Prediction of rice harvest results based on aerial image segmentation of texture and color features in Nganggil Purwodadi Grobogan village
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Published:
Nov 23, 2023
Keywords:
Area image segmentation, Canny algorithm, Color hsv algorithm, Outsu algorithm
Section
Computer Science
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Abstract
Rice growth is very unstable in areas with large areas of land. The causes of unstable crop yields are caused by several factors, including natural factors, the type of rice planted, care models and also pests and weeds found in rice fields. Due to the large area of ??agricultural land, farmers cannot monitor the progress of the rice they plant. Monitoring of farmers is mostly only carried out in the edge areas of the rice fields, while those in the middle areas are most likely not included in monitoring. So this research will carry out a broad monitoring system that covers the entire rice field area. This system is carried out by taking pictures through the air using a drone. By using drones, the area coverage becomes wider and the image data obtained will then be processed to estimate the rice production results that will be obtained. In this imaging process, the k mean method is used to group images of agricultural areas. The identification process used is HSV color and texture using the Outsu and Canny algorithms for each part of the image. The default weight factor is the factor used to convert from RGB to HSV. With line selection, Parameters are culled: angle, length, mean, mode, bounding rectangle and standard deviation, min max values. The land area process where in this study there were 624 land images, this grouping produced areas that determined the shape of the rice or non-rice type images. From the table above, look for the average value of weight per kilogram and get predicted results with an average harvest area of ??21,078 quintals or 2.32 tons with an error rate of 0.82%.
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Supriyadi, E., Nugroho, A. S., Hayuningtyas, A. R. ., Rismawati , P. and Saniya , Z. F. (2023) “Prediction of rice harvest results based on aerial image segmentation of texture and color features in Nganggil Purwodadi Grobogan village ”, Jurnal Mantik, 7(3), pp. 2380-2390. doi: 10.35335/mantik.v7i3.4331.
References
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Arifin, M. J., Basuki, A., Sena, B., Dewantara, B., & Korespondensi, P. (2021). Segmentasi Pertumbuhan Padi Berbasis Aerial Image Menggunakan Fitur Warna Dan Tekstur Untuk Estimasi Produksi Hasil Panen Segmentation of Paddy Growth Area Based on Aerial Imagery Using Color and Texture Feature for Estimating Harvest Production. 8(1), 209–216. https://doi.org/10.25126/jtiik.202183438
Hall, O., Dahlin, S., Marstorp, H., Bustos, M. F. A., Öborn, I., & Jirström, M. (2018a). Classification of maize in complex smallholder farming systems using UAV imagery. Drones, 2(3), 1–8. https://doi.org/10.3390/drones2030022
Hall, O., Dahlin, S., Marstorp, H., Bustos, M. F. A., Öborn, I., & Jirström, M. (2018b). Classification of maize in complex smallholder farming systems using UAV imagery. Drones, 2(3), 1–8. https://doi.org/10.3390/drones2030022
Holik, A., Rahimi Bachtiar, R., Studi Agribisnis Politeknik Negeri Banyuwangi Jl Raya Jember Km, P., & Kabat Banyuwangi Jawa Timur, L. (2019). PREDIKSI HASIL PANEN PADI MENGGUNAKAN PESAWAT TANPA AWAK Prediction of Rice Harvest Using Unmanned Aircraft. Jurnal Ilmiah Rekayasa Pertanian Dan Biosistem, 7(2), 249–257. https://doi.org/10.29.303/jrpb.v/7i2.139
Jin, X., Liu, S., Baret, F., Hemerlé, M., & Comar, A. (2017). Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198, 105–114. https://doi.org/10.1016/j.rse.2017.06.007
Made Parsa, I., Dirgahayu, D., Manalu, J., Carolita, I., Pusat Pemanfaatan Penginderaan Jauh, W. K., & Jln Kalisari, L. (n.d.). UJI MODEL FASE PERTUMBUHAN PADI BERBASIS CITRA MODIS MULTIWAKTU DI PULAU LOMBOK (THE TESTING OF PHASE GROWTH RICE MODEL BASED ON MULTITEMPORAL MODIS IN LOMBOK ISLAND). http://MODIS.gsfc.nasa.
Mahananto, Sutrisno Salyo, A. C. F. (2009). FAKTOR-FAKTOR YANG MEMPENGARUHI PRODUKSI PADI Studi Kasus di Kecamatan Nogosari , Boyolali , Jawa Tengah. Wacana, 12(1), 179–191. http://wacana.ub.ac.id/index.php/wacana/article/view/181
Maspiyanti, F., Fanany, M. I., & Arymurthy, A. M. (n.d.). KLASIFIKASI FASE PERTUMBUHAN PADI BERDASARKAN CITRA HIPERSPEKTRAL DENGAN MODIFIKASI LOGIKA FUZZY (PADDY GROWTH STAGES CLASSIFICATION BASED ON HYPERSPECTRAL IMAGE USING MODIFIED FUZZY LOGIC).
