Development of vegetation mapping with deep convolutional neural network

ABSTRACT

The Precision Agriculture (PA) plays a crucial part in the agricultural industry about improving the decision-making process. It aims to optimally allocate the resources to maintain the sustainable productivity of farmland and reduce the use of chemical compounds. [17] However, the on-site inspection of vegetations often falls to researchers' trained eye and experience, when it deals with the identification of the non-crop vegetations. Deep Convolution Neural Network (CNN) can be deployed to mitigate the cost of manual classification. Although CNN outperforms the other traditional classifiers, such as Support Vector Machine, it is still in question whether CNN can be deployable in an industrial environment. In this paper, we conducted a study on the feasibility of CNN for Vegetation Mapping on lawn inspection for weeds. We would like to study the possibility of expanding the concept to the on-site, near realtime, crop site inspections, by evaluating the generated results.

References

1. Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural computation, 29(9), 2352--2449.
2. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770--778).
3. Wang, J., & Perez, L. (2017). The effectiveness of data augmentation in image classification using deep learning (No. 300). Technical report.
4. Torralba, A., & Efros, A. A. (2011, June). Unbiased look at dataset bias. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 1521--1528). IEEE.
5. Zou, W. W., & Yuen, P. C. (2012). Very low-resolution face recognition problem. IEEE Transactions on Image Processing, 21(1), 327--340.
6. He, K., Zhang, X., Ren, S., & Sun, J. (2016, October). Identity mappings in deep residual networks. In European Conference on Computer Vision (pp. 630--645). Springer, Cham.
7. Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014). Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv.1405.3531.
8. Zhu, X., Vondrick, C., Ramanan, D., & Fowlkes, C. C. (2012, September). Do We Need More Training Data or Better Models for Object Detection? In BMVC (Vol. 3, p. 5).
9. Taylor, L., & Nitschke, G. (2017). Improving Deep Learning using Generic Data Augmentation. arXiv preprint arXiv:1708.06020.
10. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097--1105).
11. Chen, X. W., & Lin, X. (2014). Big data deep learning: challenges and perspectives. IEEE access, 2, 514--525.
12. Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). Deepfruits: A fruit detection system using deep neural networks. Sensors, 16(8), 1222.
13. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1), 1929--1958.
14. Shen, L. (2017). End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design. arXiv preprint arXiv:1708.09427.
15. Yu, F., & Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122.
16. Masko, D., & Hensman, P. (2015). The impact of imbalanced training data for convolutional neural networks.
17. Bongiovanni, R., & Lowenberg-DeBoer, J. (2004). Precision agriculture and sustainability. Precision agriculture, 5(4), 359--387.
18. Suh, S. H., Kim, D. Y., Jhang, J. E., Byamukama, E., Hatfield, G., & Shin, S. Y. (2017, September). Identification of the White-Mold affected Soybean fields by using Multispectral Imageries, Spatial Autocorrelation and Support Vector Machine. In Proceedings of the International Conference on Research in Adaptive and Convergent Systems (pp. 104--109). ACM.
19. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
20. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Berg, A. C. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211--252.

Index Terms

• Applied computing

  • Computers in other domains

    • Agriculture

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Object detection

  • Machine learning

    • Machine learning approaches

      • Neural networks