Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network
AbstractPatchouli (Pogostemon Cablin Bent) has higher PA (Patchouli Alcohol) and oil production if grown in soil containing 75% organic matter. One way that can be used to detect the content of organic matter is to use soil images. The problem in the use of soil images is the color of the soil that is almost similar, namely the gradation between dark brown to black. Therefore, color features are not enough to be used as input in the recognition process. For this purposes, texture features are added in this study in addition to color features. The color features are extracted using color moment and the texture features are extracted using Gray Level Co-occurrence Matrix (GLCM). These feature was then chosen to get the best combination as input in the identification process using the Backpropagation Neural Network (BPNN). The system identifies the quantity of soil organic matter into five classes, namely very low, low, medium, high, and very high. The highest accuracy result obtained was 73% and MSE value 0.5122 by using five GLCM features (Angular Second Moment, contrast, correlation, Inverse Difference Moment, and entropy). This result was obtained by using the BPNN parameter, namely learning rate values 0.5, maximum iteration values of 1000, number training data 210, and total test data 12.
Wahyudi, A., Ermiati: Prospek Pengembangan Industri Minyak Nilam di Indonesia, 2012. Balai Penelitian Tanaman Rempah dan Obat, Bogor (2012).
Singh, R., Sing, M., Srinivas, A., Prakasa Rao, E.V.S, Puttanna, K.: Assesment of Organic and Inorganic Fertilizers for Growth, Yield and Essential Oil Quality of Industrially Important Plant Patchuli (Pogostemon cablin) (Blanco) Benth. Journal of Essential Oil Bearing Plants 18:1, 1-10 (2015).
Santoiemma, G: Recent Methodologies for Studying The Soil Organic Matter. Applied Soil Ecology 123, 546-550 (2018).
More, S., Khan, M. A., Priyanka, R. A: Soil Nutrient Detection Through Image Processing in Chromatogram Image. International Journal of Pure and Applied Research in Engineering and Technology 2: 8, 360-364 (2014).
Sorenson, P.T., Quideau, S.A., Rivard, B.: High Resolution Measurement of Soil Organic Carbon and Total Nitrogen With Laboratory Imaging Spectroscopy. Geoderma 315, 170-177 (2018)
Zuhri, S., Dewi, C., Basuki, A., Setiawan, B.D.: Identification of Patchouli Plants Using Landsat-8 Satellite Imagery and Improved K-Means Method. Journal of Environmental Engineering & Sustainable Technology JEEST 03: 02, 70 â€“ 77 (2016).
Dewi, C., Basuki, A.: Identifying Citronella Plants From UAV Imagery Using Support Vector Machine. Telkomnika 16: 4, 1877 â€“ 1885 (2018).
Adnan, Suhartini, Kusbiantoro, B.: Identifikasi Varietas Berdasarkan Warna dan Tekestur Permukaan Beras Menggunakan Pengolahan Citra Digital dan Jaringan Saraf Tiruan. Jurnal Penelitian Pertanian Tanaman Pangan 32:2, 91-97 (2013).
Budisanjaya, I. P. G: Identifikasi Nitrogen dan Kalium pada Daun Tnaman Sawi Hijau Menggunakan Matriks Co-occurrence, Moments dan Jaringan Saraf Tiruan. Tesis, Universitas Udayana Denpasar (2013). Accessed at <http://www.pps.unud.ac.id>.
Thakur, A., Dhole, A: Object Recognition From Image Using Grid Based Color Moments Feature Extraction Method. International Journal of Research in Engineering and Technology 02:3, 333-336 (2013).
Mohanaiah, P., Sathyanarayana, P., Gurukumar, L. Image Texture Feature Extraction Using GLCM Approach. International Journal of Scientific and Research Publications 3:5,1-5 (2013).
Albregtsen, F. Statistical Texture Measures Computed from Gray Level Coocurrence Matrices. University of Oslo: Department of Informatics (2008).
Dinas Perkebunan Provinsi Jawa Timur: Budidaya Tanaman Nilam. Plantation Agency of East Java Province (2013). Accessed at http://disbun.jatimprov.go.id.
Notohadiprawiro, T.: Tanah dan Lingkungan. Universitas Gadjah Mada, Yogyakarta (2006).
Fenton, M., Albers, C., Ketterings, Q.: Soil Organic Matter. Cornell University Cooperative Extension, New York (2008).
Murphy, B.W.: Soil Organic Matter and Soil Functional â€“ Review of the Literature and Underlying Data. Department of the Environment, Australia (2014).
Albregtsen, F. Statistical Texture Measures Computed from Gray Level Coocurrence Matrices. University of Oslo: Department of Informatics, Norway (2008).
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