Maspiyanti, F., Fanany, M. I., & Arymurthy, A. M. (2013). Klasifikasi Fase Pertumbuhan Padi Berdasarkan Hiperspektral Dengan Modifikasi Logika Fuzzy. Jurnal Penginderaan Jauh & Pengolahan Citra LAPAN, 10(1), 41–48.
PEMETAAN POLA TANAM DAN KALENDER TANAM. (n.d.).
Pratama, R. A., Nur Iman, B., Arifin, F., Negeri, P. E., Politeknik, S., Negeri, E., Kampus, S., Raya, J., Sukolilo, K., Sby, K., & Timur, J. (2022). CYCLOTRON?: Jurnal Teknik Elektro Penerapan Wahana Terbang Tanpa Awak untuk Memprediksi Waktu Panen pada Lahan Pertanian Berbasis Pengolahan Citra Digital.
Putri, A. Y., & Sumiharto, R. (2016). Purwarupa Sistem Prediksi Luas dan Hasil Panen Padi suatu Wilayah menggunakan Pengolahan Citra Digital dengan Metode Sobel dan Otsu. IJEIS, 6(2), 187–198.
Reza, M. N., Na, I. S., Baek, S. W., & Lee, K. H. (2019). Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images. Biosystems Engineering, 177, 109–121. https://doi.org/10.1016/j.biosystemseng.2018.09.014
Saputri, W., & Amalita, N. (2020). Analisa Tentang Luas Tanam dan Luas Panen di Bidang Komoditi Perkebunan di Provinsi Sumatera Barat dengan Menggunakan Analisis Profil (Vol. 3, Issue
Ahmad Revi1., Iin Parlina2 ., M. Safii. (2018). MODEL JARINGAN SYARAF TIRUAN MEMPREDIKSI PRODUKSI PADI INDONESIA BERDASARKAN PROVINSI. Jurnal Teknovasi. Volume 05, Nomor 02, 1 – 13.
Meychael Adi Putra Hutabarat., Muhammad Julham., Anjar Wanto. (2018). PENERAPAN ALGORITMA BACKPROPAGATION DALAM MEMPREDIKSI PRODUKSI TANAMAN PADI SAWAH MENURUT KABUPATEN/KOTA DI SUMATERA UTARA. semanTIK, Vol.4, No.1, Jan-Jun 2018, pp. 77-86.
Gandhi Ramadhona., Budi Darma Setiawan., Fitra A. Bachtiar. Prediksi Produktivitas Padi Menggunakan Jaringan Syaraf Tiruan Backpropagation. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN: 2548-964X Vol. 2, No. 12, Desember 2018, hlm. 6048-6057. 260.
Ida Bagus Kade Dwi Suta Negara ., I Putu Kusuma Negara ., Norsa Yudhi Arso . (2023) . PREDIKSI HASIL PANEN PADI DI KABUPATEN JEMBRANA MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER. Jurnal Teknologi Informasi dan Komputer, Volume 9, Nomor 3.
Willmen TB Panjaitan., Ema Utami., Hanif Al Fatta. (2018). PREDIKSI PANEN PADI MENGGUNAKAN ALGORITMA K-NEAREST NEIGBOUR. P r o s i d i n g S N A T I F K e – 5.
Fitri Hidayah., S. Santosa , Renny Eka Putri. (2019). Rice Yield Prediction Model Based on Nondestructive Measurements of Rice Chlorophyll Values Paddy Leaf. Agritech, 39 (4).
Rudi Hariyanto., Anang Aris Widodo. (2019). KLASIFIKASI HASIL PREDIKSI PANEN PADI BERDASARKAN FISIOLOGIS MENGGUNAKAN METODE NAÏVE BAYES CLASSIFICATION. Conference on Innovation and Application of Science and Technology (CIASTECH 2019).
Supriyanto., Sudjono., Desty Rakhmawati. (2012). Prediksi Luas Panen dan Produksi Padi di Kabupaten Banyumas Menggunakan Metode Adaptive Neuro-Fuzzy Inference System (ANFIS). Jurnal Probisnis Vol. 5 No. 2.
Mohammad Nur Fawaiq., Ahmad Jazuli., Muhammad Malik Hakim. (2019) . PREDIKSI HASIL PERTANIAN PADI DI KABUPATEN KUDUS DENGAN METODE BROWN’S DOUBLE EXPONENTIAL SMOOTHING. JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Volume 04, Nomor 02.
Yiru Ma ., Lulu Ma ., Qiang Zhang ., Changping Huang ., Xiang Yi ., Xiangyu Chen ., Tongyu Hou . (2022) . Cotton Yield Estimation Based on Vegetation Indices and Texture Features Derived From RGB Image. Frontiers in Plant Science | www.frontiersin.org 1 June 2022 | Volume 13 | Article 925986 ORIGINAL RESEARCH.
Wenan Yuan ., nuwan Kumara Wijewardane., Shawn Jenkins., Geng Bai., Yufeng . (2019). early prediction of Soybean traits through color and texture features of canopy RGB imagery. Scientific RepoRtS | (2019) 9:14089 | https://doi.org/10.1038/s41598-019-50480-x
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