Classification of different degrees of ginkgo leaf disease based on deep learning

Li, Kaizhou; Lin, Jianhui; Liu, Jinrong; Zhao, Yandong; Dou, Suhan; Zhang, Yadong; Liu, Xuezeng

doi:10.1109/icsai48974.2019.9010110

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Deep learning approaches have recently expanded their applicability in plant disease identification, providing a comprehensive instrument with extremely accurate findings. Since Convolutional Neural Networks (CNNs) have achieved outstanding results, deep CNN models are used to categorize and analyze diseases in plants from the leaves [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis on Identifying Plant, Crop Diseases Using Machine Learning and Deep Learning

Raghavendran

Rao

Basha³

et al. 2023

Advances in Transdisciplinary Engineering

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This paper is intended to explore the research done on identifying the diseased plants and crops using Machine Learning (ML) and Deep Learning (DL) techniques during last 10 years using bibliometric methods. In this study, we used Scopus database to analyze on “Plant disease” or “Crop disease” using “Machine Learning” or “Deep Learning” or “Neural Networks”. This paper focuses on the importance of ML and DL techniques in identifying plant or crop diseases. The database collected from the Scopus is analyzed using VOSviewer software of version 1.6.16. The study is limited to publications from conferences, journals with subject areas are limited to Computer Science, Engineering and languages limited to English and Chinese. Scopus search outputs 824 articles on Plant or Crop diseases with ML, DL and Neural Networks covering conference papers and journal articles. Statistics showed that more articles were published during the last five years and major contributions were from India. By analyzing database on Authors, Subject area, Keywords, Affiliation, Source type it is evident that there is plenty of research scope in this area. Network analysis on diverse parameters specifies that there is a good scope to do research in this topic using advanced deep learning techniques.

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Section: Introductionmentioning

confidence: 99%

Bibliometric Analysis on Identifying Plant, Crop Diseases Using Machine Learning and Deep Learning

Raghavendran

Rao

Basha³

et al. 2023

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

“…Recognition of diseases at proper intervals of time is also beneficial for disease control. Agricultural experts require high professional knowledge to visualize diseases to take remedial actions [16,17]. Various image processing and computer vision techniques were employed for plant leaf disease diagnosis problems [18,19].…”

Section: Introductionmentioning

confidence: 99%

Classification of Image-Based Wheat Leaf Diseases using Deep Learning Approach: A Survey

Sharma,

Sethi

2023

J. Sci. Res.

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This paper focuses on detecting leaf diseases in wheat plants from the beginning to the end of the plant's life cycle. It highlights the best techniques for detecting various types of wheat leaf diseases and emphasizes the use of computer vision, image processing, and machine learning. The main focus is on classifying these diseases through deep convolutional neural networks, a popular image recognition and classification approach. The paper reviews various techniques for classifying image-based wheat leaf diseases, including spot blotch, stripe rust, brown rust, and powdery mildew. The paper aims to summarize the state-of-the-art techniques for detecting wheat leaf diseases.

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“…For example, the Ginkgo Leaf Disease Classification System (Li et al . 2019) and the Stomata Counter System (Fetter et al . 2019), are both based on convolutional neural networks (CNN) (LeCun et al .…”

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confidence: 99%

Machine‐learning‐based morphological analyses of leaf epidermal cells in modern and fossil ginkgo and their implications for palaeoclimate studies

Zhang,

Wang,

Ruhl

et al. 2023

Palaeontology

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Leaf stomata form an essential conduit between plant tissue and the atmosphere, thus presenting a link between plants and their environments. Changes in their properties in fossil leaves have been studied widely to infer palaeo‐atmospheric‐CO2 in deep time, ranging from the Palaeozoic to the Cenozoic. Epidermal cells of leaves, however, have often been neglected for their usefulness in reconstructing past‐environments, as their irregular shape makes the manual analyses of epidermal cells a challenging and error‐prone task. Here, we used machine‐learning (using the U‐Net architecture, which evolved from a fully convolutional network) to segment epidermal cells automatically, to efficiently reduce artificial errors. We furthermore applied minimum bounding rectangles to extract length‐to‐width ratios (RL/W) from the irregularly shaped cells. We applied this to a dataset including over 21 000 stomata and 170 000 epidermal cells in 114 Ginkgo leaves from 16 locations spanning three climate zones in China. Our results show negative correlations between the RL/W and specific climatic parameters, suggesting that local temperature and precipitation conditions may have affected the RL/W of epidermal cells. We subsequently tested this methodology and the observations from the modern dataset on 15 fossil ginkgoaleans from the Lower to the Middle Jurassic (China). It suggested that the RL/W values of fossil ginkgo generally had a similar negative response to warmer climatic backgrounds as modern G. biloba. The automated analyses of large palaeo‐floral datasets provide a new direction for palaeoclimate reconstructions and emphasize the importance of hidden morphological characters of epidermal cells in ginkgoaleans.

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Classification of different degrees of ginkgo leaf disease based on deep learning

Cited by 4 publications

References 13 publications

Bibliometric Analysis on Identifying Plant, Crop Diseases Using Machine Learning and Deep Learning

Bibliometric Analysis on Identifying Plant, Crop Diseases Using Machine Learning and Deep Learning

Classification of Image-Based Wheat Leaf Diseases using Deep Learning Approach: A Survey

Machine‐learning‐based morphological analyses of leaf epidermal cells in modern and fossil ginkgo and their implications for palaeoclimate studies

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