Deep Residual CNN with Contrast Limited Adaptive Histogram Equalization for Weed Detection in Soybean Crops

Abstract

Weeding is the fundamental task in agriculture to increase yields crop. Accurate weed recognition is major prerequisite in precision agriculture. Precision weeding significant reduces the usage of herbicides in farming. Deep learning has been a major endeavor for enhancing the learning performance, particularly for classification. This paper proposes a Deep Residual Convolutional neural network (DRCNN) with Contrast Limited Adaptive Histogram Equalization (CLAHE) for weed and crop classification helpful for accurate individual targeting of weeds. In this method, initially data augmentation is performed to avoid overfitting on training data, A deeper residual network architecture is defined through residual connections in CNN architecture this architecture improves gradient flow through the network and for training the deeper network. The experiments are carried out on the publicly available dataset with four groups of images viz., soil, grass, soybean and broadleaf. Different state-of-the-art pretrained networks like AlexNet, and VGG-16 Net are also investigated and the results are compared. The proposed method yielded an accuracy of 97.3% which is superior to other methods.