Implementation of Yolo for Disease and Pathogendetection with Infrared Photogrammetry
This paper focuses on the application of Artificial Intelligence in the field of Agriculture. As all Machine Learning algorithms requires a well defined, complete dataset to work with, we use an Unmanned Aerial Device to collect all the data in the form of photographic snapshots. The UAD is such mechanized that it is capable of capturing images both in visible light as well as Infrared Spectrum. On fetching all the required information,the UAD is connected through an Application Program Interface where the dataset is trained using YOLO Computer Vision Algorithm.The algorithm is considered as one of the fastest detecting algorithms. The dataset is then tested on real life fields. The model is said to make pathogen detection, detection of weed levels and also time-saving pre-assesment for field tasks.
Keywords: Artificial Intelligence, Machine Learning, Computer Vision, YOLO, Unmanned Aerial Device.