae, Animalia, Proristae, Fungi and Monera. Moreover, it would be desirable if the concept of plant classification could be explained with a general outline, not by an individual interpretation focusing on characters of species only. In addition to the above indications, a learning course should provide present a standard classification according to a cognitive developemental level. It also has to teach students how to classify plant, in secondary school. Learning materials focusing on algae of the present system, but should be organized based on Seed plants.

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Trends of Plant Image Processing Technology (이미지 기반의 식물 인식 기술 동향)

  • Yoon, Y.C.;Sang, J.H.;Park, S.M.
    • Electronics and Telecommunications Trends
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    • v.33 no.4
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    • pp.54-60
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    • 2018
  • In this paper, we analyze the trends of deep-learning based plant data processing technologies. In recent years, the deep-learning technology has been widely applied to various AI tasks, such as vision (image classification, image segmentation, and so on) and natural language processing because it shows a higher performance on such tasks. The deep-leaning method is also applied to plant data processing tasks and shows a significant performance. We analyze and show how the deep-learning method is applied to plant data processing tasks and related industries.

Superpixel-based Apple Leaf Disease Classification using Convolutional Neural Network (합성곱 신경망을 이용하는 수퍼픽셀 기반 사과잎 병충해의 분류)

  • Kim, Manbae;Choi, Changyeol
    • Journal of Broadcast Engineering
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    • v.25 no.2
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    • pp.208-217
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    • 2020
  • The classification of plant diseases by images captured by a camera sensor has been studied over past decades. A method that has gained much interest is to use image segmentation, from which statistical features are derived and analyzed by machine learning. Recently, deep learning has been adopted in this area. However, image segmentation is still a difficult task to achieve stable performance due to a variety of environmental variations. The end-to-end learning in neural network has a demerit that train images may be different from real images acquired in outdoor fields. To solve these problems, we propose superpixel-based disease classification method using end-to-end CNN (convolutional neural network) learning. Based on experiments performed on PlantVillage apple images, the classification accuracy is 98.29% and 92.43% for full-image and superpixel. As well, the multivariate F1-score is (0.98, 0.93). Therefore we validate that the method of using superpixel is comparable to that of full-image.

Performance Comparison of Base CNN Models in Transfer Learning for Crop Diseases Classification (농작물 질병분류를 위한 전이학습에 사용되는 기초 합성곱신경망 모델간 성능 비교)

  • Yoon, Hyoup-Sang;Jeong, Seok-Bong
    • 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.

A Deep Convolutional Neural Network with Batch Normalization Approach for Plant Disease Detection

  • Albogamy, Fahad R.
    • International Journal of Computer Science & Network Security
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    • v.21 no.9
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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.