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- W4285192419 abstract "Sugarcane production data prior harvest are key information for optimizing harvest schedule and supply chain management, which contribute directly to the increase of profitability for both growers and sugar factories. Due to its flexibility, availability, and accessibility, unmanned aerial vehicle (UAV) imagery have been using to canopy detection, disease detection, sugar content estimation, and yield predictions of sugarcane. Vegetation index and machine learning technique were used to process images from multispectral camera and RGB camera and transformed into GIS data and validated with ground sampling data. Sugarcane canopy detection using linear discriminant analysis (LDA) obtained the highest accuracy of 97%. Normalized difference red edge index (NDREI) and green normalized difference vegetation index (GNDVI) yielded the highest potential for white-leaf disease detection for sugarcane. Chlorophyll Index-Red edge (ClRE) indicated good correlation with Brix of sugarcane around 0.90. Excess green (ExG) value was used to predict sugarcane yield with ordinary least square regression (OLSR) and obtained higher accuracy (R2 = 0.75)." @default.
- W4285192419 created "2022-07-14" @default.
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- W4285192419 date "2022-01-01" @default.
- W4285192419 modified "2023-09-26" @default.
- W4285192419 title "A Review on Innovation of Remote Sensing Technology Based on Unmanned Aerial Vehicle for Sugarcane Production in Tropical Region" @default.
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- W4285192419 doi "https://doi.org/10.1007/978-981-19-0213-0_12" @default.
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