The health of oil palm plants is a strategic element in maintaining productivity and operational efficiency in plantations.
Plants that are not optimally monitored have the potential to cause a decline in production, increased maintenance costs, and risks to the operational sustainability of the plantation. To date, plant health monitoring is often carried out partially and reactively, relying on separate indicators and data that are not yet integrated. This condition makes it difficult for management to obtain a comprehensive picture of the plantation’s condition and to set appropriate data-based maintenance priorities. With the increasing availability of agronomic, spatial, climatic, and operational data, PT LPP Agro Nusantara presents an alternative solution through the development of a more measurable and proactive monitoring system. The multivariate analysis approach allows various plant health indicators to be integrated into a composite index that represents the condition of the plantation holistically and objectively.
Based on this, the development of the Multivariate Plant Health Control Dashboard has become a strategic necessity as a means of integrated monitoring and managerial decision support. This system is designed to improve the effectiveness of plant health control and support long-term plantation productivity and sustainability. The Multivariate Plant Health Algorithm is a plant health classification algorithm built on multivariate indices and combined with the Random Forest method. This algorithm functions to automatically monitor and classify the health conditions of plantation crops. Its implementation is carried out through a licensing agreement cooperation scheme for the integration of source code into the developed system.
Furthermore, the Multivariate Plant Health Dashboard was developed as an end-to-end system based on multivariate indices and the Random Forest machine learning algorithm within the framework of precision agriculture technology. This system is designed to support automatic, accurate, efficient, and measurable plant health monitoring and classification, as well as integration with the monitoring dashboard. The methodology used includes an image acquisition process sourced from satellite and drone images. The image data is then analyzed using the Normalized Difference Vegetation Index (NDVI) for plant health evaluation, land cover classification, and machine learning and artificial intelligence (AI) modeling for anomaly detection and pattern prediction. The analysis results are then presented in the form of dashboard visualizations and integrated through a geodatabase system with PostgreSQL/PostGIS and WebGIS API support.
The implementation of Multivariate Plant Health Assessment has had a significant impact on PalmCo through its ability to detect plant health problems early, improve the accuracy of determining treatment priorities, and strengthen data-driven decision making. Overall, this system contributes to increasing productivity, operational efficiency, and the achievement of the company’s sustainability targets.