{"id":1147,"date":"2022-06-21T13:24:38","date_gmt":"2022-06-21T13:24:38","guid":{"rendered":"https:\/\/www.imageintellegence.com\/?page_id=1147"},"modified":"2022-06-27T08:15:08","modified_gmt":"2022-06-27T08:15:08","slug":"api-documentation","status":"publish","type":"page","link":"https:\/\/www.imageintellegence.com\/index.php\/api-documentation\/","title":{"rendered":"API Documentation"},"content":{"rendered":"
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Layanan API (Aplication Programming Interface) dari analisis data penginderaan jauh dan GIS akan membantu perkebunan – pertanian (sawit) mengintegrasikan data biofisik, agronomi, tanah, klimatologi dengan data ERP perusahaan. <\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

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Dapatkan layanan gratis dengan registrasi dan pilihan untuk corporate service untuk memperoleh layanan API Remote Sensing dan GIS sawit lebih kompleks antar waktu dan dikalibrasi dengan karakteristik lokasi\/site specifik jenis tanah,\n data agronomi, iklim dan topografi.<\/p>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n

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5 Langkah memulai menggunakan Layanan ini<\/h4>\n <\/div>\n <\/div>\n
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  1. Register menggunakan email anda dengan menu Home > Register<\/li>\n
  2. Jika anda ingin mendapatkan layanan tertentu secara gratis, upload blok kebun dalam format GIS, data agronomi dalam format csv\/xlsx, data analisis tanah dalam format csv\/xlsx<\/li>\n
  3. Pilih API near real time yang anda butuhkan<\/li>\n
  4. Konfirmasi ke whatsapp dan tunggu waktu proses (1 x 24 jam)<\/li>\n
  5. Anda akan mendapatkan email konfirmasi, akses ke API dalam bentuk url<\/li>\n <\/ol>\n

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    How Start<\/span><\/a>\n <\/span> Register<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Agriforest Biogeophysical<\/h4>\n <\/div>\n <\/div>\n
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    API Satelite Imagery Vegatation Index (VIS)<\/h6>\n <\/div>\n <\/div>\n
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    > Indeks Kehijauan (NDVI)<\/span><\/a>\n <\/span> > EVI<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > Green Index (GI)<\/span><\/a>\n <\/span> > LAI<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > historical Vegetation Index <\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Weather Data integration with plantation<\/h6>\n <\/div>\n <\/div>\n
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    > T – Suhu Udara<\/span><\/a>\n <\/span> > Rh – Kelembaban Rata-rata<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > T Minimum<\/span><\/a>\n <\/span> > T Maksimum<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > Curah Hujan (CH)<\/span><\/a>\n <\/span> > Lama Penyinaran Matahari (SS<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > Rerata Kecepatan Angin <\/span><\/a>\n <\/span> > Kecepatan Angin Maksimum<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Kekeringan dan Kebasahan Perkebunan <\/h6>\n <\/div>\n <\/div>\n
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    > Indeks kekeringan (NDDI)<\/span><\/a>\n <\/span> > Indeks Kebasahan (NDII\/NDWI)<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Agronomi Tanaman Sawit<\/h6>\n <\/div>\n <\/div>\n
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    > Suhu Permukaan Daun<\/span><\/a>\n <\/span> > Rerata Suhu Udara <\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > Kadar N Daun (%)<\/span><\/a>\n <\/span> > Kadar P Daun (%)<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > Kadar K (%) Daun<\/span><\/a>\n <\/span> > Kadar Mg Daun (%)<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > Produksi Rata-Rata Tahunan<\/span><\/a>\n <\/span> > Produksi Rerata Bulanan<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > Usia tanam<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Kharakteristik Tanah<\/h6>\n <\/div>\n <\/div>\n
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    > Surface Temperatur<\/span><\/a>\n <\/span> > Soil Moisture<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > Temperatur permukaan tanah<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Land Cover \/ Land Use<\/h4>\n <\/div>\n <\/div>\n
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    Adalah API yang memberikan informasi peta penggunaan lahan dari hasil analisis interpretasi citra satelit.<\/p>\n <\/div>\n <\/div>\n

