Phytopathology identification using machine learning is a study that aims to develop and implement a computer-based system for the diagnosis of plant diseases. The system utilizes advanced machine learning techniques to analyse images or other data inputs of affected plants and identify the pathogen responsible for the disease. The goal is to provide a fast, accurate, and cost-effective solution for phytopathology identification, helping to prevent the spread of plant diseases and improve crop yields. The study involves the collection and labelling of a large dataset of plant disease images, which is then used to train the machine learning models. The models are evaluated based on their accuracy and ability to generalize to unseen data. In addition, the system can also take into consideration other factors such as plant species, symptoms, and location, to make a more accurate diagnosis. The results of the study demonstrate the feasibility and effectiveness of using machine learning for phytopathology identification and provide insights into the development of similar systems in the future. The implications of this study go beyond just improving crop yields. Accurate and timely diagnosis of plant diseases is crucial for food security and the preservation of biodiversity. By incorporating machine learning into phytopathology, this study has the potential to contribute to a more sustainable and resilient food system.