A New Machine Learning Approach in Detecting the Oil Palm Plantations Using Remote Sensing Data

Deforestation, certification, and transnational palm oil supply chains: Linking Guatemala to global consumer markets

Although causal links between tropical deforestation and palm oil are well established, linking this land use change to where the palm oil is actually consumed remains a distinct challenge and research gap. Supply chains are notoriously difficult to track back to their origin (i.e., the 'first-mile'). This poses a conundrum for corporations and governments alike as they commit to deforestation-free sourcing and turn to instruments like certification to increase supply chain transparency and sustainability. The Roundtable on Sustainable Palm Oil (RSPO) offers the most influential certification system in the sector, but whether it actually reduces deforestation is still unclear. This study used remote sensing and spatial analysis to assess the deforestation (2009-2019) caused by oil palm plantation expansion in Guatemala, a major palm oil source for international consumer markets. Our results reveal that plantations are responsible for 28% of deforestation in the region and that more than 60% of these plantations encroach on Key Biodiversity Areas. RSPO-certified plantations, comprising 63% of the total cultivated area assessed, did not produce a statistically significant reduction in deforestation. Using trade statistics, the study linked this deforestation to the palm oil supply chains of three transnational conglomerates - Pepsico, Mondelēz International, and Grupo Bimbo - all of whom rely on RSPO-certified supplies. Addressing this deforestation and supply chain sustainability challenge hinges on three measures: 1) reform of RSPO policies and practices; 2) robust corporate tracking of supply chains; and 3) strengthening forest governance in Guatemala. This study offers a replicable methodology for a wide-range of investigations that seek to understand the transnational linkages between environmental change (e.g. deforestation) and consumption.

The Use of Random Forest Regression for Estimating Leaf Nitrogen Content of Oil Palm Based on Sentinel 1-A Imagery

For obtaining a spatial map of the distribution of nitrogen nutrients from oil palm plantations, a quite complex Leaf Sampling Unit (LSU) is required. In addition, sample analysis in the laboratory is time consuming and quite expensive, especially for large plantation areas. Monitoring the nutrition of oil palm plants can be achieved using remote-sensing technology. The main obstacles of using passive sensors in multispectral imagery are cloud cover and shadow noise. This research used C-SAR Sentinel equipped with active sensors that can overcome cloud barriers. A model to estimate leaf nitrogen nutrient status was constructed using random forest regression (RFR) based on multiple polarization (VV-VH) and local incidence angle (LIA) data on Sentinel-1A imagery. A sample of 1116 LSU data from different islands (i.e., Sumatra, Java, and Kalimantan) was used to develop the proposed estimation model. The performance evaluation of the model obtained the averaged MAPE, correctness, and MSE of 9.68%, 90.32% and 11.03%, respectively. Spatial maps of the distribution of nitrogen values in certain oil palm areas can be produced and visualized on the web so that they can be accessed easily and quickly for various purposes of oil palm management such as fertilization planning, recommendations, and monitoring.

A Review of an Artificial Intelligence Framework for Identifying the Most Effective Palm Oil Prediction

Machine Learning (ML) offers new precision technologies with intelligent algorithms and robust computation. This technology benefits various agricultural industries, such as the palm oil sector, which possesses one of the most sustainable industries worldwide. Hence, an in-depth analysis was conducted, which is derived from previous research on ML utilisation in the palm oil in-dustry. The study provided a brief overview of widely used features and prediction algorithms and critically analysed current the state of ML-based palm oil prediction. This analysis is extended to the ML application in the palm oil industry and a comparison of related studies. The analysis was predicated on thoroughly examining the advantages and disadvantages of ML-based palm oil prediction and the proper identification of current and future agricultural industry challenges. Potential solutions for palm oil prediction were added to this list. Artificial intelligence and ma-chine vision were used to develop intelligent systems, revolutionising the palm oil industry. Overall, this article provided a framework for future research in the palm oil agricultural industry by highlighting the importance of ML.