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    API Baseline Maps<\/h4>\n <\/div>\n <\/div>\n
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    > Nama Administrasi<\/span><\/a>\n <\/span> > Status Kawasan Hutan <\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    > Kawasan gambut<\/span><\/a>\n <\/span> > Type Tanah Mineral\/Non Mineral<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n
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    Agriforestry Biogeophysical<\/h2>\n <\/div>\n <\/div>\n
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    API Satelite Imagery Vegetation Index (VIs)<\/h6>\n <\/div>\n <\/div>\n
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    Manfaat menggunakan Indeks seperti NDVI, LAI, EVI dan sebagainya dalam memonitor kebun yang luas, bagi managemen kebun terutama dapat secara visual mendapatkan gambaran tingkat pengelolaan kebunnya apakah sudah memuaskan atau memerlukan\n perhatian dalam blok kebun tertentu.<\/p>\n <\/div>\n <\/div>\n

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    NDVI, EVI, LAI, GI<\/span><\/a>\n <\/span> Historical VIS<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Weather Data integration with plantation (Iklim)<\/h6>\n <\/div>\n <\/div>\n
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    Memanfaatkan data cuaca seperti temperatur rata-rata, data blok kebun apalagi dalam bentuk timeseries data prediksi kebun, dapat memberikan informasi kapan dilakukan pemupukan, irigasi atau drainease. <\/p>\n <\/div>\n <\/div>\n

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    Tavg, CH, RH, WS, SS<\/span><\/a>\n <\/span> Historical weather<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Kekeringan dan Kebasahan Perkebunan<\/h6>\n <\/div>\n <\/div>\n
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    Penggunaan indeks kekeringan seperti NDDI, CSWI dapat diimplementasikan untuk memonitor tingkat kekeringan kebun, sehingga dapat dicegah resiko penurunan produksi akibat kekeringan.<\/p>\n <\/div>\n <\/div>\n

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    Kekeringan, Kebasahan<\/span><\/a>\n <\/span> Historical<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Agronomi Tanaman (Agro)<\/h6>\n <\/div>\n <\/div>\n
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    Penggunaan Machine Learning dalam memprediksi kandungan hara di daun, produksi sawit, sudah kami uji dengan memberikan tingkat akurasi pada level AKURAT (eror < 10%) dan BAIK (eror antara 30 sd 10%)<\/p>\n <\/div>\n <\/div>\n

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    Nutrisi Daun, Produksi, Usia<\/span><\/a>\n <\/span> Historical<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Kharakteristik Tanah (Soil) – (Under Development)<\/h6>\n <\/div>\n <\/div>\n
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    Untuk memahami fisik tanah secara mikro, data SSU atau sampling tanah perlu dianalisis secara spasial dan dihubungkan dengan data penginderaan jauh, sehingga dapat diprediksi karakteristik tanah berdasarkan data penginderaan jauh.<\/p>\n <\/div>\n <\/div>\n

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    Soil Surface Temperatur, Soil Moisture <\/span><\/a>\n <\/span> Historical<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n
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    API Baseline Maps<\/h2>\n <\/div>\n <\/div>\n
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    API Baseline Maps (Thema) (Under Development)<\/h6>\n <\/div>\n <\/div>\n
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    Untuk memahami situasi fisik secara umum kebun disuatu area, perlu didukung peta daya dukung lahan, seperti informasi lahan gambut, jenis tanah, geologi, batas kawasan hutan maupun HGU. Serta peta batas tematik pertambangan, maupun tematik\n lainnya.<\/p>\n <\/div>\n <\/div>\n

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    Batas, Legal. HGU<\/span><\/a>\n <\/span> Peta Thematik<\/span><\/a>\n <\/span>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

    Layanan API (Aplication Programming Interface) dari analisis data penginderaan jauh dan GIS akan membantu perkebunan – pertanian (sawit) mengintegrasikan data […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/full-width-page.php","meta":{"footnotes":""},"class_list":["post-1147","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/www.imageintellegence.com\/index.php\/wp-json\/wp\/v2\/pages\/1147","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.imageintellegence.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.imageintellegence.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.imageintellegence.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.imageintellegence.com\/index.php\/wp-json\/wp\/v2\/comments?post=1147"}],"version-history":[{"count":20,"href":"https:\/\/www.imageintellegence.com\/index.php\/wp-json\/wp\/v2\/pages\/1147\/revisions"}],"predecessor-version":[{"id":3238,"href":"https:\/\/www.imageintellegence.com\/index.php\/wp-json\/wp\/v2\/pages\/1147\/revisions\/3238"}],"wp:attachment":[{"href":"https:\/\/www.imageintellegence.com\/index.php\/wp-json\/wp\/v2\/media?parent=1147"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}