This work presents a remote sensing solution for sugarcane precision agriculture based on a drone-borne differential interferometric synthetic aperture radar (DInSAR) operating in the P-, L-, and C-bands. With one flight pass, the system can estimate the soil moisture, the plantation height, and the above-ground biomass map; and predict the harvest date and the respective productivity. With two flight passes, it assesses the crop growth via differential interferometry. A new methodology dedicated to sugarcane plantation was developed based on the existing methodologies for soil moisture and biomass measurement. The image information from the three bands, plus the C-band InSAR and P-band DInSAR information, show immense potential for efficient and low-cost monitoring. The results validated the methodology in a large sugarcane mill.