COMPARATIVE ACCURACIES USING MACHINE LEARNING MODELS FOR MAPPING OF SUGARCANE PLANTATION BASED ON SENTINEL-2A IMAGERY IN KEDIRI AREA, EAST JAVA
Abstract
Data collection in smallholder sugarcane plantations is still very sensitive to the subjectivity of informants and data collectors. In the meantime, the problem with data collection on sugarcane plantation companies is a low response rate. This situation can reduce the precision of the estimates that are produced. Consequently, the goal of this research is to recognize sugarcane fields using the machine learning models on Sentinel-2A satellite imagery in Kediri Area that covering Kediri Regency and Kediri Municipality, East Java. Along with developing machine learning algorithms, this research will evaluate how well LightGBM performs when compared to other algorithms, including CART, SVM, Random Forest, and XGBoost. Each model employed hyperparameter tuning with random search and stratified 10-fold cross validation to avoid overfitting. The process of labelling satellite imagery using images from Google Street View, then predictor variables used are NDVI, NDWI, NDBI, EVI, and elevation. The most accurate classification model obtained was LightGBM, with a 98% accuracy and a cohen’s kappa of 97.7%. The estimated area of sugarcane plantations in the Kediri Regency and Kediri Municipality in September 2022 is 18,897.6 ha and 571.87 ha.
Keywords
Full Text:
PDFReferences
BPS (2022). Statistik Tebu Indonesia 2021. Badan Pusat Statistik, Indonesia.
Cevallos, J. C., Villagomez, J. A., & Andryshchenko, I. S. (2019). Convolutional neural network in the recognition of spatial images of sugarcane crops in the Troncal region of the coast of Ecuador. Procedia Computer Science, 150, 757-763.
Ghaddar, B., & Naoum-Sawaya, J. (2018). High dimensional data classification and feature selection using support vector machines. European Journal of Operational Research, 265(3), 993-1004.
Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756.
Indrawanto, C., Purwono, S., Syakir, M., & Rumini, W. (2010). Budidaya dan pasca panen Tebu. ESKA media. Jakarta.
Jiang, H., Li, D., Jing, W., Xu, J., Huang, J., Yang, J., & Chen, S. (2019). Early season mapping of sugarcane by applying machine learning algorithms to Sentinel-1A/2 time series data: a case study in Zhanjiang City, China. Remote Sensing, 11(7), 861.
Kementrian Pertanian. (2013). Pedoman Pelaksanaan Pengelolaan Data Komoditas Perkebunan (PDKP). Direktorat Jenderal Perkebunan Kementerian Pertanian, Jakarta.
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
Kuhn, M., & Silge, J. (2022). Tidy Modeling with R. " O'Reilly Media, Inc.".
Landis, J. R., & Koch, G. G. (1977). The Measurement of Observer Agreement for Categorical Data. International Biometric Society, 33(1), 159–174.
Luciano, A. D. S., Picoli, M. C. A., Rocha, J. V., Franco, H. C. J., Sanches, G. M., Leal, M. R. L. V., & Maire, G. L. (2018).
Generalized space-time classifiers for monitoring sugarcane areas in Brazil. Remote sensing of environment, 215, 438-451.
Marsuhandi, A. H., Triscowati, D. W., & Wijayanto, A. W. (2020). TINJAUAN PEMANFAATAN BIG DATA PENGINDERAAN JAUH DAN PEMBELAJARAN MESIN UNTUK OFFICIAL STATISTICS DI WILAYAH PERKOTAAN. Jurnal Aplikasi Statistika & Komputasi Statistik, 12(2), 31-40.
Mulianga, B., Bégué, A., Clouvel, P., & Todoroff, P. (2015). Mapping cropping practices of a sugarcane-based cropping system in Kenya using remote sensing. Remote Sensing, 7(11), 14428-14444.
Nurmasari, Y., & Wijayanto, A. W. (2021). Oil Palm Plantation Detection in Indonesia Using Sentinel-2 and Landsat-8 Optical Satellite Imagery (Case Study: Rokan Hulu Regency, Riau Province). International Journal of Remote Sensing and Earth Sciences (IJReSES), 18(1), 1-18.
Nonato, R. T., & Oliveira, S. R. D. M. (2013). Data mining techniques for identification of sugarcane crop areas in images of Landsat 5. Engenharia Agricola, 33, 1268-1280.
Pramana, S., Yuniarto, B., Mariyah, S., Santoso, I., & Nooraeni, R. (2018). Data mining dengan R konsep serta implementasi. Jakarta: InMedia.
Prusty, S., Patnaik, S., & Dash, S. K. (2022). SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer. Frontiers in Nanotechnology, 4, 972421.
Ruslan, K., & Prasetyo, O. R. (2021). Perbaikan Data Perkebunan Indonesia: Kopi, Gula dan Kakao.
Schultz, B., Immitzer, M., Roberto Formaggio, A., Del’Arco Sanches, I., José Barreto Luiz, A., & Atzberger, C. (2015). Self-guided segmentation and classification of multi-temporal Landsat 8 images for crop type mapping in Southeastern Brazil. Remote Sensing, 7(11), 14482-14508.
Som-ard, J., Atzberger, C., Izquierdo-Verdiguier, E., Vuolo, F., & Immitzer, M. (2021). Remote sensing applications in sugarcane cultivation: A review. Remote sensing, 13(20), 404
Sulaiman, A. A., Subagyono, K., Soetopo, D., Richana, N., Syukur, M., & Ardana, I. K. (2018). Menjaring investasi meraih swasembada gula. IAARD Press.
Syathori, A. D., & Verona, L. (2020). Faktor-Faktor Yang Mempengaruhi Produksi Usahatani Tanaman Tebu di Desa Majangtengah Kecamatan Dampit Kabupaten Malang. AGRIEKSTENSIA: Jurnal Penelitian Terapan Bidang Pertanian, 19(2), 95-103.
Wang, J., Xiao, X., Liu, L., Wu, X., Qin, Y., Steiner, J. L., & Dong, J. (2020). Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images. Remote Sensing of Environment, 247, 111951.
Refbacks
- There are currently no refbacks.
