Monitoring Kesehatan Tanaman Berbasis Remote Sensing dalam Upaya Kemandirian Produktifitas Pertanian Kabupaten Ponorogo

Isi Artikel Utama

Eko Yuli Handoko
Irena Hana Hariyanto
Candida A.D.S Nusantara
Muhammad Aldila Syariz
Achmad Fahriza
Akbar Kurniawan

Abstrak

Monitoring Kesehatan tanaman pertanian penting dilakukan utamanya pada daerah produktif seperti Kabupaten Ponorogo. Mengingat area yang cukup luas, metode in-situ kurang tepat dilakukan karena membutuhkan biaya dan waktu yang besar. Oleh karena itu, kegiatan pengabdian kali ini akan membantu masyarakat dalam monitoring kesehatan tanaman utamanya pertanian dengan memanfaatkan teknologi penginderaan jauh. Pemrosesan data melalui Growth and Yield Processing dan Crop Diseases and Pest Infestation pada citra Satelit Sentinel-2A menghasilkan kelas Kesehatan tanaman pada vegetasi pertanian. Selain itu, penggunaan data sekunder dan ground truth melengkapi pemrosesan sehingga menghasilkan gambaran lainnya meliputi kondisi vegetasi non-pertanian dan nonvegetasi di Kabupaten Ponorogo. Hasil akhir kegiatan pengabdian kepada masyarakat ini adalah berupa platform informasi berbasis web bernama Sistem Aplikasi Monitoring Kesehatan Tanaman (SAMIKNA). Platform ini membantu para pengguna, baik petani, komunitas, maupun masyarakat dalam memantau kesehatan tanaman di Kabupaten Ponorogo secara real-time, interaktif, dan efisien.

Rincian Artikel

Cara Mengutip
Handoko, E. Y., Hariyanto , I. H., Nusantara , C. A., Syariz, M. A., Fahriza , A., & Kurniawan, A. (2026). Monitoring Kesehatan Tanaman Berbasis Remote Sensing dalam Upaya Kemandirian Produktifitas Pertanian Kabupaten Ponorogo. Sewagati, 10(3), 540–553. https://doi.org/10.12962/j26139960.v10i3.7684
Bagian
Articles

Referensi

1. Malau LRE, Rambe KR, Ulya NA, Purba AG. Dampak Perubahan Iklim Terhadap Produksi Tanaman Pangan di Indonesia. Jurnal Penelitian Pertanian Terapan 2023;23(1):34–46.

2. Siregar FA. Penggunaan Pupuk Organik dalam Meningkatkan Kualitas Tanah dan Produktivitas Tanaman. OSF Preprints; 2023. https://doi.org/10.31219/osf.io/fyz8v.

3. Rahmanto Y, Rifaini A, Samsugi S, Riskiono SD. Sistem Monitoring pH Air Pada Aquaponik Menggunakan Mikrokontroler Arduino UNO. Jurnal Teknologi Dan Sistem Tertanam 2020;1(1):23–28.

4. Nurhaliza DV, Novianti I, Rahman KR, Rozak RWA, Nurlela T, Sugiarti Y, et al. Dampak Perubahan Iklim Terhadap Ketahanan Pangan dan Gizi di Indonesia Demi Tercapainya Tujuan SDGs. Bulletin Agro Industri 2023;50(1):1–7.

5. Jannah SR, Hatta GM, Basir B. Kesehatan Tanaman Kayu Putih (Melaleuca Leucadendra Linn) di Lahan Rehabilitasi Daerah Aliran Sungai (DAS) Gunung Batu Desa Tebing Siring Pelaihari Kabupaten Tanah Laut. Jurnal Sylva Scienteae 2022;5(2):292–300.

6. Malau LRE, et al. Study of ENSO impact on agricultural food crops price as basic knowledge to improve community resilience in climate change. In: IOP Conference Series: Earth and Environmental Science; 2021. p. 1–11. https://doi.org/10.1088/1755-1315/874/1/012008.

7. Selvira, Safe’i R, Yuwono SB, Kaskoyo H. Nilai Indeks Kerusakan Pohon Karet (Hevea Brasiliensis) di Hutan Rakyat Kabupaten Tulang Bawang. Perennial 2022;18(1):1–6. http://dx.doi.org/10.24259/perennial.v18i1.18301.

8. Yuniasih B, Adjie ARP. Evaluasi Kondisi Kebun Kelapa Sawit Menggunakan Indeks NDVI dari Citra Satelit Sentinel 2. J Teknotan 2022;16(2):127–132.

9. Phiri D, Simwanda M, Salekin S, Nyirenda VR, Murayama Y, Ranagalage M. Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing 2020;12(14):2291. https://doi.org/10.3390/rs12142291.

10. Bodah BW, Neckel A, Stolfo Maculan L, Milanes CB, Korcelski C, Ramírez O, et al. Sentinel-5P TROPOMI satellite application for NO2 and CO studies aiming at environmental valuation. Journal of Cleaner Production 2022;357:131960. https://doi.org/10.1016/j.jclepro.2022.131960.

11. Rondeaux G, Steven M, Baret F. Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment 1996;55(2):95–107. https://doi.org/10.1016/0034-4257(95)00186-7.

12. Drusch M, Del Bello U, Carlier S, Colin O, Fernandez V, Gascon F, et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sensing of Environment 2012;120:25–36. https://doi.org/10.1016/j.rse.2011.11.026.

