Prediction of Phenotypic Parameters of Sugarcane Plants Based on Multispectral Drone Imagery and Machine learning
DOI:
https://doi.org/10.23960/jtep-l.v13i4.1182-1195Abstract
Measuring phenotypic parameters is important in evaluating the productivity of sugarcane. Existing manual measurements are considered less efficient, so a better alternative method is needed. This research aims to explore the potential of using multispectral drone imagery and machine learning to estimate phenotypic parameters of sugarcane plants that are efficient, accurate, inexpensive, and support sustainable agricultural practices. Spectrum data captured by drones, namely Green, Red, RedEdge and NIR are used as inputs to estimate phenotypic parameters including brix value, number of stands, stem diameter, and plant height. Based on the results of machine learning model development, the ANN algorithm model is most effective in predicting Brix Value with R2 0.74 and RMSE 0.06 and number of stands with R2 0.68 and RMSE 2.13. All models could not predict stem diameter and plant height well. The best model to predict plant height was obtained by RF algorithm with R2 0.53 and RMSE 14.09. SVR algorithm was the best model to predict plant diameter with R2 0.39. and RMSE 0.49. This indicates that the effectiveness of an algorithm depends on the specific parameter being predicted and there is no dominant algorithm for all phenotypic parameters.
Keywords: Machine learning, Multispectral drone imagery, Phenotypic parameter, Plant productivity, Sugarcane.
References
Akbarian, S., Jamnani, M.R., Xu, C., Wang, W., & Lim, S. (2023). Plot level sugarcane yield estimation by machine learning on multispectral images: A case study of Bundaberg, Australia. Information Processing in Agriculture, xxxx. https://doi.org/10.1016/j.inpa.2023.06.004
Akbarian, S., Xu, C., Wang, W., Ginns, S., & Lim, S. (2022). Sugarcane yields prediction at the row level using a novel cross-validation approach to multi-year multispectral images. Computers and Electronics in Agriculture, 198, 107024. https://doi.org/10.1016/j.compag.2022.107024
Badan Pusat Statistik. (2023). Statisitik Tebu Indonesia 2022 (Direktorat Statistik Tanaman Pangan (ed.); Vol. 13). BPS RI/BPS-Statistics Indonesia.
Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., & Gattelli, M. (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 7(4), 4026–4047. https://doi.org/10.3390/rs70404026
Cedric, L. S., Adoni, W. Y. H., Aworka, R., Zoueu, J. T., Mutombo, F. K., Krichen, M., & Kimpolo, C. L. M. (2022). Crops yield prediction based on machine learning models: Case of West African countries. Smart Agricultural Technology, 2. https://doi.org/10.1016/j.atech.2022.100049
Costa, L., Ampatzidis, Y., Rohla, C., Maness, N., Cheary, B., & Zhang, L. (2021). Measuring pecan nut growth utilizing machine vision and deep learning for the better understanding of the fruit growth curve. Computers and Electronics in Agriculture, 181, 105964. https://doi.org/10.1016/j.compag.2020.105964
de Oliveira, R.P., Barbosa Júnior, M.R., Pinto, A.A., Oliveira, J.L.P., Zerbato, C., & Furlani, C.E.A. (2022). Predicting Sugarcane Biometric Parameters by UAV Multispectral Images and Machine Learning. Agronomy, 12(9). https://doi.org/10.3390/agronomy12091992
Ellafi, M.A., Deeks, L.K., & Simmons, R.W. (2021). Application of artificial neural networks to the design of subsurface drainage systems in Libyan agricultural projects. Journal of Hydrology: Regional Studies, 35, 100832. https://doi.org/10.1016/j.ejrh.2021.100832
Guo, Y., Wang, H., Wu, Z., Wang, S., Sun, H., Senthilnath, J., Wang, J., Bryant, C.R., & Fu, Y. (2020). Modified red blue vegetation index for chlorophyll estimation and yield prediction of maize from visible images captured by uav. Sensors (Switzerland), 20(18), 1–16. https://doi.org/10.3390/s20185055
Hanif, W.H., & Gunawan, F.E. (2022). Random forest regression to predict catalyst deactivation in industrial catalytic process. TEM Journal, 11(1), 104–110. https://doi.org/10.18421/TEM111-12
Haryadi, D., Hakim, A.R., Atmaja, D.M.U., & Yutia, S.N. (2022). Implementation of support vector regression for polkadot cryptocurrency price prediction. International Journal on Informatics Visualization, 6(1–2), 201–207. https://doi.org/10.30630/joiv.6.1-2.945
