Sugarcane Yield Estimation Using UAV-Based RGB Images and Allometric Equations
Article Sidebar
Main Article Content
Abstract
Accurately estimating pre-harvest sugarcane yield has long been a challenge. There are many methods for predicting sugarcane yield, ranging from simple empirical equations to complex physiological models. This paper reports a study on a method for predicting sugarcane yield using allometric equations combined with UAV-based RGB images (UAVI). UAVI were used for the estimation of the sugarcane height, which is in the form of the sugarcane height model (HM). Sugarcane height is one of the key factors in calculating sugarcane yield in the allometric equation. The results showed that the HM could be used in the allometric equation. There was only a slight discrepancy compared to measurement with a tape measure in the field. In developing the allometric equation, the authors created regression models to estimate aboveground biomass weight in leaf bush (Wl) and millable stalk (Wms) based on sugarcane height (H) and diameter measured from the first segment of sugarcane aboveground (Dfs). The model estimated aboveground biomass weight sufficiently for all stages of cultivation. Based on this model, the authors developed two general equations: Y = 0.0842 H * Dfs0.9827; R2 = 0.93 (used for leaf bush), and Y = 0.1254 H * Dfs1.3926; R2 = 0.93 (used for millable stalk). The decision correlation coefficient (R2) was 80% reliable. The HM models were slightly different from field measurements with a tape measure. Also, there was a little inaccuracy in the root-mean-square error (RMSE) between the sugarcane heights analyzed from UAVI and field measurements using tape measure. The RMSE values, arranged from highest to lowest according to the sugarcane growth stage were: 0.35 m (tillering phase, TP), 0.25 m (grand growth phase, GP), and 0.24 m (ripening phase, RP). Using the HM value in the allometric equation effectively estimated the sugarcane yield at different growth stages. Sugarcane growth at TP and GP phases gave the highest sugarcane yield at 200 cm of the HM value, with total yields of 11.49 and 16.75 ton/hectare, respectively. Whereas, RP gave the highest sugarcane yield at 350 cm of the HM value (total yield at 42.97 ton/hectare). Overall, an accurate estimation of the aboveground biomass of leaf bush and millable stalk can be obtained using these equations. The authors believe that the research methods presented here can help sugarcane farmers to better estimate yield prior to harvest.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright Transfer Statement
The copyright of this article is transferred to Current Applied Science and Technology journal with effect if and when the article is accepted for publication. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, electronic form (offline, online) or any other reproductions of similar nature.
The author warrants that this contribution is original and that he/she has full power to make this grant. The author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors.
Here is the link for download: Copyright transfer form.pdf
References
Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R. & Sousa, J. J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing, 9(11), Article 1110. https://doi.org/10.3390/rs9111110
Ahmed, A. Z., Ahmed, A. L. M. & Osman, M. S. (2011). Optimium inter-row spacing and number of ploughings for two promising sugarcane varietes. Egyptian Journal of Agricultural Research, 89(1), 301-316. https://doi.org/10.21608/ejar.2011.174277
Amphawan, W. (2018). Investigation of unmanned aerial vehicle technique to estimate a physical of rubber trees. [Bachelor's degree thesis, Naresuan University]. https://ww2.agi.nu.ac.th/nred/Document/is-PDF/2561/geo_2561_09_FullPaper.pdf
Anthony, D., Elbaum, S., Lorenz, A. & Detweiler, C. (2014). On crop height estimation with UAVs. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4805-4812). https://doi.org/10.1109/iros.2014.6943245
Antunes, W. C., Pompelli, M.F., Carretero, D. M. & DaMatta, F. M. (2008). Allometric models for non-destructive leaf area estimation in coffee (Coffea arabica and Coffea canephora). Annals of Applied Biology, 153, 33-40. https://doi:10.1111/j.1744-7348.2008.00235.x
Bajaj, Y. P. S. & Jian, L. C. (1995). Cryopreservation of germplasm of sugarcane (Saccharum species). In Y. P. S. Bajaj (Ed.). Cryopreservation of plant germplasm I. Biotechnology in Agriculture and Forestry, Vol 32 (pp. 256-265). Springer.
Bakker, H. (1999). Sugarcane cultivation and management. Klwer Academic/Plenum Publishers.
Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S. & Bareth, G. (2014). Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 6(11), 10395-10412. https://doi.org/10.3390/rs61110395
Blackburn, F. (1984). Sugarcane. Longman.
Boonlua, T., Pakdeethai, C. & Keawplang, S. (2021). An evaluation of UAV-derived aerial imagery for estimating the yield of sugarcane. Khon Kaen Agriculture Journal, 49 (Suppl. 1), 306-313.
Bunruang, P. & Kaewplang, S. (2021). Evaluation of sugarcane plant height using uav remote sensing. Engineering Access, 7(2), 98-102.
