These days, people see more and more applications for drones, including monitoring rainforests to protect plant and animal species. However, drones face challenges when navigating through the dense and cluttered vegetation of the forest. These environments necessitate advanced autonomous detection and navigation to make the drone traverse robustly and fly safely. In addition, the forest brings extra challenges, such as blocked signals for GPS localisation, remote control, and remote supervising. In this thesis project, a drone is designed, built, and programmed to navigate autonomously in the rainforest with complete onboard computing and no GPS localisation. This 500-gram drone is being extensively tested and optimized in real forest conditions, and a dataset is being created from its autonomous flights to simulate various configurations of the path-planning algorithm. The results of these simulations on this dataset are then used for thorough research on how the algorithm can downscale to smaller systems and how this affects performance. By using the results of this research on downscaling, a 100-gram drone is built and programmed to fly in forest conditions with complete onboard computation. Challenging on this small-size drone is the use of low-quality lightweight sensors and processor. The processor only weighs 10 grams, and the depth camera weighs 8 grams. Unique on this small drone is the 3D path planning fully computed onboard and the implementation of a new type of depth camera.