Detection of Two Types of Weed through Machine Vision System: Improving Site-Specific Spraying

Document Type : Research Article

Authors

• S. Sabzi ¹
• Y. Abbaspour Gilandeh ¹
• H. Javadikia ²

¹ Department of Biosystems Engineering, Faculty of Agricultural and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran

² Department of Biosystems Engineering, College of Agriculture, Razi University, Kermanshah, Iran

https://doi.org/10.22067/jam.v8i1.60647

Abstract

Introduction
With increase in world population, one of the approaches to provide food is using site-specific management system or so-called precision farming. In this management system, management of crop production inputs such as fertilizers, lime, herbicides, seed, etc. is done based on farm location features, with the aim of reducing waste, increasing revenues and maintaining environmental quality. Precision farming involves various aspects and is applicable on farm fields at all stages of tillage, planting, and harvesting. Today, in line with precision farming purposes, and to control weeds, pests, and diseases, all the efforts of specialists in precision farming is to reduce the amount of chemical substances in products. Although herbicides improve the quality and quantity of agricultural production, the possibility of applying inappropriately and unreasonably is very high. If the dose is too low, weed control is not performed correctly. Otherwise, If the dosage is too high, herbicides can be toxic for crops, can be transferred to soil and stay in it for a long time, and can penetrate to groundwater. By applying herbicides to variable rate, the potential for significant cost savings and reduced environmental damage to the products and environment will be possible. It is evident that in large-scale modern agriculture, individual management of each plant without using some advanced technologies is not possible. using machine vision systems is one of precision farming techniques to identify weeds. This study aimed to detect three plant such as Centaurea depressa M.B, Malvaneglecta and Potato plant using machine vision system.
Materials and Methods
In order to train algorithm of designed machine vision system, a platform that moved with the speed of 10.34 was used for shooting of Marfona potato fields. This platform was consisted of a chassis, camera (DFK23GM021,CMOS, 120 f/s, Made in Germany), and a processor system equipped with Matlab 2015 version. The video camera was installed in 60-centimeter height above the ground level. Therefore, all plants in the camera field of view (whether on the crops row or between the rows) were analyzed. This study conducted on 4 hectares of potato fields in Kermanshah–Iran (longitude: 7.03 E; latitude: 4.22 N). The most suitable color space for segmentation plants was HSV color space and most suitable channel of applying threshold was the H channel. In this study, features in two areas of color features, texture features based on gray co-occurrence matrix were extracted. Ultimately, 126 color features and 80 texture features were extracted from each object. In final six features among 206 features were selected.
Results and Discussion
Among 206 extracted features, six effective features including the additional second component of the YCbCr color space, green index minus blue in RGB color space, sum entropy in the neighborhood of 45 degree, diagonal moment in the neighborhood of 0 degree, entropy in the neighborhood of 45 degree, additional third component index in CMY color space were selected using hybrid ANN-PSO. This means that, two set features have the same effect over plants. The result shows that hybrid ANN-SAGA classified Centaurea depressa M.B, Malvaneglecta and Potato plant with 99.61% accuracy. This accuracy is high and this meant that 1. These plants have different 6 selected features, 2. The classifier is very powerful to classify.

Conclusion
1. Plants with similar features make the classification process complicated and less accurate.
2. The presence of shadow on the plants’ leaves reduces the accuracy of the classification.

Keywords

• Classification
• Machine vision
• Segmentation
• Video processing
• Weeds

20.1001.1.22286829.1397.8.1.2.4

References

1. Aakif, A., and M. F. FaisalKhan. 2015. Automatic classification of plants based on their leaves. Biosystem Engineering 139: 66-75.

2. Asaei, H., A. Jafari, and M. Loghavi. 2016. Development and evaluation of a targeted orchard sprayer using machine vision technology. Journal of Agricultural Machinery 6 (2): 362-375. (In Farsi).

3. Gianessi, L. P., and N. P. Reigner. 2007. The value of herbicides in U.S. crop production. Weed Technology 21 (2): 559-566.

4. Golzarian, M. R., and R. A. Frick. 2011. Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis. Plant Methods 7 (1): 7-28.

5. Gonzalez, R. C., R. E. Woods, and S. L. Eddins. 2004. Digital Image Processing Using MATLAB. Prentice Hall.

6. Hlaing, S. H., and A. S. Khaing. 2014. Weed and crop segmentation and classification using area thresholding. International Journal of Research in Engineering and Technology 3: 375-382.

7. Kataoka, T., T. Kaneko, H. Okamoto, and S. Hata. 2003. Crop growth estimation system using machine vision. in Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM).

8. Kazmi, W., F. J. Garcia-Ruiz, J. Nielsen, J. Rasmussen, and H. J. Andersen. 2015. Detecting creeping thistle in sugar beet fields using vegetation indices. Computers and Electronics in Agriculture 112: 10-19.

9. Meyer, G. E., and J. A. C. Neto. 2008. Verification of color vegetation indices for automated crop imaging applications. Computer and Electronic in Agriculture 63 (2): 282-293.

10. Meyer, G. E., T. Mehta, M. F. Kocher, D. A. Mortensen, and A. Samal. 1998. Textural imaging and discriminant analysis for distinguishing weeds for spot spraying. Transactions of the ASAE 41 (4): 1189-1197.

11. Monaco, T. J., and A. S. Grayson, and D. C. Sanders. 1981. Influence of four weed species on the growth, yield, and quality of direct-seeded tomatoes (Lycopersicon esculentum). Weed Science 29 (4): 394-397.

12. Pantazi, X. E., D. Moshou, and C. Bravo. 2016. Active learning system for weed species recognition based on hyperspectral sensing. Biosystems engineering xxx, ARTICLE IN PRESS.

13. Pereira, L. A. M., R. Y. M. Nakamura, G. F. S. d. Souza, D. Martins, and J. P. Papa. 2012. Aquatic weed automatic classification using machine learning techniques. Computers and Electronics in Agriculture 87: 56-63.

14. Rohani, A., and H. Makarian. 2011. Preparation weed management maps using artificial neural networks aimed application at Precision Agriculture. Journal of Agricultural Machinery Engineering 1: 74-83. (In Farsi).

15. Tang, J. L., X. Q. Chen, R. H. Miao, and D. Wang. 2016. Weed detection using image processing under different illumination for site-specific areas spraying. Computers and Electronics in Agriculture 122: 103-111.

16. Woebbecke, D., G. E. Meyer, K. V. Bargen, and D. A. Mortensen. 1995. Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the ASAE 38 (1): 259-269.

17. Woebbecke, D. M., G. E. Meyer, K. V. Bargen, and D. A. Mortensen. 1992. Plant species identification, size, and enumeration using machine vision techniques on near-binary images. Optics in Agriculture and Forestry 1836: 208-219.