Chiadamrong, N. & Kawtummachai, R. (2008). A methodology to support decision-making on sugar distribution for export channel: a case study of Thai sugar industry. Computers and Electronics in Agriculture, 64(2), 248-261. https://doi.org/10.1016/j.compag.2008.05.018
Daba, D. E. & Soromessa, T. (2019). The accuracy of species-specific allometric equations for estimating aboveground biomass in tropical moist montane forests: case study of Albizia grandibracteata and Trichilia dregeana. Carbon Balance and Management, 14, 1-13. https://doi.org/10.1186/s13021-019-0134-8
de Carvalho, E. X., Menezes, R. S. C., Sampaio, E. V. D. S. B., Neto, D. E. S., Tabosa, J. N., de Oliveira, L. R., Simões, A. L., & Sales, A. T. (2019). Allometric equations to estimate sugarcane aboveground biomass. Sugar Tech, 21(6), 1039-1044. https://doi.org/10.1007/s12355-019-00741-w
de Souza, C. H. W., Lamparelli, R. A. C., Rocha, J. V. & Magalhães, P. S. G. (2017). Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds. International journal of remote sensing, 38(8-10), 2218-2230. https://doi.org/10.1080/01431161.2017.1285082
Eggleston, G. & Lima, I. (2015). Sustainability issues and opportunities in the sugar and sugar-bioproduct industries. Sustainability, 7, 12209-12235. https://doi.org/10.3390/su70912209
Field Crops Research Institute. (1983). Advice on planting crops. Department of Agriculture, Ministry of Agriculture and Cooperatives, Bangkok.
Garside, A. L. & Bell, M. J. (2009). Row spacing and planting density effects on the growth and yield of sugarcane. 3. Responses with different cultivars. Crop and Pasture Science, 60(6), 555-565. https://doi.org/10.1071/CP08313
Gunnula, W., Kosittrakun, M., Righetti, T. L. Weerathaworn, P. & Prabpan, M. (2012). Evaluating sugarcane growth and maturity using ground-based measurements and remote sensing data. Thai Journal of Agricultural Science, 45(1), 17-28.
Han, L., Yang, G., Dai, H., Xu, B., Yang, H., Feng, H., Li, Z. & Yang, X. (2019). Modeling maize above-ground biomass based on machine learning approaches using UAV remote sensing data. Plant Methods, 15(1), 1-19. https://doi.org/10.1186/s13007-019-0394-z
Herwitz, S. R., Johnson, L. F., Dunagan, S. E., Higgins, R. G., Sullivan, D. V., Zheng, J., Lobitz, B. M., Leung, J. G., Gallmeyer, B. A., Aoyagi, M., Slye, R. E. & Brass, J. A. (2004). Imaging from an unmanned aerial vehicle: Agricultural surveillance and decision support. Computers and Electronics in Agriculture, 44(1), 49-61. https://doi.org/10.1016/j.compag.2004.02.006
Manivong, P. & Bourgois, E. (2017). White paper: Thai sugarcane sector and sustainability. https://www.bonsucro.com/wp-content/uploads/2017/08/Thai-White-Paper-FINAL-LowRes.docx.pdf
Daud, S. M. S. M., Yusof, M. Y. P. M., Heo, C. C., Khoo, L. S., Singh, M. K. C., Mahmood, M. S. & Nawawi, H. (2022). Applications of drone in disaster management: a scoping review. Science & Justice, 62(1), 30-42. https://doi.org/10.1016/j.scijus.2021.11.002
Na, S., Park, C. & Lee, K. (2016). Application of highland kimchi cabbage status map for growth monitoring based on unmanned aerial vehicle. Korean Journal of Soil Science and Fertilizer, 49(5), 469-479. https://doi.org/10.7745/KJSSF.2016.49.5.469
Nodthaisong, C., Saengprachathanarug, K., Chea, C., Posom, J., Wongpichet, S., Konyai, S. & Wongphati, M. (2019). Feasibility study of sugarcane yield prediction using NDVI, CIred edge indices associated with volume of digital surface model (DSM). Khon Kaen Agriculture Journal, 47(4), 679-694.
Office of the Cane and Sugar Board. (2015). Sustainable sugarcane farm management guide. http://www.ocsb.go.th/upload/journal/fileupload/144-4003.pdf
Office of the Cane and Sugar Board. (2024). Cane area and yield. https://www.ocsb.go.th/report_area_yield/
Ogunsola, A., Zancaner, A. & Seed, B. (2021). The sugar series: the top 10 sugar producing countries in the world. https://www.czarnikow.com/blog/the-sugar-series-the-top-10-sugar-producing-countries-in-the-world
Ormzubsin, V., Sasiprapa, W., Cheukittisak, R., Maliphan, A. & Jungpol, P. (2019). Water use efficiency and physiological responses of advanced sugarcane clones under rainfed condition. Khon Kaen Agriculture Journal, 47(Suppl. 1), 663-670.
