Fig. 4

Network architecture of the proposed deep learning model to score cotton leaf hairiness. The proposed model consists of four main parts. First the image is passed through a Data Augmentation module (a) that augments the image by applying a variety of image processing techniques. Processed images are then passed to a Feature Extraction Network (b) that extracts discriminative visual features from the image representation. Extracted visual features are then passed to a simple Classification Neural Network (c) that assigns each input image to a specific score. Raw scores are then processed by the Leaf Hairiness Scoring module (d) which generates three accuracy metrics for scoring cotton leaf hairiness