Classification of orchid species using Neural Network

Sani, Maizura Mohd; Kutty, Suhaili Beeran; Omar, Habibah; Isa, Ili Nadia Md

doi:10.1109/iccsce.2013.6720033

Cited by 11 publications

(5 citation statements)

References 7 publications

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“…Hingga saat ini, pengenalan pola citra telah dan terus dikembangkan menggunakan bermacam-macam pendekatan. Beberapa pengidentifikasi banyak digunakan dalam studi klasifikasi citra sebelumnya adalah seperti K-Nearest Neighbor (KNN) [5]- [7],Support Vector Machine (SVM) [8]- [10], Multi-SVM [11] dan Neural Network [3], [12]. Penelitian ini mengusulkan ekstraksi fitur orde satu sebagai input untuk proses identifikasi.…”

Section: Pendahuluanunclassified

Multi-SVM Dalam Identifikasi Bunga Berbasis Ekstraksi Ciri Orde Satu

Sari

2023

InComTech

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Bunga merupakan modifikasi tunas dimana bentuk, warna, dan susunannya menyesuaikan kepentingan dari tumbuhan tersebut. Bunga berfungsi sebagai tempat berlangsungnya penyerbukan. Ada sangat banyak jenis bunga yang dapat dikenali di dunia ini. Perkembangan teknologi saat ini dapat dimanfaatkan sebagai fasilitas kepada manusia untuk membangun sebuah sistem yang dapat mengenali suatu citra. Penelitian ini, mengusulkan teknik mengidentifikasi citra bunga dengan ekstraksi ciri orde satu dan berbasis Multi-Support Vector Machine (Multi-SVM). Pemilihan ekstraksi ciri orde satu adalah karena merupakan ekstraksi ciri tekstur pada struktur makro yang dianggap cocok dalam mengidentifikasi jenis bunga. Pada tahap ekstraksi ciri, citra yang semula adalah citra RGB dikonversi terlebih dahulu menjadi citra berskala abu-abu. Multi-SVM memiliki keunggulan dalam mengklasifikasikan lebih dari dua kelas. Dalam penelitian ini digunakan lima jenis bunga yaitu Rose, Calendula, Peony, Leucanthemum Maximum, dan Iris dengan 300 citra pelatihan dan 150 citra pengujian. Berdasarkan pengujian identifikasi, menghasilkam akurasi sebesar 90.6667%.

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Section: Pendahuluanunclassified

Multi-SVM Dalam Identifikasi Bunga Berbasis Ekstraksi Ciri Orde Satu

Sari

2023

InComTech

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“…The value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbours. By choosing the size and shape of the neighbourhood, it can construct a morphological operation that is sensitive to specific shapes in the input image [20][21][22][23].…”

Section: Border Segmentation Techniquesmentioning

confidence: 99%

“…Healthy plants are less likely to became infected with disease or be attractive to insects. Orchid disease has three categories which are fungal, bacterial and viruses [3]. Fungal is a mound like organism which infects a plant and become visible when it produces its spores while bacterial are always far too small to be seen through the symptoms are visible when the plant becomes disease.…”

Section: Introductionmentioning

confidence: 99%

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Orchid leaf disease detection using border segmentation techniques

Fadzil

Rizam

Jailani

et al. 2014

2014 IEEE Conference on Systems, Process and Control (ICSPC 2014)

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Nowadays, it is hard to distinguish the type of orchid leaf diseases just by using naked eyes. This paper presents an image segmentation technique for classify two difference types of orchid leaf disease such as black leaf spot and sun scorch. The orchid leaves images were digitally captured by using digital camera. With respect to the region of interest selected orchid leaves are analyze by using border segmentation techniques using MATLAB. In this paper, filtering technique and morphological processing technique will be applied to the images. The graphical user interface has been developed to automatically classify orchid diseases. The system has potential as early detection system for classify orchid diseases.Index Terms-image segmentation, morphological techniques, black leaf spot, sun scorch 978-1-4799-6106-

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“…Dendrobium orchids [3] are able to meet the demands of interest consumers whose tastes change over time. This can be seen from the types of orchids on the market that have varied flower colors and shapes, as well as the presence of new varieties with a more beautiful and attractive appearance [4].…”

Section: Introductionmentioning

confidence: 99%

Orchid types classification using supervised learning algorithm based on feature and color extraction

et al. 2021

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Orchid flower as ornamental plants with a variety of types where one type of orchid has various characteristics in the form of different shapes and colors. Here, we chosen support vector machine (SVM), Naïve Bayes, and k-nearest neighbor algorithm which generates text input. This system aims to assist the community in recognizing orchid plants based on their type. We used more than 2250 and 1500 images for training and testing respectively which consists of 15 types. Testing result shown impact analysis of comparison of three supervised algorithm using extraction or not and several variety distance. Here, we used SVM in Linear, Polynomial, and Gaussian kernel while k-nearest neighbor operated in distance starting from K1 until K11. Based on experimental results provide Linear kernel as best classifier and extraction process had been increase accuracy. Compared with Naïve Bayes in 66%, and a highest KNN in K=1 and d=1 is 98%, SVM had a better accuracy. SVM-GLCM-HSV better than SVM-HSV only that achieved 98.13% and 93.06% respectively both in Linear kernel. On the other side, a combination of SVM-KNN yield highest accuracy better than selected algorithm here.

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Classification of orchid species using Neural Network

Cited by 11 publications

References 7 publications

Multi-SVM Dalam Identifikasi Bunga Berbasis Ekstraksi Ciri Orde Satu

Multi-SVM Dalam Identifikasi Bunga Berbasis Ekstraksi Ciri Orde Satu

Orchid leaf disease detection using border segmentation techniques

Orchid types classification using supervised learning algorithm based on feature and color extraction

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