Weed Detection Model Using the Generative Adversarial Network and Deep Convolutional Neural Network
Keywords:Deep learning, Weed detection, Generative Adversarial Network, Deep Convolutional Neural Network
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.
J. Du, Y. Liu, and Z. Liu, “Study of precipitation forecast based on deep belief networks,” Algorithms, vol. 11, no. 9, pp. 1–11, 2018, DOI: 10.3390/a11090132.
C. Wang et al., “Pulmonary image classification based on inception-v3 transfer learning model,” IEEE Access, vol. 7, pp. 146533–146541, 2019, DOI: 10.1109/ACCESS.2019.2946000.
F. Liu, Y. Yang, Y. Zeng, and Z. Liu, “Bending diagnosis of rice seedling lines and guidance line extraction of automatic weeding equipment in paddy field,” Mech. Syst. Signal Process., vol. 142, no. 381, p. 106791, 2020, DOI: 10.1016/j.ymssp.2020.106791.
A. Wang, Y. Xu, X. Wei, and B. Cui, “Semantic Segmentation of Crop and Weed using an Encoder-Decoder Network and Image Enhancement Method under Uncontrolled Outdoor Illumination,” IEEE Access, vol. 8, pp. 81724–81734, 2020, DOI: 10.1109/ACCESS.2020.2991354.
M. D. Bah, A. Hafiane, and R. Canals, “CRowNet: Deep Network for Crop Row Detection in UAV Images,” IEEE Access, vol. 8, pp. 5189–5200, 2020, DOI: 10.1109/ACCESS.2019.2960873.
S. L. Madsen, M. Dyrmann, R. N. Jørgensen, and H. Karstoft, “Generating artificial images of plant seedlings using generative adversarial networks,” Biosyst. Eng., vol. 187, pp. 147–159, 2019, doi: 10.1016/j.biosystemseng.2019.09.005.
S. P. Adhikari, G. Kim, and H. Kim, “Deep neural network-based system for autonomous navigation in paddy field,” IEEE Access, vol. 8, pp. 71272–71278, 2020, DOI: 10.1109/ACCESS.2020.2987642.
V. Maeda-Gutiérrez et al., “Comparison of convolutional neural network architectures for classification of tomato plant diseases,” Appl. Sci., vol. 10, no. 4, 2020, DOI: 10.3390/app10041245.
H. Jiang, C. Zhang, Y. Qiao, Z. Zhang, W. Zhang, and C. Song, “CNN feature-based graph convolutional network for weed and crop recognition in smart farming,” Comput. Electron. Agric., vol. 174, no. October 2019, p. 105450, 2020, DOI: 10.1016/j.compag.2020.105450.
A. Abdalla et al., “Fine-tuning convolutional neural network with transfer learning for semantic segmentation of ground-level oilseed rape images in a field with high weed pressure,” Comput. Electron. Agric., vol. 167, no. October, p. 105091, 2019, DOI: 10.1016/j.compag. 2019.105091.
R. Raja, T. T. Nguyen, D. C. Slaughter, and S. A. Fennimore, “Real-time weed-crop classification and localization technique for robotic weed control in lettuce,” Biosyst. Eng., vol. 192, pp. 257–274, 2020, doi: 10.1016/j.biosystemseng.2020.02.002.
J. Gao, A. P. French, M. P. Pound, Y. He, T. P. Pridmore, and J. G. Pieters, “Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields,” Plant Methods, vol. 16, no. 1, pp. 1–12, 2020, DOI: 10.1186/s13007-020-00570-z.
J. You, W. Liu, and J. Lee, “A DNN-based semantic segmentation for detecting weed and crop,” Comput. Electron. Agric., vol. 178, no. March, p. 105750, 2020, DOI: 10.1016/j.compag.2020.105750.
M. H. Asad and A. Bais, “Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network,” Inf. Process. Agric., vol. 7, no. 4, pp. 535–545, 2020, DOI: 10.1016/ j.inpa.2019.12.002.
A. dos Santos Ferreira, D. M. Freitas, G. G. da Silva, H. Pistorius, and M. T. Folhes, “Unsupervised deep learning and semi-automatic data labelling in weed discrimination,” Comput. Electron. Agric., vol. 165, no. July, p. 104963, 2019, DOI: 10.1016/j.compag.2019.104963.
K. Hu, G. Coleman, S. Zeng, Z. Wang, and M. Walsh, “Graph weeds net: A graph-based deep learning method for weed recognition,” Comput. Electron. Agric., vol. 174, no. April, p. 105520, 2020, DOI: 10.1016/j.compag.2020.105520.
T. F. Gonzalez, “ImageNet Classification with Deep Convolutional Neural network,” Handb. Approx. Algorithms Metaheuristics, pp. 1–1432, 2007, DOI: 10.1201/9781420010749.
K. Simonyan and A. Zisserman, “Intense convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 – Conf. Track Proc., pp. 1–14, 2015.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016 – December, pp. 770–778, 2016, DOI: 10.1109/CVPR.2016.90.
C. Szegedy et al., “Going deeper with convolutions,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June, pp. 1–9, 2015, DOI: 10.1109/CVPR.2015.7298594.
A. Olsen et al., “DeepWeeds: A Multiclass Weed Species Image Dataset for Deep Learning,” Sci. Rep., vol. 9, no. 1, pp. 1–12, 2019, DOI: 10.1038/s41598-018-38343-3.
I. Sa et al., “WeedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming,” IEEE Robot. Autom. Lett., vol. 3, no. 1, pp. 588–595, 2018, DOI: 10.1109/LRA.2017. 2774979.