Case Study: BSFR laboratory optimizes sugarcane harvest schedules with Pix4D

Optimizing harvest schedules for Thai sugarcane fields

BSFR laboratory at Khon Kaen University in Thailand needed a better way to estimate Brix content and primary yield in an 8-month-old sugarcane field so it could optimize harvest schedules. To move beyond random handheld sampling, the team turned to Pix4D and its agriculture mapping tools, using multispectral drone imagery to assess crop maturity and field conditions across the 3.7-hectare site.

With PIX4Dmapper and a MicaSense RedEdge MX sensor on a VESPA HEX drone, BSFR laboratory generated reflectance maps and a DSM from 295 images. Pix4D’s outputs helped the team build a Brix model with an RMSEP of about ±1° Brix and estimate sugarcane height with an RMSEP of ±0.3 meters, supporting yield prediction with up to about 5% error across multiple fields.

BSFR laboratory

Chanreaksa Chea

Research Assistant