Analysis of the Effects of Digital Image Recognition, Quality, SOP Compliance, and Soil Control on the Enhancement of Oil Palm Yield (Tons/Ha) and CPO Output Per Hectare Using Structural Equation Modeling – Partial Least Squares (SEM-PLS)

This study investigates the factors influencing oil palm productivity and Crude Palm Oil (CPO) output per hectare through the integration of digital image recognition mitigation systems, operational quality, SOP compliance, and soil control management. The increasing demand for palm oil, combined with limitations on plantation expansion, requires plantation companies to optimize productivity through intensification and efficient operational management. Therefore, this research aims to analyze the direct and indirect effects of mitigation practices, operational quality, SOP compliance, and soil control on yield and CPO/ha production at PT XYZ. The study employed a quantitative approach using secondary data collected from 67 plantation areas during the 2024–2025 period. Data analysis was conducted using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS 4.0 software. The findings indicate that SOP compliance has a significant positive effect on yield (? = 0.464; p = 0.000) and indirectly affects CPO/ha through yield mediation. Yield also demonstrated the strongest direct influence on CPO/ha (? = 0.713; p = 0.000). Operational quality showed a moderate positive relationship with yield, while digital image recognition mitigation and soil control did not exhibit statistically significant direct effects. The study concludes that improving SOP compliance, harvesting quality, and operational discipline are essential strategies for enhancing oil palm productivity and increasing CPO output per hectare sustainably.