This paper proposes a method for the early detection of this disease, which is the most significant pathogen of coffee plants worldwide, using multispectral images acquired using a Mapir Surve圓W camera and an unmanned aerial vehicle (UAV). The NN algorithm was also more accurate (27% RMSE) in predicting yield.Ĭonventional methodology in the field for the sampling of coffee leaf rust, caused by Hemileia vastatrix, has proven to be impractical. The NN algorithm performed best and was capable of estimating yield with 23% RMSE, 20% MAPE and R² 0.82 using 85% of the training and 15% of the validation data of the algorithm. The results show that the blue band and green normalized difference vegetation index (GNDVI) exhibit greater correlation with yield. Despite the low spatial resolution in estimating agricultural variables below the canopy, the presence of specific bands such as the red edge, mid infrared and the derived vegetation indices, act as a countermeasure. The Sentinel 2 satellite images proved to be favorable in estimating coffee yield. Statistical analysis was performed to assess the absolute Pearson correlation and coefficient of determination values. Yield data from a same study area in 2017, 20, Sentinel 2 images, Random Forest (RF) algorithms, Support Vector Machine (SVM), Neural Network (NN) and Linear Regression (LR) were used. Thus, the aim of the present study was to estimate the yield of a coffee crop using multispectral images and machine learning algorithms. However, strategies to estimate its yield are questionable given the characteristics of this crop in this context, robust techniques, such as those based on machine learning, may be an alternative. The coffee plant is one of the main crops grown in Brazil.
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