Shallot Growth Stage Monitoring with Multispectral Imagery using MobileNetV2 - Yanuar Firmansyah; Nugraha Priya Utama (Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika, 2025)
Shallots are a strategic horticultural commodity, playing a pivotal role in the Indonesian food supply as a staple food. It is anticipated that the demand for shallots will increase in the future. Nonetheless, the production level remains inadequate, necessitating the importation of shallots. The success of the production process is contingent upon the monitoring of the crops by the farmers themselves. The current manual approach to this process has proven to be inadequate, necessitating the development of agricultural technology. Remote sensing has the potential to become a valuable technology in agriculture and represents a feasible methodology for accurate land mapping. However, its application in Indonesia remains constrained by the heterogeneity of the mapped areas and the quality of the images obtained. Furthermore, Indonesia's agricultural land is extensive and heterogeneous. This research presents the use of multispectral cameras for the precise monitoring and mapping of agricultural land. The imaging system employs a multispectral camera attached to a UAV to perform land mapping. RGB imagery and vegetation indices, including NDVI, NDRE, and OSAVI, are calculated to ascertain the crop status within a single shallot planting period. The shallot growth stage prediction is proposed using MobileNetV2 architecture. The proposed method demonstrated an accuracy of 84.78% using RGB and 44.78% using a combination of vegetation indices.