In-Season Yield Prediction and Monitoring of Sugarcane Using Cumulative Growth of Normalized Difference Vegetation Index

This paper demonstrates the successful implementation of the in-season yield estimation model in Thailand's sugarcane industry. NDVI (Normalized Difference Vegetation Index) is a crucial parameter of the model that can be used to identify the growth capability of the sugarcane in each contract farm of the factory, as explained. This paper also identifies the state problem of using satellite data in the context of ASEAN agriculture regimes that need high-resolution satellite imagery for most small farms in this region. The processing workload may increase the cost of technology implementation, which is vital for actual performance. The fewer input parameters while giving higher interpretation ability is the way to use the satellite data technology for this region successfully.