Pemanfaatan Remote Sensing dalam Peningkatan Kualitas Data Pertanian: Systematic Literature Review

Bill Van Ricardo Zalukhu; Fitri Noor Hikmah

doi:10.4590/jarsic.v4i1.56

Authors

Bill Van Ricardo Zalukhu
Fitri Noor Hikmah

DOI:

https://doi.org/10.4590/jarsic.v4i1.56

Keywords:

Pertanian, Remote Sensing, Systematic Literatur Review

Abstract

Pertanian menjadi salah satu sektor yang cukup vital, di mana sektor ini memainkan peran dalam pemenuhan kebutuhan manusia, baik dari sisi pangan maupun ekonomi. Hal tersebut mengindikasikan bahwa produk dari sektor pertanian harus dikontrol kualitasnya agar memiliki nilai ekonomi yang cukup memadai untuk menggerakkan perekonomian dan persediaannya harus bisa mencukupi kebutuhan, baik dari pemerintah maupun rumah tangga. Pengontrolan kualitas dan persediaan dari sektor ini hanya dapat dipantau secara akurat dengan data yang baik, relevan, dan akurat sesuai dengan fakta yang ada di lapangan. Saat ini, metode konvensional seperti survei dan sensus masih digunakan, namun dalam melakukan kegiatan tersebut dibutuhkan waktu yang cukup lama, biaya yang cukup besar, dan sumber daya yang cukup mahal. Remote sensing merupakan metode yang cost-effective, di mana metode ini dapat digunakan untuk mengumpulkan data yang lebih up to date, dan tidak membutuhkan resource yang terlalu mahal. Dengan memanfaatkan kelebihan dari remote sensing untuk saling melengkapi dengan metode konvensional, peningkatan kualitas data pertanian akan semakin lebih baik.

References

Abbas, M., Saleem, S., Subhan, F., & Bais, A. (2020). Feature points-based image registration between satellite imagery and aerial images of agricultural land. Turkish Journal of Electrical Engineering and Computer Sciences, 28(3), 1458–1473. https://doi.org/10.3906/elk-1907-92

Ahmed, Z., Shew, A., Nalley, L., Popp, M., Green, V. S., & Brye, K. (2023). An examination of thematic research, development, and trends in remote sensing applied to conservation agriculture. In International Soil and Water Conservation Research. KeAi Communications Co. https://doi.org/10.1016/j.iswcr.2023.04.001

Becker, A., Russo, S., Puliti, S., Lang, N., Schindler, K., & Wegner, J. D. (2023). Country-wide retrieval of forest structure from optical and SAR satellite imagery with deep ensembles. ISPRS Journal of Photogrammetry and Remote Sensing, 195, 269–286. https://doi.org/10.1016/j.isprsjprs.2022.11.011

Bhattacharya, S., Lal, H., & Sachdev, B. K. (2021). A Study on the Agriculture Sector and the Problems Associated with it which has an Impact on the Farmers. International Journal of Trend in Scientific Research and Development (IJTSRD), 5(6), 589–593. https://www.researchgate.net/publication/362124853

Bhatti, M. A., Zeeshan, Z., M.S., S., Bhatti, U. A., Khan, A., Ghadi, Y. Y., Alsenan, S., Li, Y., Asif, M., & Afzal, T. (2024). Advanced Plant Disease Segmentation in Precision Agriculture Using Optimal Dimensionality Reduction With Fuzzy C-Means Clustering and Deep Learning. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 18264–18277. https://doi.org/10.1109/JSTARS.2024.3437469

Brown, L. A., Fernandes, R., Djamai, N., Meier, C., Gobron, N., Morris, H., Canisius, F., Bai, G., Lerebourg, C., Lanconelli, C., Clerici, M., & Dash, J. (2021). Validation of baseline and modified Sentinel-2 Level 2 Prototype Processor leaf area index retrievals over the United States. ISPRS Journal of Photogrammetry and Remote Sensing, 175, 71–87. https://doi.org/10.1016/j.isprsjprs.2021.02.020

