Weed Detection Model Using the Generative Adversarial Network and Deep Convolutional Neural Network

Abstract

Agriculture crop demand is increasing day by day because of population. Crop production can be increased by removing weeds in the agriculture field. However, weed detection is a complicated problem in the agriculture field. The main objective of this paper is to improve the accuracy of weed detection by combining generative adversarial networks and convolutional neural networks. We have implemented deep learning models, namely Generative Adversarial Network and Deep Convolutional Neural Network (GAN-DCNN), AlexNet, VGG16, ResNet50, and Google Net perform the detection of the weed. A generative Adversarial Network generates the weed image, and Deep Convolutional Neural Network detects the weed in the image. GAN-DCNN method outperforms than existing weed detection method. Simulation results confirm that the proposed GAN-DCNN has improved performance with a maximum weed detection rate of 87.12 and 96.34 accuracies.