Pati, P. K., Kaushik, P., Khan, M. L. & Khare, P. K. (2022). Allometric equations for biomass and carbon stock estimation of small diameter woody species from tropical dry deciduous forests: Support to REDD+. Trees, Forests and People, 9, Article 100289. https://doi.org/10.1016/j.tfp.2022.100289
Paul, K. I., Roxburgh, S. H., England, J. R., Ritson, P., Hobbs, T., Brooksbank, K., Raison, R. J., Larmour, J. S., Murphy, S., Norris, J., Neumann, C., Lewis, T., Jonson, J., Carter, J. L., McArthur, G., Barton, C. & Rose, B. (2013). Development and testing of allometric equations for estimating above-ground biomass of mixed-species environmental plantings. Forest Ecology and Management, 310, 483-494. https://doi.org/10.1016/j.foreco.2013.08.054
Prasad, A. K., Chai, L., Singh, R. P. & Kafatos, M. (2006). Crop yield estimation model for lowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation, 8(1), 26-33. https://doi.org/10.1016/j.jag. 2005.06.002
Prasara-A, J. & Gheewala, S. H. (2016). Sustainability of sugarcane cultivation: case study of selected sites in north-eastern Thailand. Journal of Cleaner Production, 134, 613-622. https://doi.org/10.1016/j.jclepro.2015.09.029
Romeiras, M. M., Vieira, A., Silva, N. D., Moura, M., Santos-Guerra, A., Dora Batista, D., Duarte, M. C. & Paulo, O. S. (2016). Evolutionary and biogeographic insights on the macaronesian beta-Patellifolia species (Amaranthaceae) from a time-scaled molecular phylogeny. PLoS One, 11(3), Article e0152456. https://doi.org/10.1371/journal.pone.0152456
Rudorff, B. F. T., de Aguiar, D. A., da Silva, W. F., Sugawara, L. M., Adami, M. & Moreira, M. A. (2010). Studies on the rapid expansion of sugarcane for ethanol production in São Paulo State (Brazil) using Landsat data. Remote Sensing, 2(4), 1057-1076. https://doi.org/10.3390/rs2041057
Shahbandeh M. (2022). Sugar: exports of major countries 2021/2022. https://www.statista.com/statistics/273437/exported-amount-of-sugar-in-leading-countries/
Sofonia, J., Shendryk, Y., Phinn, S., Roelfsema, C., Kendoul, F. & Skocaj, D. (2019). Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry. International Journal of Applied Earth Observation and Geoinformation, 82, Article 101878. https://doi.org/10.1016/j.jag.2019.05.011
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), Article 4040. https://doi.org/10.3390/rs13204040
Som-ard, J., Hossain, M. D., Ninsawat, S. & Veerachitt, V. (2018). Pre-harvest sugarcane yield estimation using UAV-based RGB images and ground observation. Sugar Tech, 20, 645-657. https://doi.org/10.1007/s12355-018-0601-7
Sowcharoensuk, C. (2021). Industry Outlook 2021-2023: Sugar Industry. Bank of Ayudhya Public Co., Ltd. https://www.krungsri.com/th/research/industry/industry-outlook/ agriculture/sugar/IO/io-sugar-21
The ISAAN Record. (2019). Sweetness and power (5)- how Thailand became Southeast Asia’s sugar bowl. https://theisaanrecord.co/2019/09/11/sweetness-and-power-part-5-th/
Tsouros, D. C., Bibi, S. & Sarigiannidis, P. G. (2019). A review on UAV-based applications for precision agriculture. Information, 10(11), Article 349. https://doi.org/10.3390/info10110349
Vashum, K. T. & Jayakumar, S. (2012). Methods to estimate above-ground biomass and carbon stock in natural forests-a review. Journal of Ecosystem and Ecography, 2(4), 1-7. https://doi.org/10.4172/2157-7625.1000116
Wall, L., Larocque, D. & Leger, P.M. (2007). The early explanatory power of NDVI in crop yield modeling. International Journal of Remote Sensing, 29, 2211-2225. https://doi.org/10.1080/01431160701395252
Xiang, H. & Tian, L. (2011). Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosystems Engineering, 108(2), 174-190. https://doi.org/10.1016/j.biosystemseng.2010.11.010
Xiaoqin, W., Miaomiao, W., Shaoqiang, W., & Yundong, W. (2015). Extraction of vegetation information from visible unmanned aerial vehicle images. Transactions of the Chinese Society of Agricultural Engineering, 31(5), 152-159. https://doi.org/10.3969/j.issn. 1002-6819.2015.05.022
Youkhana, A. H., Ogoshi, R. M., Kiniry, J. R., Meki, M. N., Nakahata, M. H. & Crow, S. E. (2017). Allometric models for predicting aboveground biomass and carbon stock of tropical perennial C4 grasses in Hawaii. Frontiers in Plant Science, 8, 650-659. https://doi.org/10.3389/fpls.2017.00650
Yu, D., Zha, Y., Shi, L., Jin, X., Hu, S., Yang, Q., Huang, K. & Zeng, W. (2020). Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations. European Journal of Agronomy, 121, Article 126159. https://doi.org/10.1016/j.eja.2020.126159