Design of GRT feature approximation-based segmentation and DCNN weed classification for effective yield estimation and fertilizer regulation

The issue of weed classification in paddy fields has received extensive research. There are various methods for resolving the weed classification issue. These methods use color, texture and binary features in classifying the weed image in the paddy field, but it suffers to achieve higher performance. Green–red-texture (GRT) approximation segmentation with the DCNN weed classification model (GRT-DCNN) proposed in this paper to solve this problem. Initially, the method preprocesses the weed image with multiple filters to remove noise from the color image. Further, the process applies K neighbor semicircular color approximation segmentation algorithm toward segmentation. The segmentation scheme groups the weed image features into several groups according to the paddy field nature. With the segmented image, the method applies GRTFA (green–red texture feature approximation) algorithm in selecting the region of interest (ROI). The selected ROI has been used to extract the color features and texture of the selected ROI. The features extracted are used to train the deep convolution neural network. This DCNN model has been designed with four convolution layers to reduce the dimensionality of the features considered. The GR features of the weed image are convolved in two level and texture features are convolved in the next two levels of convolution. The DCNN model was trained with four convolution and pooling layers to perform classification. The neurons at the output layer estimate weed class GR support (WCGRS) toward various classes of weed according to green and color features. Also, the method computes the weed class texture support (WCTS) according to the texture of different weed-maintained classes. The method computes disease class support (DCS) to carry out classification based on the value of WCGRS and WCTS. The decision support system would also estimate the distribution measure according to the growth region to detect the weed growth. The potential yield and the amount of fertilizer needed to support the field worker have been determined. The proposed GRT-DCNN-based weed classification model improves the performance in classification as well as in estimating the yield to support effective regulation of fertilizer.