<![CDATA[Abstrak Pengukuran intesitas serangan penyakit blas biasanya dilakukan secara manual, sehingga diperlukan waktu lama dan ketelitian dalam mengidentifikasi. Hal tersebut dapat menghambat penanganan yang menyebabkan penyebaran semakin meluas sehingga beresiko terhadap penurunan produktivitas padi, maka diperlukan pengembangan mengenai pendugaan intesitas serangan melalui teknologi dengan menggunakan citra multispektral. Penelitian ini bertujuan untuk menentukan hubungan antara indeks vegetasi dengan intensitas serangan penyakit padi pada berbagai ketinggian akuisisi citra dan mendapatkan akurasi pendugaan intensitas serangan penyakit padi pada berbagai ketinggian. Sampel intesitas serangan penyakit diambil sebanyak 3 petak dimana perpetaknya diambil 5 titik secara diagonal, setiap titik terdapat 9 rumpun. Akuisisi citra pada ketinggian 15 meter, 30 meter, dan 45 meter menggunakan drone Phantom 4 yang dilengkapi dengan kamera multispektral. Mosaiking menggunakan software Agisoft dan normalisasi menggunakan photoshop. Analisis indeks vegetasi NDVI, SAVI, CIG menggunakan QGIS 2.28. Hasil penelitian menunjukkan hubungan indeks vegetasi dengan intesitas serangan penyakit blas pada berbagai ketinggian berkorelasi linier. Indeks vegetasi NDVI 15meter, SAVI 15meter dan 30meter, CIG 15meter, 30meter, dan 45meter berkorelasi sangat kuat. Indeks vegetasi NDVI 30meter, SAVI 45meter berkorelasi kuat dengan intesitas serangan penyakit, sedangkan NDVI ketinggian 45meter berkorelasi cukup kuat dengan intesitas serangan. NDVI 15meter memiliki akurasi paling tinggi sebesar 97,96%. Citra multispektral dengan ketinggian 15meter dapat digunakan untuk menduga penyakit blas karena memiliki korelasi sangat kuat dan akurasi untuk pendugaan yang tinggi. Abstract Measurement of the intensity of blast disease attack is usually done manually, so it takes a long time and accuracy in identifying. This can hamper the handling which causes the spread to be more widespread so that it risks a decrease in rice productivity, so it is necessary to develop the estimation of attack intensity through technology using multispectral imagery. This study aims to determine the relationship between vegetation index and the intensity of rice disease attack at various heights of image acquisition and get the accuracy of estimating the intensity of rice disease attack at various heights. Samples of disease attack intensity were taken as many as 3 plots where each plot was taken 5 points diagonally with per point taken 9 clumps. Followed by acquiring images of heights of 15 meters, 30 meters, and 45 meters using a Phantom 4 drone equipped with a multispectral camera, after the image is obtained, continued mosaicing using Agisoft software, then normalization using Photoshop. NDVI, SAVI, CIG vegetation index analysis is carried out using QGIS 2.28. The results showed that correlation was made to obtain the equation used for validation, and accuracy. The relationship between vegetation index and blast disease intensity at various altitudes was linearly correlated. Vegetation index NDVI 15 meters, SAVI 15 meters and 30 meters, CIG 15 meters, 30 meters, and 45 meters were strongly correlated. Vegetation indices NDVI at 30 meters, SAVI at 45 meters were strongly correlated with disease intensity, while NDVI at 45 meters was moderately strongly correlated with disease intensity. The 15-meter NDVI had the highest accuracy of 97.96%. Multispectral imagery with a height of 15 meters can be used to predict blast disease because it has a very strong correlation and high accuracy for estimation.]]>
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