Chen, G., Lu, H., Zou, W., Li, L., Emam, M., Chen, X., Jing, W., Wang, J., & Li, C. (2023). Spatiotemporal fusion for spectral remote sensing: A statistical analysis and review. In Journal of King Saud University - Computer and Information Sciences (Vol. 35, Issue 3, pp. 259–273). King Saud bin Abdulaziz University. https://doi.org/10.1016/j.jksuci.2023.02.021

Danner, M., Berger, K., Wocher, M., Mauser, W., & Hank, T. (2021). Efficient RTM-based training of machine learning regression algorithms to quantify biophysical & biochemical traits of agricultural crops. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 278–296. https://doi.org/10.1016/j.isprsjprs.2021.01.017

Dash, J. P., Watt, M. S., Pearse, G. D., Heaphy, M., & Dungey, H. S. (2017). Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 1–14. https://doi.org/10.1016/j.isprsjprs.2017.07.007

El-Gammal, M. I., Ali, R. R., & Eissa, R. (2014). Land use assessment of barren areas in Damietta Governorate, Egypt using remote sensing. Egyptian Journal of Basic and Applied Sciences, 1(3–4), 151–160. https://doi.org/10.1016/j.ejbas.2014.07.002

Felegari, S., Sharifi, A., Moravej, K., Amin, M., Golchin, A., Muzirafuti, A., Tariq, A., & Zhao, N. (2021). Integration of sentinel 1 and sentinel 2 satellite images for crop mapping. Applied Sciences (Switzerland), 11(21). https://doi.org/10.3390/app112110104

Friedman, E. A., & Gostin, L. O. (2016). The United Nations Sustainable Development Goals: Achieving The United Nations Sustainable Development Goals: Achieving the Vision of Global Health with Justice the Vision of Global Health with Justice. The Georgetown Public Policy Review, 21. https://scholarship.law.georgetown.edu/facpub/1777http://ssrn.com/abstract=2773616

Gallego, F. J., Kussul, N., Skakun, S., Kravchenko, O., Shelestov, A., & Kolotii, A. (2016). Efficiency assessment of using satellite data for crop area estimation in Ukraine. International Journal of Applied Earth Observation and Geoinformation, 52, 337–348. https://doi.org/10.1016/j.jag.2016.06.011.

Guo, Y., Jia, X., & Paull, D. (2018). Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification. IEEE Transactions on Image Processing, 27(6), 3036–3048. https://doi.org/10.1109/TIP.2018.2808767

Jafarbiglu, H., & Pourreza, A. (2022). A comprehensive review of remote sensing platforms, sensors, and applications in nut crops. In Computers and Electronics in Agriculture (Vol. 197). Elsevier B.V. https://doi.org/10.1016/j.compag.2022.106844

Kementrian Perancangan Pembangunan Nasional. (2020). PEDOMAN TEKNIS PENYUSUNAN RENCANA AKSI TUJUAN PEMBANGUNAN BERKELANJUTAN (TPB)/ SUSTAINABLE DEVELOPMENT GOALS (SDGs) (V. Yulaswati, J. R. Primana, Oktorialdi, D. S. Wati, Maliki, A. N. S. Moeljono, P. B. Ali, A. Alhumami, W. S. Sulistyaningrum, T. D. Virgiyanti, Y. R. Hidayat, M. P. Saronto, L. Adypurnama, M. Cholifihani, M. Amalia, S. Yanti, N. H. Rahayu, P. Pandawangi, & E. C. Buana, Eds.; II). Kedeputian Bidang Kemaritiman dan Sumber Daya Alam, Kementerian Perencanaan Pembangunan Nasional/Badan Perencanaan Pembangunan Nasional.

