ANALISIS HUBUNGAN NILAI SPAD DAN INDEKS VEGETASI BERBASIS CITRA MULTISPEKTRAL UNMANNED AERIAL VEHICLE VERTICAL TAKE-OFF AND LANDING (UAV-VTOL) PADA TANAMAN PEPAYA

Mutiara Alifia Ramadhanty; Ardan Wiratmoko; Fahmi Arsyad; Andri Prima Nugroho; Lilik Sutiarso; Takashi Okayasu

doi:10.35760/jpp.2026.v10i1.261

Penulis

Mutiara Alifia Ramadhanty Universitas Gadjah Mada Penulis
Ardan Wiratmoko Universitas Gadjah Mada Penulis
Fahmi Arsyad Universitas Gadjah Mada Penulis
Andri Prima Nugroho Universitas Gadjah Mada Penulis
Lilik Sutiarso Universitas Gadjah Mada Penulis
Takashi Okayasu Kyushu University Penulis

DOI:

https://doi.org/10.35760/jpp.2026.v10i1.261

Kata Kunci:

Citra multispektral UAV, Indeks vegetasi, Klorofil daun

Abstrak

Monitoring kandungan klorofil daun tanaman secara spasial penting dalam pertanian presisi, namun pengukuran konvensional memiliki keterbatasan cakupan dan efisiensi. Penelitian ini bertujuan menganalisis hubungan nilai SPAD dengan tiga indeks vegetasi berbasis citra multispectral (NDVI, GNDVI, NDRE) pada tanaman pepaya (Carica papaya L.) varietas California. Data diperoleh dari citra multispectral UAV dan pengukuran SPAD pada 95 titik sampel di kebun pepaya komersial di Sleman, Yogyakarta. Analisis meliputi evaluasi distribusi data, korelasi Pearson dan Spearman, regresi linear sederhana, serta sensitivitas berdasarkan kemiringan regresi. Hasil menunjukkan nilai SPAD memiliki rentang 18.46–77.78, mencerminkan variabilitas kandungan klorofil tanaman di area penelitian. NDVI mengalami saturasi pada nilai tinggi (skewness = −1.35), sedangkan GNDVI dan NDRE memiliki distribusi lebih normal. Analisis korelasi menunjukkan NDRE memiliki hubungan paling kuat dengan SPAD (r = 0.73; R² = 0.54), diikuti GNDVI (r = 0.72; R² = 0.52) dan NDVI (r = 0.54; R² = 0.29). NDRE juga menghasilkan nilai error prediksi terendah (MAE = 6.42; RMSE = 8.15). Temuan ini menunjukkan bahwa NDRE merupakan indeks vegetasi paling optimal untuk monitoring klorofil tanaman pepaya berbasis citra multispectral UAV, sehingga berpotensi mendukung pengembangan sistem pemantauan nutrisi tanaman dalam kerangka pertanian presisi.

Unduhan

Data unduhan tidak tersedia.

Biografi Penulis

Mutiara Alifia Ramadhanty, Universitas Gadjah Mada

Researcher affiliated with the Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada
Ardan Wiratmoko, Universitas Gadjah Mada

Lecturer and researcher at the Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada
Fahmi Arsyad, Universitas Gadjah Mada

Researcher affiliated with the Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada
Andri Prima Nugroho, Universitas Gadjah Mada

Academic and researcher at the Smart Agriculture Research Center, Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada
Lilik Sutiarso, Universitas Gadjah Mada

Professor at the Department of Agricultural and Biosystems Engineering, Faculty of Agricultural Technology, Universitas Gadjah Mada
Takashi Okayasu, Kyushu University

Professor at the Department of Agro-Environmental Science, Faculty of Agriculture, Kyushu University

Referensi

Afif, S., Wiratmoko, A., Nugroho, A.P., Okayasu, T., Sutiarso, L., 2025. Design of Smart Plant Electrical Signal Monitoring System for Indoor Farming. BIO Web Conf. 167, 05004. https://doi.org/10.1051/bioconf/202516705004

Ahmad, A. (2025). Integrating Uav-Derived VARI, GNDVI, NDVI, and NDRE for phenological rice growth analysis. ARPN Journal of Engineering and Applied Sciences, 1572. https://doi.org/10.59018/0925178

An, G., Xing, M., He, B., Liao, C., Huang, X., Shang, J., & Kang, H. 2020. Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data. Remote Sensing, 12(18), 3104. https://doi.org/10.3390/rs12183104

Daliman, S., Anak Michael, M. J., Rendra, P. P. R., Sukiyah, E., Hadian, M. S. D., & Sulaksana, N. 2024. Dual Vegetation Index Analysis and Spatial Assessment in Kota Bharu, Kelantan using GIS and Remote Sensing. BIO Web of Conferences, 131, 05009. https://doi.org/10.1051/bioconf/202413105009

