Medicinal Plant Species Identification System Using Texture Analysis and Median Filter

Harsani, Prihastuti; Qur'ania, Arie

doi:10.28961/kursor.v8i4.112

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…Sistem yang dibangun melibatkan teknologi pengolahan citra. Penelitian tentang pengolahan citra digital untuk identifikasi tanaman sudah dilakukan diantaranya adalah identifikasi dini jenis tanaman obat melalui ciri daun melalui berbagai teknik machine learning yaitu dengan metode dimensi fraktral [2], pengembangan identifikasi tanaman obat menggunakan kode fraktral [3].…”

Section: Pendahuluanunclassified

Identifikasi Defisiensi Unsur Hara Pada Tanaman Cabai Menggunakan Support Vector Machine

Qur’ania,

Karlitasari,

Maryana

et al. 2023

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Tanaman seperti halnya makhluk hidup yang lain membutuhkan kombinasi unsur hara untuk hidup tumbuh dan berkembang biak. Unsur hara di dalam tanaman juga dapat dibagi menjadi dua, yaitu unsur hara bergerak (mobile nutrient) dan immobile nutrient (unsur hara tidak bergerak). Kondisi tanaman yang mengalami defisiensi atau kekurangan unsur hara akan mengalami gangguan pertumbuhan dan mempengaruhi terhadap hasil panen daun atau buahnya. Warna daun dapat menjadi ciri tanaman dalam kondisi normal atau mengalami defisiensi unsur hara. Defisiensi unsur hara pada tanaman akan berpengaruh pada bentuk daun, produksi buah dan usia tanaman yang mengakibatkan tanaman tumbuh kerdil dan lekas mati, pada produksi buah akan terjadi kerontokan pada bunga atau bakal buah sehingga hasil produksi akan mengalami penurunan. Selama ini pemeliharaan tanaman dilakukan secara manual. Setiap tanaman dilihat untuk kemudian dianalisis hasilnya dan memerlukan waktu untuk mengidentifikasi defisiensi unsur hara. Tujuan penelitian adalah mengidentifikasi defisiensi unsur hara pada tanaman cabai berbasis citra berdasarkan ciri warna Red, Green, Blue (RGB) dan analisis tekstur menggunakan Suport Vector Model (SVM). Manfaat penelitian adalah membuat aplikasi berbasis machine learning untuk mengidentifikasi kekurangan unsur hara yang terbagi dalam empat kelas, yaitu kekurangan nitrogen (N) dan Posfor (P), kekurangan P dan kalium (K), kekurangan N dan K, serta daun cabai dengan kelas normal. Data tanaman sebanyak 120 data dengan hasil akurasi sebesar 84,4%.

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Section: Pendahuluanunclassified

Identifikasi Defisiensi Unsur Hara Pada Tanaman Cabai Menggunakan Support Vector Machine

Qur’ania,

Karlitasari,

Maryana

et al. 2023

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View full text Add to dashboard Cite

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“…The datasets that have been widely used are Swedish Leaf Dataset, Flavia Dataset, Leafsnap dataset and ICL dataset. The examples of studies that used these datasets for automatic identification are Arun et al (2013), Begue et al (2017), Dahigaonkar and Kalyane (2018), Harsani and Qurania (2016), Janani and Gopal (2013), Pornpanomchai et al (2011), Vijayashree and Gopal (2017), Wäldchen and Mäder (2018a). The standard datasets for the studies in the uncontrolled environment are very limited, and until now, the only reliable option is the ImageCLEF datasets.…”

Section: Introductionmentioning

confidence: 99%

MYLPHerb-1: A Dataset of Malaysian Local Perennial Herbs for the Study of Plant Images Classification under Uncontrolled Environment

Pushpanathan¹,

Hanafi²,

Masohor³

et al. 2022

JST

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Research in the medicinal plants’ recognition field has received great attention due to the need of producing a reliable and accurate system that can recognise medicinal plants under various imaging conditions. Nevertheless, the standard medicinal plant datasets publicly available for research are very limited. This paper proposes a dataset consisting of 34200 images of twelve different high medicinal value local perennial herbs in Malaysia. The images were captured under various imaging conditions, such as different scales, illuminations, and angles. It will enable larger interclass and intraclass variability, creating abundant opportunities for new findings in leaf classification. The complexity of the dataset is investigated through automatic classification using several high-performance deep learning algorithms. The experiment results showed that the dataset creates more opportunities for advanced classification research due to the complexity of the images. The dataset can be accessed through https://www.mylpherbs.com/.

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“…References [12,13] have developed research using leaf imagery. All two studies result in an application that can identify early types of medicinal plants.…”

Section: Introductionmentioning

confidence: 99%

Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

Qur'ania

Harsani

Triastinurmiatiningsih

et al. 2020

CommIT (Communication and Information Technology) Journal

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The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and Pand P and K. The r esearchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.

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Medicinal Plant Species Identification System Using Texture Analysis and Median Filter

Abstract: Identification of plants can be done through objects -objects in plants by asking

Cited by 4 publications

References 1 publication

Identifikasi Defisiensi Unsur Hara Pada Tanaman Cabai Menggunakan Support Vector Machine

Identifikasi Defisiensi Unsur Hara Pada Tanaman Cabai Menggunakan Support Vector Machine

MYLPHerb-1: A Dataset of Malaysian Local Perennial Herbs for the Study of Plant Images Classification under Uncontrolled Environment

Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks

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