Laamrani, A., Lara, R. P., Berg, A. A., Branson, D., & Joosse, P. (2018). Using a mobile device “app” and proximal remote sensing technologies to assess soil cover fractions on agricultural fields. Sensors (Switzerland), 18(3). https://doi.org/10.3390/s18030708

Lame, G. (2019). Systematic literature reviews: An introduction. Proceedings of the International Conference on Engineering Design, ICED, 2019-August, 1633–1642. https://doi.org/10.1017/dsi.2019.169

Levering, A., Marcos, D., & Tuia, D. (2021). On the relation between landscape beauty and land cover: A case study in the U.K. at Sentinel-2 resolution with interpretable AI. ISPRS Journal of Photogrammetry and Remote Sensing, 177, 194–203. https://doi.org/10.1016/j.isprsjprs.2021.04.020

Marshall, M., & Thenkabail, P. (2015). Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 108, 205–218. https://doi.org/10.1016/j.isprsjprs.2015.08.001

Mercier, A., Betbeder, J., Baudry, J., Le Roux, V., Spicher, F., Lacoux, J., Roger, D., & Hubert-Moy, L. (2020). Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages. ISPRS Journal of Photogrammetry and Remote Sensing, 163, 231–256. https://doi.org/10.1016/j.isprsjprs.2020.03.009

Morell-Monzó, S., Sebastiá-Frasquet, M. T., Estornell, J., & Moltó, E. (2023). Detecting abandoned citrus crops using Sentinel-2 time series. A case study in the Comunitat Valenciana region (Spain). ISPRS Journal of Photogrammetry and Remote Sensing, 201, 54–66. https://doi.org/10.1016/j.isprsjprs.2023.05.003

O’Connell, J., Bradter, U., & Benton, T. G. (2015). Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 109, 165–177. https://doi.org/10.1016/j.isprsjprs.2015.09.007

Opryshko, O., Pasichnyk, N., Kiktev, N., Dudnyk, A., Hutsol, T., Mudryk, K., Herbut, P., Łyszczarz, P., & Kukharets, V. (2024). European Green Deal: Satellite Monitoring in the Implementation of the Concept of Agricultural Development in an Urbanized Environment. Sustainability (Switzerland) , 16(7). https://doi.org/10.3390/su16072649

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Journal of Clinical Epidemiology, 134, 178–189. https://doi.org/10.1016/j.jclinepi.2021.03.001

Papoutsis, I., Bountos, N. I., Zavras, A., Michail, D., & Tryfonopoulos, C. (2023). Benchmarking and scaling of deep learning models for land cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 195, 250–268. https://doi.org/10.1016/j.isprsjprs.2022.11.012

Peng, J., Nieto, H., Neumann Andersen, M., Kørup, K., Larsen, R., Morel, J., Parsons, D., Zhou, Z., & Manevski, K. (2023). Accurate estimates of land surface energy fluxes and irrigation requirements from UAV-based thermal and multispectral sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 238–254. https://doi.org/10.1016/j.isprsjprs.2023.03.009

Pires, R. de P., Olofsson, K., Persson, H. J., Lindberg, E., & Holmgren, J. (2022). Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads. ISPRS Journal of Photogrammetry and Remote Sensing, 187, 211–224. https://doi.org/10.1016/j.isprsjprs.2022.03.004

Robertson, G. P. (2015). A sustainable agriculture? Daedalus, 144(4), 76–89. https://doi.org/10.1162/DAED_a_00355

Rogan, J., & Chen, D. M. (2004). Remote sensing technology for mapping and monitoring land-cover and land-use change. In Progress in Planning (Vol. 61, Issue 4, pp. 301–325). Elsevier Ltd. https://doi.org/10.1016/S0305-9006(03)00066-7

Safarov, F., Temurbek, K., Jamoljon, D., Temur, O., Chedjou, J. C., Abdusalomov, A. B., & Cho, Y. I. (2022). Improved Agricultural Field Segmentation in Satellite Imagery Using TL-ResUNet Architecture. Sensors, 22(24). https://doi.org/10.3390/s22249784

Samarkhanov, K., Abuduwaili, J., Samat, A., Ge, Y., Liu, W., Ma, L., Smanov, Z., Adamin, G., Yershibul, A., & Sadykov, Z. (2022). Dimensionality-Transformed Remote Sensing Data Application to Map Soil Salinization at Lowlands of the Syr Darya River. Sustainability (Switzerland), 14(24). https://doi.org/10.3390/su142416696

