This survey paper examines the current state of the art in plant species and disease detection using machine learning techniques. The paper explores various approaches, including image-based, and analyses their strengths and weaknesses. Additionally, the paper investigates the challenges associated with data collection and annotation, as well as the performance metrics used to evaluate the accuracy of detection models. The paper concludes with a discussion on the potential future directions of this field, including the integration of emerging technologies such as drone-based imaging and edge computing.