13. Segarra J, Buchaillot ML, Araus JL, Kefauver SC. Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agronomy 2020;10(5):641. https://doi.org/10.3390/agronomy10050641.

14. Curran PJ, Dungan JL, Macler BA, Plummer SE. The effect of a red leaf pigment on the relationship between red edge and chlorophyll concentration. Remote Sensing of Environment 1991;35(1):69–76. https://doi.org/10.1016/0034-4257(91)90066-f.

15. Phadikar S, Goswami J. Vegetation indices based segmentation for automatic classification of brown spot and blast diseases of rice. In: Proceedings of the 3rd International Conference on Recent Advances in Information Technology (RAIT); 2016. https://doi.org/10.1109/rait.2016.7507917. 552 Handoko, DKK.

16. Kanke Y, Tubaña B, Dalen M, Harrell D. Evaluation of red and red-edge reflectance-based vegetation indices for rice biomass and grain yield prediction models in paddy fields. Precision Agriculture 2016;17(5):507–530. https://doi.org/10.1007/s11119-016-9433-1.

17. Hashimoto N, Saito Y, Maki M, Homma K. Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields. Remote Sensing 2019;11(18):2119. https://doi.org/10.3390/rs11182119.

18. Zhou L, Chen N, Chen Z, Xing C. ROSCC: An Efficient Remote Sensing Observation-Sharing Method Based on Cloud Computing for Soil Moisture Mapping in Precision Agriculture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2016;9(12):5588–5598. https://doi.org/10.1109/JSTARS.2016.2574810.

19. Biró L, Kozma-Bognár V, Berke J. Comparison of RGB Indices used for Vegetation Studies based on Structured Similarity Index (SSIM). Journal of Plant Science and Phytopathology 2024;8(1):007–012. https://doi.org/10.29328/journal.jpsp.1001124.

20. Hassan MA, Yang M, Rasheed A, Yang G, Reynolds M, Xia X, et al. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science 2019;282:95–103. https://doi.org/10.1016/j.plantsci.2018.10.022.

21. Ihuoma SO, Madramootoo CA. Sensitivity of spectral vegetation indices for monitoring water stress in tomato plants. Computers and Electronics in Agriculture 2019;163:104860. https://doi.org/10.1016/j.compag.2019.104860.

22. Venancio LP, Mantovani EC, do Amaral CH, Usher Neale CM, Gonçalves IZ, Filgueiras R, et al. Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI). Agricultural Water Management 2019;225:105779. https://doi.org/10.1016/j.agwat.2019.105779.

23. Gao Y, Walker JP, Allahmoradi M, Monerris A, Ryu D, Jackson TJ. Optical Sensing of Vegetation Water Content: A Synthesis Study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2015;8(4):1456–1464. https://doi.org/10.1109/jstars.2015.2398034.

24. Meng J, Xu J, You X. Optimizing soybean harvest date using HJ-1 satellite imagery. Precision Agriculture 2014;16(2):164–179. https://doi.org/10.1007/s11119-014-9368-3.

25. Martínez-Casasnovas JA, Uribeetxebarría A, Escolà A, Arnó J. Sentinel-2 vegetation indices and apparent electrical conductivity to predict barley (Hordeum vulgare L.) yield. In: Precision Agriculture 2019 Wageningen, The Netherlands: Wageningen Academic Publishers; 2019.p. 415–421. https://doi.org/10.3920/978-90-8686-888-9_38.

26. DadrasJavan F, Samadzadegan F, Seyed Pourazar SH, Fazeli H. UAV-based multispectral imagery for fast Citrus Greening detection. Journal of Plant Diseases and Protection 2019;126(4):307–318. https://doi.org/10.1007/s41348-019-00234-8.

27. Abdulridha J, Ampatzidis Y, Kakarla SC, Roberts P. Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques. Precision Agriculture 2019;https://doi.org/10.1007/s11119-019-09703-4.

28. Ballester C, Zarco-Tejada PJ, Nicolás E, Alarcón JJ, Fereres E, Intrigliolo DS, et al. Evaluating the performance of xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species. Precision Agriculture 2017;19(1):178–193. https://doi.org/10.1007/s11119-017-9512-y.

29. Amaral LR, Molin JP, Portz G, Finazzi FB, Cortinove L. Comparison of crop canopy reflectance sensors used to identify sugarcane biomass and nitrogen status. Precision Agriculture 2014;16(1):15–28. https://doi.org/10.1007/s11119-014-9377-2.

30. Pourazar H, Samadzadegan F, Dadrass Javan F. Aerial multispectral imagery for plant disease detection: radiometric calibration necessity assessment. European Journal of Remote Sensing 2019;52(sup3):17–31. https://doi.org/10.1080/22797254.2019.1642143.

31. Mudereri BT, Dube T, Adel-Rahman EM, Niassy S, Kimathi E, Khan Z, et al. A comparative analysis of PlanetScope and Sentinel-2 space-borne sensors in mapping Striga weed using Guided Regularised Random Forest classification ensemble. Handoko, DKK. 553 In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42; 2019. p. 701–708. https://doi.org/10.5194/isprs-archives-xlii-2-w13-701-2019.

32. Zhang PP, Zhou XX, Wang ZX, Mao W, Li WX, Yun F, et al. Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat. Scientific Reports 2020;10(1). https://doi.org/10.1038/s41598-020-62125-5.

Artikel paling banyak dibaca berdasarkan penulis yang sama