Plant Recognition from Leaf Image through Artificial Neural Network

Hati, Shayan; Sajeevan, G.

doi:10.5120/10172-4897

Cited by 29 publications

(20 citation statements)

References 4 publications

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“…The results of these methods are then combined with the help of incremental classification algorithm which provides 81.1%accuracy. The research document introduced by Shayan Hati, et al, [7] includes an article that describes how Artificial Neural Network is used to identify plant by inputting leaf image. Compared to earlier approaches, new input features and image processing approach that matter in efficient classification in Artificial Neural Network have been introduced.…”

Section: Literature Reviewmentioning

confidence: 99%

Leaf Classification based on GLCM Texture and SVM

Naveen¹,

Vidyashankara²,

Hemantha³

2020

IJCA

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This paper involves classification of leaves using GLCM (Gray Level Co-occurrence matrix) texture and SVM (Support Vector Machines). GLCM is used for extracting texture feature of leaves. Creating a plant database for quick and efficient classification and recognition is an important step for their conservation. This approach would help to extract useful features of leaf and improve the accuracy of leaf classification. The standard leaf images are subjected to preprocessing. Feature values are extracted from pre-processed image and they are trained and classified. Standard data sets are used for enhancing the properties of the image.

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Section: Literature Reviewmentioning

confidence: 99%

Leaf Classification based on GLCM Texture and SVM

Naveen¹,

Vidyashankara²,

Hemantha³

2020

IJCA

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“…Hati and Sajeevan [ 13 ] calculated the angle of apex and base angle in and . Ab Jabal et al [ 14 ], and Arun Priya, Balasaravanan and Thanamani [ 15 ] only found the angle of leaf tip in from leaf length regardless of its base information.…”

Section: Related Workmentioning

confidence: 99%

“…Previous researchers, such as Hati and Sajeevan [ 13 ], Arun Priya [ 15 ], measured the angle of the leaf apex and base to make this measurement as a parameter to interpret the leaf apex. However, in the perspective of botanical knowledge, all leaf bases with the same angle are actually not the same.…”

Section: Related Workmentioning

confidence: 99%

An expert botanical feature extraction technique based on phenetic features for identifying plant species

et al. 2018

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In this paper, we present a new method to recognise the leaf type and identify plant species using phenetic parts of the leaf; lobes, apex and base detection. Most of the research in this area focuses on the popular features such as the shape, colour, vein, and texture, which consumes large amounts of computational processing and are not efficient, especially in the Acer database with a high complexity structure of the leaves. This paper is focused on phenetic parts of the leaf which increases accuracy. Detecting the local maxima and local minima are done based on Centroid Contour Distance for Every Boundary Point, using north and south region to recognise the apex and base. Digital morphology is used to measure the leaf shape and the leaf margin. Centroid Contour Gradient is presented to extract the curvature of leaf apex and base. We analyse 32 leaf images of tropical plants and evaluated with two different datasets, Flavia, and Acer. The best accuracy obtained is 94.76% and 82.6% respectively. Experimental results show the effectiveness of the proposed technique without considering the commonly used features with high computational cost.

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“…It can make simple judgments by simulating the human brain, so it has been widely used in plant disease detection and recognition. Hati et al [14] programmed it with 400 leaves from 20 plants and tested 134 leaves, achieving an accuracy of 92%.…”

Section: Introductionmentioning

confidence: 99%

Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification

Lin

Liu

et al. 2020

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Diseases from Ginkgo biloba have brought great losses to medicine and the economy. Therefore, if the degree of disease can be automatically identified in Ginkgo biloba leaves, people will take appropriate measures to avoid losses in advance. Deep learning has made great achievements in plant disease identification and classification. For this paper, the convolution neural network model was used to classify the different degrees of ginkgo leaf disease. This study used the VGGNet-16 and Inception V3 models. After preprocessing and training 1322 original images under laboratory conditions and 2408 original images under field conditions, 98.44% accuracy was achieved under laboratory conditions and 92.19% under field conditions with the VGG model. The Inception V3 model achieved 92.3% accuracy under laboratory conditions and 93.2% under field conditions. Thus, the Inception V3 model structure was more suitable for field conditions. To our knowledge, there is very little research on the classification of different degrees of the same plant disease. The success of this study will have a significant impact on the prediction and early prevention of ginkgo leaf blight.

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Plant Recognition from Leaf Image through Artificial Neural Network

Cited by 29 publications

References 4 publications

Leaf Classification based on GLCM Texture and SVM

Leaf Classification based on GLCM Texture and SVM

An expert botanical feature extraction technique based on phenetic features for identifying plant species

Using Deep Learning for Image-Based Different Degrees of Ginkgo Leaf Disease Classification

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