Artificial Intelligence in Greenhouses
Machine Vision
Machine vision (MV) is a branch of artificial intelligence. Machine vision is an application of computer vision that enables a machine to recognize an object. Machine vision captures and analyzes visual information using one or more video cameras, analog-to-digital conversations, and digital signal processing. Machine vision has been widely used to support precision agriculture by providing automated solutions to tasks that are traditionally performed manually. Manual methods tend to be tedious and error-prone.
Machine Vision Algorithms
Machine vision algorithms encompass various techniques and methods designed to process and analyze images. These algorithms transform raw image data into meaningful information that can be used for various tasks. They are essential for enabling machines to understand and interact with their environment.
Machine Vision Image Training Datasets
Common to all computer vision-based precision agriculture tasks is presumably the goal of detecting the objects of interest (e.g., crop, weed, or fruit) and discriminating them from the rest of the scene. Achieving this requires, in addition to a well-designed hardware system, a robust data analysis pipeline that generally involves training machine learning models with specific image datasets.
Image Annotation
Image annotation in agriculture is an essential process that involves labeling objects and features in images. It provides contextual information that aids machine learning models in recognizing patterns and making accurate predictions. Data labeling is crucial for training AI/ML systems to identify, analyze, and optimize various aspects of agricultural practices.
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