THE COFFEE NDVI MODELING USING BUILT-IN RGB PASSIVE SENSOR IN UAS

Abstract

Studies carried out used different sensors and field applications can be accomplished to obtain information and make decisions related to coffee management. The objective of this work was to train machine learning algorithms to estimate the NDVI based on the GreenseekerTM active optical sensor in 20 arabica coffee cultivars in the experimental area of the National Institute of Coffee Science and Technology (INCT of Coffee) using a passive RGB sensor. in unmanned aerial system (UAS) and its relationship with foliage. With the spectral signatures recorded in the RGB images of the 20 coffee cultivars, the relationships with the foliage and the NDVI data obtained with the Greenseeker™ active optical sensor were analyzed. Through machine learning and digital image processing techniques, it was possible to obtain an NDVI equation using the RGB bands based on the NDVI of the Greenseeker TM active optical sensor. The results were satisfactory when compared to in situ data, providing the use of a simple and effective method of evaluating the vegetative vigor of different coffee cultivars.