Identifikasi Varietas Padi Menggunakan Pengolahan Citra Digital dan Analisis Diskriminan

Adnan, Adnan; Widiastuti, Mira Landep; Wahyuni, Sri

doi:10.21082/jpptp.v34n2.2015.p89-96

Cited by 8 publications

(14 citation statements)

References 6 publications

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“…Identifikasi varietas padi umumnya berdasarkan deskripsi secara visual, namun teknik tersebut sulit mengenali varietas yang mempunyai morfologi benih serupa (Adnan et al, 2015). Teknik yang berpotensi digunakan untuk identifikasi benih padi antara lain Simple Sequence Repeat (SSR) (Mulsanti et al, 2013), khusus untuk membedakan tetua padi hibrida, varietas padi yang berbeda atau campuran varietas lain, bahkan pada benih yang tertular penyakit.…”

Section: Pendahuluanunclassified

Mesin Pembelajaran Ensemble Untuk Identifikasi Varietas Padi

Ikhlas¹,

Abdullah

Prasetyo³

2020

Informatika Pertanian

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Setiap varietas padi memiliki karakter tertentu dengan anjuran tanam berbeda. Petani umumnya kesulitan memilih varietas padi yang cocok untuk ditanam di lahan mereka karena kurangnya kemampuan identifikasi. Algoritma klasifikasi merupakan solusi mengatasi masalah ini karena mampu mengidentifikasi varietas padi melalui citra digital. Tujuan penelitian ini adalah menerapkan dan mengevaluasi beberapa algoritma klasifikasi untuk mengidentifikasi varietas padi menggunakan fitur warna dan tekstur. Penelitian dilakukan di kabupaten Indagiri Hilir Riau pada tahun 2018. Mesin pembelajaran dibangun dengan cara menggabungkan beberapa algoritma klasifikasi (classifier), yaitu Support Vector Machine, k-Nearest Neighbors, Logistic Regression, dan Decision Tree. Varietas yang diteliti adalah IR42, Inpara-9. dan Batang Piaman. Berdasarkan tingkat ketelitian masing-masing algoritma, k-Nearest Neighbors memberikan hasil lebih baik dibanding algoritma lainnya, baik dengan maupun tanpa normalisasi data. Terdapat enam sampel Inpara-9 yang diprediksi benar (true positive) dan lima sampel diprediksi salah (false positive). Pada varietas Batang Piaman terdapat delapan sampel yang diprediksi benar (true positive). Pada IR42 terdapat lima sampel yang diprediksi benar.

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Section: Pendahuluanunclassified

Mesin Pembelajaran Ensemble Untuk Identifikasi Varietas Padi

Ikhlas¹,

Abdullah

Prasetyo³

2020

Informatika Pertanian

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“…Kuo et al (2016) discriminated rice grain by quantifying morphological parameters, texture and color with an accuracy of 89.1%. Adnan et al (2015) stated that round parameter had a considerable coefficient compared to other parameters, therefore it influenced the mode classified for different rice varieties.…”

Section: Rice Seed Identificationmentioning

confidence: 99%

“…A few researchers have measured the morphology of rice seed. Adnan et al (2015) identified rice variety based on area, perimeter, minimum feret, aspect ratio and round and got an accurate value of more than 50%. Others researchers (Kuo et al 2016) obtained an accurate value of 89%.…”

Section: Seed Purity Clarification Using Gotmentioning

confidence: 99%

“…Currently many computer hardwares are used for supporting image analysis with different purposes. In rice, image process has been used to identify milled rice derived varieties based on their surface colors and texture (Adnan et al 2015) and to screen salinity tolerance traits using the LemnaTec 3D Scanalyzer system (Hairmansis et al 2014). However, application of image-based phenotyping in the development of rice remains limited.…”

Section: Introductionmentioning

confidence: 99%

“…Studies using flatbed scanner on rice seeds include identifying rice seeds of the same variety based on surface shape and texture (Adnan et al 2015) and measuring the color of Chinese black rice (Kaisaat and Keawdonree 2017). However, confirmation of image analysis results with other methods has not been found.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Digital Image Analysis Using Flatbed Scanning System for Purity Testing of Rice Seed and Confirmation by Grow Out Test

Widiastuti¹,

Hairmansis²,

Palupi³

et al. 2018

Indones. J. Agric. Sci.

Self Cite

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<p class="abstrakinggris">The common method used for purity testing of rice seed is human visual observation. This method, however, has a high degree of subjectivity when dealing with different rice varieties which have similar morphology. Digital image analysis with flatbed scanning for purity testing of rice seed was proposed by investigating the morphology of rice seeds and confirmation by grow out test (GOT) in the field. Two extra-long seed varieties were used in this study including a red rice Aek Sibundong and an aromatic rice Sintanur. The identification on 14 parameters of morphological characteristics indicated that only six parameters were correlated, i.e. area, feret, minimum feret, aspect ratio, round, and solidity. The purity of rice seed can be effectively determined using digital image analysis of spikelet color and shape. Based on the discriminant analysis of the digital image the recognition rate of rice seed purity was higher than 99.2% for shape and 93.55% for color. The method, therefore, has a potential to be used as a complement in rice seed purity testing to increase the accuracy of human visual method and it is more sensitive than GOT.</p>

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Rice growth phase analysis in Pidie regency, Indonesia using multitemporal Sentinel-2 image data: a spectral angle mapper approach

Lisviananda

Sugianto

Rusdi

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Remote sensing data provides fast and relatively accurate information to retrieve the plant growth phase using spectral analysis. Spectral analysis of plants is the critical point of identifying the stages of rice growth using Sentinel-2 data. Sentinel-2 satellite images were utilized for this study. This study aims to analyze the growth phase of rice in Pidie regency, Aceh Province, Indonesia, as a sample area of the rice-growing site. The Spectral Angle Mapper (SAM) approach was performed to describe the plant growth stages. The results show variations in the rice growth phase across the study area for 2019, 2020, and 2021 growing seasons from vegetative, generative, wet fallow, and dry fallow. The most extensive vegetative phase is for April 2021 data, counting for 1,278.16 Ha. The most extensive generative phase was identified of June 2020 data, counting for 1,107.55 Ha. For wet fallow, counting for 949,30 Ha is the largest in this category. A total of 1,311.94 Ha of dry fallow is identified in 2019. The different growth phases and the total area for different years indicate variation in starting for the growing season of the sample location. In this paper, multitemporal Sentinel-2 data analyzed with the SAM approach has demonstrated identifying rice-growing season phases. This finding can help predict the total area along the year for a change of the pattern of the rice-growing season in the last three years of the study area.

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Identifikasi Varietas Padi Menggunakan Pengolahan Citra Digital dan Analisis Diskriminan

Cited by 8 publications

References 6 publications

Mesin Pembelajaran Ensemble Untuk Identifikasi Varietas Padi

Mesin Pembelajaran Ensemble Untuk Identifikasi Varietas Padi

Digital Image Analysis Using Flatbed Scanning System for Purity Testing of Rice Seed and Confirmation by Grow Out Test

Rice growth phase analysis in Pidie regency, Indonesia using multitemporal Sentinel-2 image data: a spectral angle mapper approach

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