Santos, L. A., Ferreira, K. R., Camara, G., Picoli, M. C. A., & Simoes, R. E. (2021). Quality control and class noise reduction of satellite image time series. ISPRS Journal of Photogrammetry and Remote Sensing, 177, 75–88. https://doi.org/10.1016/j.isprsjprs.2021.04.014

Schulz, D., Yin, H., Tischbein, B., Verleysdonk, S., Adamou, R., & Kumar, N. (2021). Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 97–111. https://doi.org/10.1016/j.isprsjprs.2021.06.005

Selcuk, A. A. (2019). A Guide for Systematic Reviews: PRISMA. Turkish Archives of Otorhinolaryngology, 57(1), 57–58. https://doi.org/10.5152/tao.2019.4058

Shendryk, Y., Rist, Y., Ticehurst, C., & Thorburn, P. (2019). Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 157, 124–136. https://doi.org/10.1016/j.isprsjprs.2019.08.018

Sinha, P., Robson, A., Schneider, D., Kilic, T., Mugera, H. K., Ilukor, J., & Tindamanyire, J. M. (2020). The potential of in-situ hyperspectral remote sensing for differentiating 12 banana genotypes grown in Uganda. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 85–103. https://doi.org/10.1016/j.isprsjprs.2020.06.023

Sun, J., Yan, S., Alexandridis, T., Yao, X., Zhou, H., Gao, B., Huang, J., Yang, J., & Li, Y. (2024). Enhancing Crop Mapping through Automated Sample Generation Based on Segment Anything Model with Medium-Resolution Satellite Imagery. Remote Sensing, 16(9), 1505. https://doi.org/10.3390/rs16091505

Tesfaye, A. A., Osgood, D., & Aweke, B. G. (2021). Combining machine learning, space-time cloud restoration and phenology for farm-level wheat yield prediction. Artificial Intelligence in Agriculture, 5, 208–222. https://doi.org/10.1016/j.aiia.2021.10.002

United Nations Economic Commission for Europe (UNECE). (2014). Fundamental Principles of Official Statistics.

USGS. (2013). Advanced and Applied Remote Sensing of Environmental Conditions.

Verrelst, J., Rivera-Caicedo, J. P., Reyes-Muñoz, P., Morata, M., Amin, E., Tagliabue, G., Panigada, C., Hank, T., & Berger, K. (2021). Mapping landscape canopy nitrogen content from space using PRISMA data. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 382–395. https://doi.org/10.1016/j.isprsjprs.2021.06.017

Xiong, J., Thenkabail, P. S., Gumma, M. K., Teluguntla, P., Poehnelt, J., Congalton, R. G., Yadav, K., & Thau, D. (2017). Automated cropland mapping of continental Africa using Google Earth Engine cloud computing. ISPRS Journal of Photogrammetry and Remote Sensing, 126, 225–244. https://doi.org/10.1016/j.isprsjprs.2017.01.019

Yang, S., Li, L., Fei, S., Yang, M., Tao, Z., Meng, Y., & Xiao, Y. (2024). Wheat Yield Prediction Using Machine Learning Method Based on UAV Remote Sensing Data. Drones, 8(7). https://doi.org/10.3390/drones8070284

Zhang, X., Jia, W., Lu, S., & He, J. (2024). Ecological assessment and driver analysis of high vegetation cover areas based on new remote sensing index. Ecological Informatics, 82, 102786. https://doi.org/10.1016/j.ecoinf.2024.102786

Zhang, X., Wang, J., Henebry, G. M., & Gao, F. (2020). Development and evaluation of a new algorithm for detecting 30 m land surface phenology from VIIRS and HLS time series. ISPRS Journal of Photogrammetry and Remote Sensing, 161, 37–51. https://doi.org/10.1016/j.isprsjprs.2020.01.012