Furnitto, N., Ramírez-Cuesta, J. M., Sottosanti, G., Longo, D., Schillaci, G., & Failla, S. 2024. Potentiality of Multispectral Vegetation Indexes for Evaluating the Influence of the Sowing Technique on Durum Wheat Cultivation Density. pp. 313–321. https://doi.org/10.1007/978-3-031-63504-5_32

Gitelson, A., Kaufman, Y. J., Merzlyak, M. N. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298. https://doi.org/10.1016/S0034-4257(96)00072-7

Gitelson, A., Merzlyak, M. N. 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: Experiments with autumn chestnut and maple leaves. Journal of Photochemistry and Photobiology B: Biology, 22, 247–252. https://doi.org/10.1016/1011-1344(93)06963-4

Haque, M. A., Reza, M. N., Ali, M., Karim, M. R., Ahmed, S., Lee, K.-D., Khang, Y. H., & Chung, S.-O. 2024. Effects of Environmental Conditions on Vegetation Indices from Multispectral Images: A Review. Korean Journal of Remote Sensing, 40(4), 319–341

Harish, K., Dilip, R., Gupta, D., & Nair, A. R. 2025. Assessing NDVI Through Environmental and Urbanization Variables Using Machine Learning Techniques. 2025 IEEE 4th International Conference for Advancement in Technology (ICONAT), 1–6. https://doi.org/10.1109/ICONAT66879.2025.11362457

Hu, R., Chen, X., Chen, J., Zhang, S., Kuang, Y., Yu, H., Ji, H., Zhao, X., Yi, S., & Meng, B. 2024. MODIS NDVI saturation assessment of alpine meadow grassland biomass estimation using remote sensing: a case study in the eastern edge of the Qinghai-Tibet Plateau. Shengtai Xuebao, 44(14), 6357–6372

Itaya, K., Ohsumi, A., Sawada, H., Mizumoto, A., & Fukushima, A. 2022. Relationships between Indexes Related to Vegetation Measured by Ground Sensing and Growth Traits in a Wheat Cultivar ‘Satonosora’. Japanese Journal of Crop Science, 91(4), 356–364. https://doi.org/10.1626/jcs.91.356

Kalaiselvan, M., & Senthil Kumar, K. 2023. Design and Development of Multi-copter Drone Incorporating with Multispectral Sensor for Agricultural Application. pp. 215–226. https://doi.org/10.1007/978-981-19-2358-6_21

Khalesi, F., Ahmed, I., Daponte, P., Picariello, F., de Vito, L., & Tudosa, I. (2024). The Uncertainty Assessment by the Monte Carlo Analysis of NDVI Measurements Based on Multispectral UAV Imagery. Sensors, 24(9), 2696. https://doi.org/10.3390/s24092696

Lei, X.-X., Zhao, J., Liu, H.-C., Zhang, J.-Y., Tian, J.-L., & Long, Y.-B. 2019. Inversion of Chlorophyll Content and SPAD Value of Vegetable Leaves Based on PROSPECT Model. Spectroscopy and Spectral Analysis, 39(10), 3256–3260

Li, F., Mistele, B., Hu, Y., Yue, X., Yue, S., Miao, Y., Chen, X., Cui, Z., Meng, Q., & Schmidhalter, U. 2012. Remotely estimating aerial N status of phenologically differing winter wheat cultivars grown in contrasting climatic and geographic zones in China and Germany. Field Crops Research, 138, 21–32. https://doi.org/10.1016/j.fcr.2012.09.002

Li, Z., Ma, Z., Bao, R., Fang, W., Tang, Q., Yu, E., Song, X., & Tan, C. 2026. Exploring the hyperspectral response of leaf chlorophyll content under canopy structure and soil background variability. European Journal of Agronomy, 175, 127982. https://doi.org/10.1016/j.eja.2026.127982

Nugroho, A.P., Wiratmoko, A., Nugraha, D., Markumningsih, S., Sutiarso, L., Falah, M.A.F., Okayasu, T., 2025. Development of a low-cost thermal imaging system for water stress monitoring in indoor farming. Smart Agricultural Technology 11, 101048. https://doi.org/10.1016/j.atech.2025.101048

Peng, Y., & Gitelson, A. A. 2012. Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sensing of Environment, 117, 440–448. https://doi.org/10.1016/j.rse.2011.10.021

