Leaf feature extraction using glcm, moment invariant and shape morphology for indonesian medicinal plants recognition

Syahputra, Hermawan; Indra, Zulfahmi; Febrian, Didi; Adriani, Dhea Putri

doi:10.1088/1742-6596/1317/1/012008

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…In GLCM, a matrix shows how a pair of pixels' ( ) y x, values change in accordance to an image's spatial connection. These pixels together make up a feature vector that indicates whether a pixel belongs to color or shape [25]. Angle ( )  and displacement ( ) d between x and y establish the spatial connection.…”

Section: Handcrafted Feature Extractionmentioning

confidence: 99%

A Cloud-Based Platform for Leaf Disease Segmentation and Classification using Hybrid Deep Learning Model

Subash,

Arulselvi

2023

ijmst

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This paper presents a cloud-based hybrid deep learning method for classifying leaf diseases. At first, sensors aid in detecting soil factors such as moisture content, pH level, air temperature, and humidity. The hybrid Recurrent Layer based Deep Convolutional Neural Network (RLDCNN) approach consists of five distinct stages: preprocessing, segmentation, handcrafted feature extraction, feature selection, and classification. The preprocessing of image involves removal of noise using Averaged Double Plateau Histogram Equalisation (ADPHE). Afterwards, the specific part of the image that is impacted is divided into segments using an improved Wavelet transform. After the process of segmentation, the GLCM (Gray-Level Co-occurrence Matrix) is used to extract features. The hybrid Fire Hawk with Egret Swarm Optimisation (FHESO) algorithm is a very efficient approach for feature selection, significantly lowering the dimensionality of features. The detection of apple, banana mango and groundnut leaf disease are accomplished via the use of a Recurrent Layer based Deep Convolutional Neural Network (RLDCNN), which is trained using the Deer Hunting Algorithm (DHA) that has been created. The suggested concept is implemented inside a cloud-based collaborative framework to provide immediate assistance to farmers. The experimental findings exhibit favourable outcomes, demonstrating a high level of accuracy, precision, a low rate of loss, recall, and F-score. The RLDCNN network method outperforms previous parameters, obtaining an accuracy of 98.51% according to comparative research.

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Section: Handcrafted Feature Extractionmentioning

confidence: 99%

A Cloud-Based Platform for Leaf Disease Segmentation and Classification using Hybrid Deep Learning Model

Subash,

Arulselvi

2023

ijmst

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Digital image processing for preliminary detection of infected porang (Amorphophallus muelleri) seedlings

Mutia Zahra,

Anggi Nurrahmah,

Rahayoe

et al. 2024

Res. Agric. Eng.

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Porang (Amorphophallus muelleri) is an Indonesian parental plant tuber developed vegetatively from bulbils during dormancy and harvested through petiole detachment for the industrial production of glucomannan. Pathogenic fungi and whiteflies can cause infection during harvesting and storage, destructing plant cells as well as reducing seed quality and crop yields. Therefore, this study aimed to develop a calibration model for detecting infected and non-infected porang bulbils using a computer vision system. Image parameters such as colour (red, green, blue -RGB and hue, saturation, intensity -HSI), texture (contrast, homogeneity, correlation, energy, and entropy), and dimensions (width, area, and height) were evaluated on 90 samples in three positions. The results showed that the majority of image quality properties were significantly associated with non-infected and infected porang bulbils as showed by Pearson correlation values of 0.901 and 0.943, respectively. Discriminant analysis based on image attributes effectively classified non-infected and infected seedlings, achieving a model accuracy of 97.0% for correctly classified cross-validated grouped cases. Therefore, computer vision can be used for the preliminary detection of fungal infection in porang bulbils, as evidenced by its high accuracy and outstanding model performance.

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Leaf feature extraction using glcm, moment invariant and shape morphology for indonesian medicinal plants recognition

Cited by 2 publications

References 2 publications

A Cloud-Based Platform for Leaf Disease Segmentation and Classification using Hybrid Deep Learning Model

A Cloud-Based Platform for Leaf Disease Segmentation and Classification using Hybrid Deep Learning Model

Digital image processing for preliminary detection of infected porang (Amorphophallus muelleri) seedlings

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