Parametric Analysis on Crop/Weed Classification via Optimized Convolutional Neural Network

A new crop/weed classification model is established in this work that includes three main phases like (1) Preprocessing (2) Feature extraction (3) and Classification Initially, the input image is pre-processed via contrast enhancement process. Subsequently, feature extraction is performed, where “Gray Level Co-occurrence Matrix (GLCM) as well as Gray-Level Run-Length Matrix (GRLM)” based features are extracted. These extracted features together with RGB image (totally 5 channels) are classified via “optimized Convolutional Neural Network (CNN) For enhancing the classification, the weight and activation function of CNN are chosen optimally via Hybridized Whale and Sea Lion Algorithm (HW-SLA) model. Eventually, algorithmic analysis is carried out on proposed HW-SLA algorithm by varying its parameters.