Enhancing Deep Learning-Based Semantic Segmentation Approaches for Smart Agriculture

Human civilization relies on agriculture as an essential component and has been the foundation of human society for thousands of years. However, agriculture faces several problems that affect its sustainability, productivity and profitability. Indeed, plant diseases, soil degradation and others as well as the development of artificial intelligence techniques are pushing human beings to think about smart agriculture and develop models of machine learning and deep learning to solve the problem. In this paper, we will present the process of preparing data using satellite images and the different models of machine learning and deep learning. Data preparation from satellite images and the various libraries implemented on python in order to apply different semantic segmentation approaches based on deep learning, which helps in decision-making and improving the overall performance and efficiency of the agricultural industry.