Optimization of GeoJSON Data in Python for Enhancing Precision Fertilization in Oil Palm Plantations Using on-Board Computer Units

This study focuses on optimizing GeoJSON data processing techniques in the Python environment to improve the performance of the On-board Computer Unit system (OBC) function used in precision fertilization in oil palm plantations. By integrating advanced data processing algorithms with OBC, this study can also improve the accuracy and efficiency of the Variable-Rate Fertilizer Application (VRFA) system. The GeoJSON format, known for its ability to represent geographic data, is used as the basis for representing detailed spatial map data from oil palm plantations. This optimized data is provided in the OBC unit, allowing for real-time adjustment of fertilizer applications based on specific conditions in the field. Experimental results show that GeoJSON data optimization in Python applied to OBC using Advanced RISC Machines (ARM) processors based on Reduced Instruction Set Computing (RISC) processor architecture significantly improves processing capabilities in terms of time efficiency when compared to Personal Computer (PC) applications using Intel processors based on Complex Instruction Set Computing (CISC) architecture by 13.41% for Nitrogen (N) macro elements, 27.67% for macro elements Phosphorus (P) and 32.77% for macro elements Potassium (K). So, it can be concluded that OBC computers with processor speed specifications almost similar to PCs offer a faster and more efficient solution for GeoJSON data processing time in precision agriculture.