ABSTRACT
Machine learning brings out an analytical model in data analysis of natural time environment, including artificial intelligence that can process real-time data, find patterns and implement choices with minimal latency. There are many processed in the field of agriculture embedded with a machine learning algorithm in the early stages. An innovation of combining the machine learning algorithm with the un-manned vehicle called a drone to perform agricultural activities has been a challenge so far. In recent times, a paradigm shift with machine learning and high-performance computing came to fruition in the multi-disciplinary agri-technologies industry with big data. Drones play a major role in accompanying the farmers in recent times. Real-time artificial intelligence is available for improved decision support and action planning in agriculture by implementing machine learning techniques to the Raspberry Pi board to control the drone. Thus, the three goals of DAD (drone, agriculture and deep learning) are deep learning technology, agricultural activities, drone management and smart farming. This chapter ends with a case study of applying the goals in urea spraying in agricultural fields. Furthermore, a case study can be made in the other exciting fields of agriculture. The evaluation represents the different machine learning algorithms supporting the various agricultural sectors for more in-depth knowledge in the future.
