Stefani, Olivia (2024) Pengembangan Model Prediksi Hasil Panen Jagung Berbasis Citra Satelit Menggunakan Model Indeks Vegetasi dan Klorofil Daun (Studi Kasus : Kabupaten Tuban). Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Prediksi hasil panen yang akurat pada suatu komoditas penting untuk memperkuat ketahanan pangan nasional. Salah satu komoditas strategis Indonesia adalah komoditas jagung. Penghasil komoditas jagung tertinggi di Indonesia adalah Jawa Timur, dimana Kabupaten Tuban merupakan penghasil jagung tertinggi di Jawa Timur. Pemantauan perkembangan jagung penting dilakukan demi produktivitas tanaman jagung yang maksimal. Oleh karena itu, penginderaan jauh merupakan teknologi yang cepat dan efisien dalam proses pematauan perkembangan dan produktivitas tanaman jagung. Penelitian ini bertujuan untuk mengetahui perubahan fase, sebaran varietas, model prediksi hasil panen, dan mengetahui model prediksi model prediksi terbaik tanaman jagung di Kabupaten Tuban. Penelitian ini terdiri dari beberapa tahap yaitu proses pembuatan data eksisting lahan jagung menggunakan metode Support Vector Machine (SVM) citra satelit Sentinel-2 Level 2A, selanjutnya proses fenologi menggunakan citra satelit MODIS MCD-43A4 dengan menghitung indeks spektral Normalized Difference Vegetation Index (NDVI) dan Normalized Difference Water Index (NDWI). Tahap ketiga yaitu proses pembuatan sebaran varietas tanaman jagung NK-6172, NK-212, dan NK-7328 fase generatif akhir menggunakan metode Linear Spectral Unmixing (LSU) citra satelit Sentinel-2 Level 2A. Tahap terakhir yaitu proses pembuatan model prediksi panen menggunakan metode regresi linear antara hasil panen per luasan (kg/m2 ) dengan indeks vegetasi Two-band Enhanced Vegetation Index (EVI2), indeks klorofil Green Chlorophyll Vegetation Index (GCVI), dan data klorofil hasil pengukuran lapangan menggunakan Soil Plant Analysis Development (SPAD). Hasil yang diperoleh dari penelitian ini yaitu, pertama, diperoleh kalender tanam tanaman jagung, dimana terdapat dua kali periode tanam jagung yang dilakukan, yaitu Bulan Maret hingga Juni dan November hingga Maret. Kedua, diperoleh peta sebaran fraksi endmember dan varietas dominan tanaman jagung, luas terbesar yang diperoleh yaitu varietas NK-6172 yaitu 80,97%. Varietas jagung terbanyak kedua adalah NK-7328 yaitu 15,15%. Varietas jagung paling sedikit yaitu NK-212 yaitu 3,48%. Ketiga, diperoleh tiga model prediksi hasil panen jagung menggunakan EVI2, GCVI, dan klorofil, dimana model terbaik pertama yaitu model pertama (EVI2) dengan R2 = 0,9304, r = 0,96, dan MSE = 0,0005. Model terbaik kedua yaitu model kedua (GCVI) dengan R2 = 0,8184, r = 0,90, dan MSE = 0,0013. Model terakhir adalah model ketiga (klorofil) dengan R2 = 0,7582, r = 0,87, dan MSE = 0,0021.
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Accurate prediction of crop yields in a commodity is important to enhance national food security. One of Indonesia's strategic commodities is corn. The highest corn producer in Indonesia is East Java, where Tuban Regency is the highest corn producer in East Java. Monitoring the development of corn is important for ensuring maximum productivity of the corn yield. Therefore, remote sensing is a fast and efficient technology in the process of monitoring the development and productivity of corn crops. This research aims to determine phase changes, cultivar distribution, yield prediction models, and determine the best prediction model for corn crops in Tuban Regency. This research consists of several steps, such as the process of creating existing data on corn fields using the Support Vector Machine (SVM) method of Sentinel-2 Level 2A satellite imagery, then the phenology process using MODIS MCD-43A4 satellite imagery by calculating the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) spectral indices. The third step is the process of making the distribution of corn plant Cultivars NK-6172, NK-212, and NK-7328 in the late generative phase using the Linear Spectral Unmixing (LSU) method of Sentinel-2 Level 2A satellite imagery. The last step is the process of making a harvest prediction model using the linear regression method between yield per area (kg/m2) with the Two-band Enhanced Vegetation Index (EVI2) vegetation index, the Green Chlorophyll Vegetation Index (GCVI) chlorophyll index, and chlorophyll data from ground measurements using Soil Plant Analysis Development (SPAD). The results obtained from this study are, first, obtained a planting calendar for corn plants, where there are two periods of corn planting carried out, from March to June and November to March. Second, the distribution map of endmember fractions and dominant varieties of corn plants is obtained, the largest area obtained is the NK�6172 cultivar, which is 80.97%. The second largest corn cultivar is NK-7328 which is 15.15%. The least corn cultivar is NK-212 which is 3.48%. Third, three models of corn yield prediction were obtained using EVI2, GCVI, and chlorophyll, where the first best model is the first model (EVI2) with R2 = 0.9304, r = 0.96, and MSE = 0.0005. The second is the second model (GCVI) with R2 = 0.8184, r = 0.90, and MSE = 0.0013. The last model is the third model (chlorophyll) with R2 = 0.7582, r = 0.87, and MSE = 0.0021.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Kata kunci : Endmember, fenologi, jagung, prediksi panen, varietas ============================================================ Keywords: Cultivar, corn, endmember, phenology, yeild prediction |
| Subjects: | S Agriculture > S Agriculture (General) > S600.7.P53 Planting time G Geography. Anthropology. Recreation > G Geography (General) > G70.5.I4 Remote sensing |
| Divisions: | Faculty of Civil Engineering and Planning > Geomatics Engineering > 29101-(S2) Master Thesis |
| Depositing User: | Olivia Stefani |
| Date Deposited: | 31 Jul 2024 02:17 |
| Last Modified: | 31 Jul 2024 02:17 |
| URI: | http://repository.its.ac.id/id/eprint/110079 |
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