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Analisis Perilaku Air Di Perkebunan Kelapa Sawit Pada Tanah Spodosol dan Ultisol Menggunakan Soil Moisture Content Monitoring System (SMCMS) Sukarman, Sukarman; Sutiarso, Lilik; Suwardi, Suwardi; Wirianata, Herry; Prima Nugroho, Andri; Primananda, Septa; Syarovy, Muhdan; Pradiko, Iput; Hijri Darlan, Nuzul
Jurnal Penelitian Kelapa Sawit Vol 32 No 1 (2024): Jurnal Penelitian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/iopri.jur.jpks.v32i1.270

Abstract

Ketersediaan air merupakan aspek yang sangat penting agar kelapa sawit dapat tumbuh dan berproduksi secara optimal. Tujuan penelitian ini adalah untuk menganalisis perilaku air melalui monitoring dinamika kelembaban dan suhu tanah menggunakan Soil Moisture Content Monitoring System (SMCMS). SMCMS terdiri atas sensor yang dipasang di lapangan dan sistem monitoring berbasis internet. SMCMS dipasang di perkebunan kelapa sawit pada tanah Ultisol (A), Spodosol dengan perlakuan pecah hardpan dan mounding (B), dan Spodosol tanpa perlakuan (C). Sensor kelembaban dan suhu tanah dipasang pada tiga kedalaman yang berbeda. Hasil menunjukkan bahwa SMCMS dapat beroperasi secara otomatis dan real-time dalam mengukur perilaku air. Berdasarkan hasil monitoring dan pengukuran, dapat diketahui bahwa kelembaban tanah tertinggi terdapat pada lokasi A dengan rerata 46,91%, kemudian diikuti lokasi B 38,40%, dan C yaitu 29,49%. Spodosol dengan perlakuan (B) memiliki suhu tanah terendah dengan rerata 27,36°C, kemudian diikuti Ultisol (A) 27,58°C, dan Spodosol kontrol (C) 28,40°C. Lebih lanjut, kelembaban tanah berkorelasi lemah dengan suhu tanah. Suhu tanah memiliki korelasi yang lemah dengan suhu udara. Sementara itu, kedua perilaku air tanah tersebut memiliki korelasi yang sangat lemah dengan variabel lingkungan, khususnya curah hujan.
EFEK KEKERINGAN DAN GANGGUAN ASAP TERHADAP EKOFISIOLOGI DAN PRODUKTIVITAS TANAMAN KELAPA SAWIT DI SUMATRA SELATAN Syarovy, Muhdan; Pradiko, Iput; Listia, Eka; Darlan, Nuzul Hijri; Hidayat, Fandi; Winarna, Winarna; Rahutomo, Suroso
Jurnal Penelitian Kelapa Sawit Vol 25 No 3 (2017): Jurnal Penelitian Kelapa Sawit
Publisher : Pusat Penelitian Kelapa Sawit

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (227.781 KB) | DOI: 10.22302/iopri.jur.jpks.v25i3.31

Abstract

Prolonged dry season, land fire, and haze disturbance occurred during El Niño 2015 in Indonesia. A study had been conducted to identify impacts of prolonged dry season and haze disturbance on ecophysiology of oil palm in Dawas Estate, South Sumatra. The study was conducted by collecting data of precipitation, visibility, oil palm fronds addition, rate of photosynthesis, Photosinthetically Active Radiation (PAR), and Elaeidobius kamerunicus activity on mature and immature palm before, during and after the incidence of drought and haze disturbance. T test was used for statistics analysis. The results showed that water deficit was recorded in July, August, September and October, it was 45, 92, 80, and 148 mm respectively. Dry month (precipitation was ≤ 60 mm) was 2 months, while dry spell occurred 3 times in June to July (33 days), August to September (42 days), and September to October (40 days). Haze disturbance occurred in August to November, it had decreased visibility to 80%. During drought stress and haze disturbance, there was decrease in fronds addition, photosynthesis rate and bunch productivity in following year. In addition, haze disturbance had decreased number of Elaeidobius kamerunicus visitting female flowers up to 95%.
Klasifikasi Kematangan Buah Kelapa Sawit Menggunakan Model Yolov8 Berbasis Deep Learning Muna, Mukhes Sri; Setiyo, Yohanes; Wirawan, I Putu Surya; Syarovy, Muhdan; Jaya, Gigieh Henggar
Journal of Agricultural and Biosystem Engineering Research Vol 6 No 1 (2025): Journal of Agricultural and Biosystem Engineering Research: Regular Issue
Publisher : Program Studi Teknik Pertanian, Fakultas Pertanian, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jaber.2025.6.1.15953

Abstract

Determining the ripeness level of oil palm fruit is a crucial aspect in enhancing the efficiency and quality of palm oil production. To date, most ripeness classification processes are still manually conducted, leading to inconsistencies and human error. This study aims to develop an oil palm fruit ripeness classification model using YOLOv8, a state-of-the-art deep learning architecture known for its excellence in computer vision tasks. The dataset consists of six ripeness classes, divided into training, validation, and testing sets sourced from the Roboflow platform. The training process involved five YOLOv8 sub-models with optimized parameter configurations. Evaluation was carried out using MAPE and confidence score metrics to measure prediction accuracy. The results showed that all sub-models successfully classified fruit ripeness with high accuracy, with YOLOv8l-cls achieving the lowest MAPE value of 0.01167. These, confirm that the YOLOv8-based approach is highly effective in supporting automated classification of oil palm fruit ripeness, offering faster, more accurate, and consistent results, and holds strong potential for widespread application in the plantation industry.