Segmentação semântica de áreas de plantações agrícolas via U-Net em dois estágios

Abstract

The agribusiness tax is mainly levied on the production of agricultural crops. To reduce tax evasion in agribusiness, it is possible to monitor the development of plantations through the analysis of satellite images. For this, we can apply Machine Learning techniques to satellite images to segment the planted area, and the area, in turn, can be used to estimate the production of monitored plantations. This work aims to solve the first stage of the problem, the Segmentation of the Planted Area. For this, we developed a machine learning architecture for segmentation of plantation areas, the Two-stage U-net. In addition, the work also included the creation of a satellite image dataset with annotations for the segmentation of plantation areas. We trained the proposed model and we adjusted its hyperparameters considering the U-net Encoder, the Optimizer, the Loss Function, and the Batch Size of images. We selected the fitted model that performed best in tests with Hyperopt and GridSearch. The results in mIoU of the Two-stage U-net were superior to the results of other architectures used in similar works.