Jurado-Expósito, M., Torres-Sánchez, J., López-Granados, F., & Jiménez-Brenes, F.M. (2021). Monitoring the spatial variability of knapweed (Centaurea diluta aiton) in wheat crops using geostatistics and uav imagery: Probability maps for risk assessment in site-specific control. Agronomy, 11(5). https://doi.org/10.3390/agronomy11050880
Kayad, A., Sozzi, M., Gatto, S., Marinello, F., & Pirotti, F. (2019). Monitoring within-field variability of corn yield using sentinel-2 and machine learning techniques. Remote Sensing, 11(23). https://doi.org/10.3390/rs11232873
Kerkech, M., Hafiane, A., & Canals, R. (2020). Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach. Computers and Electronics in Agriculture, 174. https://doi.org/10.1016/j.compag.2020.105446
Khanal, S., Fulton, J., Klopfenstein, A., Douridas, N., & Shearer, S. (2018). Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Computers and Electronics in Agriculture, 153, 213–225. https://doi.org/10.1016/j.compag.2018.07.016
Kou, J., Duan, L., Yin, C., Ma, L., Chen, X., Gao, P., & Lv, X. (2022). Predicting leaf nitrogen content in cotton with UAV RGB images. Sustainability (Switzerland), 14(15). https://doi.org/10.3390/su14159259
Kumar, R., Singh, M.P., Kumar, P., & Singh, J.P. (2015). Crop selection method to maximize crop yield rate using machine learning technique. 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials, ICSTM 2015 - Proceedings, 138–145. https://doi.org/10.1109/ICSTM.2015.7225403
Magfiroh, I.S., Setyawati, I.K., & Wibowo, R. (2017). Mutu tebu industri gula di Indonesia. Prosiding Seminar Nasional Pembangunan Pertanian II, 94–100.
Munir, S., Seminar, K.B., Sudradjat, Sukoco, H., & Buono, A. (2023). The use of random forest regression for estimating leaf nitrogen content of oil palm based on Sentinel 1-A Imagery. Information (Switzerland), 14(1). https://doi.org/10.3390/info14010010
Narmilan, A., Gonzalez, F., Salgadoe, A.S.A., Kumarasiri, U.W.L.M., Weerasinghe, H.A.S., & Kulasekara, B.R. (2022). Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imagery. Remote Sensing, 14(5).
Nunes, M., Ferreira, Ó., Schaefer, M., Clifton, J., Baily, B., Moura, D., & Loureiro, C. (2009). Hazard assessment in rock cliffs at Central Algarve (Portugal): A tool for coastal management. Ocean and Coastal Management, 52(10), 506–515. https://doi.org/10.1016/j.ocecoaman.2009.08.004
Pusdatin. (2022). Outlook Komoditas perkebunan tebu. In A.A. Susanti & R.K. Putra (Eds.), Pusat Data dan Sistem informasi Petanian (Tahun 2022, Issue 1). Pusat Data dan Sistem inofrmasi Pertanian.Sekretariat Jendral-Kementrian Pertanian.
Schirrmann, M., Hamdorf, A., Giebel, A., Gleiniger, F., Pflanz, M., & Dammer, K.H. (2017). Regression kriging for improving crop height models fusing ultra-sonic sensing with UAV imagery. Remote Sensing, 9(7), 1–18. https://doi.org/10.3390/rs9070665
Shendryk, Y., Sofonia, J., Garrard, R., Rist, Y., Skocaj, D., & Thorburn, P. (2020). Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging. International Journal of Applied Earth Observation and Geoinformation, 92, 102177. https://doi.org/10.1016/j.jag.2020.102177
Solahudin, M., Pramudya, B., Liyantono, Supriyanto, & Manaf, R. (2015). Gemini virus attack analysis in field of chili (Capsicum Annuum L.) using aerial photography and bayesian segmentation method. Procedia Environmental Sciences, 24, 254–257. https://doi.org/10.1016/j.proenv.2015.03.033
Vijayakumar, V., Ampatzidis, Y., & Costa, L. (2023). Tree-level citrus yield prediction utilizing ground and aerial machine vision and machine learning. Smart Agricultural Technology, 3, 100077. https://doi.org/10.1016/j.atech.2022.100077
Wahyuni, A.P., Astuti, I.S., & Purwanto, P. (2023). Estimasi fase pertumbuhan dan produktivitas tebu menggunakan citra Sentinel 2 di Kecamatan Dampit, Kabupaten Malang. Jurnal Integrasi dan Harmoni Inovatif Ilmu-Ilmu Sosial (JIHI3S), 3(2), 104–122. https://doi.org/10.17977/um063v3i2p104-122
Downloads
Published
Issue
Section
License
- Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International Lice that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Jurnal Teknik Pertanian Lampung
JTEPL is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
