Syarifarudin Afa, AI Advisor at PT Lembaga Aplikasi Teknologi (LAT) Trisakti for the oil palm sector, said one of the approaches being developed involves on-premises AI processing. Under this model, AI models and plantation data are processed using computing infrastructure located within the company’s own environment. The approach could provide plantation companies with greater control over operational data and plant images collected from the field.   AI Monitoring Starts in the Nursery At the nursery stage, image analysis can help assess seedlings in both the pre-nursery and main nursery. Visual parameters such as leaf color, canopy condition and plant shape can be incorporated into the analysis. The system can also be developed to identify seedlings showing abnormal growth or characteristics that differ from company standards. The results can then serve as additional information when selecting seedlings before they are transferred to the field. According to Afa, collecting plant-condition data from the nursery could create a continuous information trail covering plant development through production. However, he stressed that AI-generated results still need to be validated against actual field conditions. Once the palms are planted in the field, monitoring shifts from individual seedlings to plant populations and crop development. Images collected by drones or cameras can support stand censuses by identifying dead palms, planting gaps and changes in canopy development. The data can also be integrated with geographic information systems (GIS), allowing plantation managers to locate areas requiring further inspection. This could support replanting decisions and monitoring of immature oil palm areas.   Vision AI for Plant Health and Yield Forecasting For mature oil palm plantations, Vision AI can be expanded to monitor plant health. Image-based indicators may include canopy conditions, frond numbers, signs of plant stress and weed presence. The technology could also be trained to identify visual symptoms associated with pests and diseases, including basal stem rot. However, accuracy depends heavily on training data and image quality, while visually similar symptoms can still lead to misidentification. Field verification therefore remains important. AI applications are also being explored for production forecasting through parameters such as Black Bunch Count (BBC) and Harvesting Density Number (AKP). BBC can help identify bunches that indicate potential future production, while AKP is commonly used in harvest forecasting. Regular image-based data collection could support a rolling forecast, allowing production projections to be updated as plantation conditions change. Such information could assist decisions on labor requirements, transportation, harvesting schedules and mill capacity.   AI-Based FFB Quality Inspection At the harvesting and FFB reception stages, Vision AI could also help identify fruit maturity levels, including unripe, ripe and overripe bunches. Image-processing systems could additionally be trained to recognize rotten bunches, empty bunches and loose fruit left at collection points. The technology could make inspections more consistent and provide structured data for plantation operations. Nevertheless, its accuracy must be tested under real field conditions, as lighting, camera angles, image quality and fruit characteristics can influence recognition results. The development of Vision AI indicates that digital technology is gradually becoming part of plantation management. Rather than replacing field expertise, its role is increasingly positioned as an additional source of data to support faster, more consistent and evidence-based decision-making.