Sparse Representation for Paddy Plants Nutrient Deficiency Tracking System

Moving object detection and tracking from consecutive frames of sensing devices (Unmanned Aerial Vehicles-UAV) needs efficient sampling from mass data with sufficient memory saving. Objects with super pixels are tracked by Compressive Sensing (CS) and the generative structural part model is designed to be adaptive to variation of deformable objects. CS can precisely reconstruct sparse signal with a small amount of sampling data. This system creates the sparse representation (SR) dictionary representing the nutrient deficiency tracking system for paddy plants to support the healthily growth of the whole field. This system uses compressed domain features that can be exploited to map the semantic features of consecutive frames. As the CS is a developing signal processing technique, a sparse signal is reconstructed with efficient sampling rate and creates the sparse dictionary. The SR for paddy plant health system can build rich information about paddy plants from signaling devices and can alert the deficiency conditions accurately in real time