Hybrid Texture and Gradient Modeling for Dynamic Background Subtraction Identification System in Tobacco Plant Using 5G Data Service
Background: Detecting the plants as objects of interest in any vision-based input sequence is highly complex due to nonlinear background objects such as rocks, shadows, etc. Therefore, it is a difficult task and an emerging one with the development of precision agriculture systems. The nonlinear variations of pixel intensity with illumination and other causes such as blurs and poor video quality also make the object detection task challenging. To detect the object of interest, background subtraction (BS) is widely used in many plant disease identification systems, and its detection rate largely depends on the number of features used to suppress and isolate the foreground region and its sensitivity toward image nonlinearity.
Methodology: A hybrid invariant texture and color gradient-based approach is proposed to model the background for dynamic BS, and its performance is validated by various real-time video captures covering different kinds of complex backgrounds and various illumination changes. Based on the experimental results, a simple multimodal feature attribute, which includes several invariant texture measures and color attributes, yields finite precision accuracy compared with other state-of-art detection methods. Experimental evaluation of two datasets shows that the new model achieves superior performance over existing results in spectral-domain disease identification model.
5G assistance: After successful identification of tobacco plant and its analysis, the final results are stored in a cloud-assisted server as a database that allows all kinds of 5G services such as IoT and edge computing terminals for data access with valid authentication for detailed analysis and references.
Keywordsbackground subtraction, local binary pattern, tobacco plant, texture, Gaussian mixture model, illumination changes, plant disease identification system,
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