Qian, X., Liu, L., Croft, H., & Chen, J. 2021. Relationship Between Leaf Maximum Carboxylation Rate and Chlorophyll Content Preserved Across 13 Species. Journal of Geophysical Research: Biogeosciences, 126(2). https://doi.org/10.1029/2020JG006076

Rodríguez Sánchez, A., Salmerón Gómez, R., & García, C. (2022). The coefficient of determination in the ridge regression. Communications in Statistics - Simulation and Computation, 51(1), 201–219. https://doi.org/10.1080/03610918.2019.1649421

Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W., & Harlan, J. C. 1974. Monitoring the vernal advancements and retrogradation of natural vegetation. NASA/GSFC Final Report

Rodríguez Sánchez, A., Salmerón Gómez, R., & García, C. (2022). The coefficient of determination in the ridge regression. Communications in Statistics - Simulation and Computation, 51(1), 201–219. https://doi.org/10.1080/03610918.2019. 1649421

Sayili, U., & Gunver, M. G. (2025). A novel modification approach for the one sample Kolmogorov-Smirnov test in large sample size. Scandinavian Journal of Clinical and Laboratory Investigation, 85(4), 287–298. https://doi.org/10.1080/00365513.2025.2512384

Sharma, P., & Dadheech, P. 2026. Evaluating Vegetation Indices for Crop Monitoring Using Multispectral Satellite Imagery. pp. 197–206. https://doi.org/10.1007/978-3-031-95540-2_18

Singh, S., Pandey, P., Khan, M. S., & Semwal, M. 2021. Multi-temporal High Resolution Unmanned Aerial Vehicle (UAV) Multispectral Imaging for Menthol Mint Crop Monitoring. 2021 6th International Conference for Convergence in Technology (I2CT), 1–4. https://doi.org/10.1109/I2CT51068.2021.9418204

Venkataanusha, P., Anuradha, Ch., Chandra Murty, Dr. P. S. R., & Chebrolu, Dr. S. K. (2019). Detecting Outliers in High Dimensional Data Sets Using Z-Score Methodology. International Journal of Innovative Technology and Exploring Engineering, 9(1), 48–53. https://doi.org/10.35940/ijitee.A3910.119119

Wang, D., Su, A., & Liu, W. 2019. Trend Analysis of Vegetation Cover Changes Based on Spearman Rank Correlation Coefficient. Journal of Applied Sciences, 37(4), 519–528

Wang, S., Li, Y., Ju, W., Chen, B., Chen, J., Croft, H., Mickler, R. A., & Yang, F. 2020. Estimation of Leaf Photosynthetic Capacity from Leaf Chlorophyll Content and Leaf Age in a Subtropical Evergreen Coniferous Plantation. Journal of Geophysical Research: Biogeosciences, 125(2). https://doi.org/10.1029/2019JG005020

Wijayanto, Y., Safitri, M., Purnamasari, I., Budiman, S. A., Saputra, T. W., Regar, A. F., & Ristiyana, S. (2024). Estimating the contents of Chlorophyll, Nitrogen, and Yields on Rice through Sentinel-2 Vegetation Indices in Heterogeneous Land Management. Indonesian Journal of Geography, 56(3). https://doi.org/10.22146/ijg.87159

Wiratmoko, A., Nugroho, A. P., Afif, S., Arsyad, F., Ramadhanty, M. A., & Sutiarso, L. 2025. Analisis Dinamika Vapor Pressure Deficit pada Lingkungan Mikroklimat Padi Sawah Terbuka Berbasis Smart Automatic Weather Station Tipe Ultrasonik. Journal of Agricultural and Biosystem Engineering Research, 6(2), 118. https://doi.org/10.20884/1.jaber.2025.6.2.19697

Wiratmoko, A., Nugroho, A. P., Muna, M. S., Syarovy, M., Suwardi, Sukarman, & Sutiarso, L. 2023. Development of Cloud-Based Decision Support System for Fertilizer Management - A Case Study in Wilmar Oil Palm Plantation. ICOSEAT 2022 Proceedings, 26. https://doi.org/10.2991/978-94-6463-086-2_69

Wiratmoko, A., Nugroho, A.P., Ramadhanty, M.A., Arsyad, F., Pradana, F.A., Pamungkas, B.S., Sutiarso, L., Okayasu, T., 2026. Predicting Physiological Health States of Tropical Papaya Using UAV Multispectral Imagery for Precision Agriculture Monitoring. Agricultural Environment and Sustainability 100023. https://doi.org/10.1016/j.ages.2026.100023

Zhang, Y., Ru, G., Zhao, Z., & Wang, D. 2024. Hyperspectral Prediction Models of Chlorophyll Content in Paulownia Leaves under Drought Stress. Sensors, 24(19), 6309. https://doi.org/10.3390/s24196309