Yield estimation comparison of oil palm based on plant density coefficient variation index using spot-6 imagery in part of Riau

Oil Palm Productivity Estimation Based on Landsat 8 Vegetation Index, Rainfall, and Fertilization Dosage at PT PSJ, South Sumatera

“…One of the vegetation index values often used is the Normalized Difference Vegetation Index (NDVI). Based on several studies, NDVI value has a relationship with oil palm production [5], [6]. This research's NDVI value was obtained from the Landsat 8 satellite using Google Earth Engine (GEE).…”

“…According to some research, there are many vegetation indexes of remote sensing methods that can be used to estimate palm oil productivity, one of those is NDVI [4], [5], [6]. NDVI (Normalized Difference Vegetation Index) can represent the plant density and condition of the palm oil in an area that can be used to estimate palm oil productivity [6].…”

“…According to some research, there are many vegetation indexes of remote sensing methods that can be used to estimate palm oil productivity, one of those is NDVI [4], [5], [6]. NDVI (Normalized Difference Vegetation Index) can represent the plant density and condition of the palm oil in an area that can be used to estimate palm oil productivity [6]. This research was conducted to assess palm oil productivity with NDVI from Landsat 8 and another additional parameter like rainfall, rainy days, fertilization dosage, and the age of the palm oil tree from the company's data using multiple linear regression that haven't been researched much.…”

Potensi Dan Tantangan Penerapan Precision Farming Dalam Upaya Membangun Perkebunan Kelapa Sawit Yang Berkelanjutan

“…Penerapan PF untuk mengestimasi tanaman didasarkan pada i n f o r m a s i m e n g e n a i f a k t o r -f a k t o r y a n g mempengaruhi produksi seperti kondisi iklim, kondisi tanah, umur tanaman, dan faktor lainnya. Saat ini PF yang banyak diterapkan di perkebunan kelapa sawit untuk mengestimasi produksi didasarkan pada informasi indeks tanaman yang diturunkan foto satelit Quickbird (Balasundram et al, 2013;Darmawan et al, 2016;Khamiz et al, 2005;Setyowati et al, 2016). Namun demikian, melalui kombinasi dari informasi faktor eksternal yang mempengaruhi produksi seperti curah hujan, kesuburan tanah, serangan hama penyakit, dan faktor internal seperti jumlah tanaman, varietas tanaman, dan umur tanaman dapat dianalisis menggunakan berbagai teknologi komputerisasi untuk mendapatkan model prediksi produksi yang akurat dalam bentuk software.…”