Recognition and location of crop seedlings based on image processing

Yafeng Han; Xinfa Wang; V.I. Onychko; V.M. Zubko; Guohou Li

doi:10.32782/agrobio.2020.4.5

Yafeng Han Henan Institute of Science and Technology, Xinxiang, China https://orcid.org/0000-0002-3085-5910
Xinfa Wang Henan Institute of Science and Technology, Xinxiang, China https://orcid.org/0000-0002-6293-5624
V.I. Onychko Sumy National Agrarian University (Sumy, Ukraine) https://orcid.org/0000-0003-0584-319X
V.M. Zubko Sumy National Agrarian University (Sumy, Ukraine) https://orcid.org/0000-0002-2426-2772
Guohou Li Henan Institute of Science and Technology, Xinxiang, China https://orcid.org/0000-0003-1410-5076

DOI: https://doi.org/10.32782/agrobio.2020.4.5

Keywords: image segmentation; machine vision; color features; green identification; adaptive threshold method.

Abstract

With the development of digital image technology, we can easily obtain a large number of crop growth images. Through effective analysis of the image, the growth information of crops can be obtained, which can better direct agricultural production. The efficiency of traditional seedling growth monitoring is low, especially in large-scale farmland, which takes a lot of time. Artificial method timely restricts scientific decision-making of cultivation crops. The progress of machine vision and image processing technology provides a new way for harmlessly monitoring of crop seedling growth .The results of image analysis can help agricultural producers to understand the growth of crop seedlings quickly and accurately, so as to take effective management as soon as possible. In this paper, the images of sunflower seedling collected in farmland environment are taken as the research object. The main research content is to segment green crops from soil background. Segmentation method of sunflower seedling image based on color features and Ostu threshold segmentation is proposed. The method is simple in calculation, and can adapt to the segmentation of farmland environment images, which lays the foundation for crop recognition process. Based on the image recognition results, the algorithm locates the seedlings. Through the rapid identification of sunflower seedlings, it is possible to fill the gaps with seedlings where the seedlings are less distributed. On the contrary, if the seedlings are too dense, the number of seedlings needs to be reduced. The algorithm provides a basis for precise management. The results show that the algorithm with extra green feature can quickly and effectively identify sunflower seedlings from background, and locate the seedlings based on the image recognition results. This algorithm is not sensitive to soil moisture and light conditions, and is less affected by crop residual coverage, so it can adapt to different soil environment which realize the non-destructive monitoring of sunflower seedlings.

References

1. Hamuda, E., Glavin, M., & Jones, E. (2016). A survey of image processing techniques for plant extraction and segmentation in the field. Computers and Electronics in Agriculture, 125, 184‒199.
2. Iqbal, Z., Khan, M. A., Sharif, M., Shah, J. H., ur Rehman, M. H., & Javed, K. (2018). An automated detection and classification of citrus plant diseases using image processing techniques: A review. Computers and electronics in agriculture, 153, 12‒32.
3. Nguyen, L. H., Zhu, J., Lin, Z., Du, H., Yang, Z., Guo, W., & Jin, F. (2019, April). Spatial-temporal multi-task learning for within-field cotton yield prediction. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Cham, 343‒354.
4. Lei Yaping, Han Yingchun, Wang Guoping, Feng Lu, Yang Beifang, fan Zhengyi, Wei Xiaowen, Wang zhanbiao, Zhi Xiaoyu and Xiong Shiwu (2017). Low altitude digital image diagnosis technology of cotton seedling by UAV. China Cotton, 5, 23343‒354. 25
5. Zhang Meina, Feng Aijing, zhoujianfeng and lvxiaolan (2019). prediction of cotton yield based on visual and spectral images collected by UAV. (English). "Journal of Agricultural Engineering (5), 11.
6. José M. Peña, Jorge Torres-Sánchez, Angélica Serrano-Pérez, Ana I. de Castro and Francisca López-Granados (2015).Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution. Sensors (Basel, Switzerland), 15(3), 5609‒5626.
7. Di Gennaro, S. F., Toscano, P., Cinat, P., Berton, A., & Matese, A. (2019). A precision viticulture UAV-based approach for early yield prediction in vineyard. In Precision agriculture’19, Wageningen Academic Publishers, 370‒378.
8. Filippi, P., Whelan, B. M., Vervoort, R. W., & Bishop, T. F. (2020). Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates. Agricultural Systems, 184, 102894.
9. Tedesco-Oliveira, D., da Silva, R. P., Maldonado Jr, W., & Zerbato, C. (2020). Convolutional neural networks in predicting cotton yield from images of commercial fields. Computers and Electronics in Agriculture, 171, 105307.
10. Samiei, S., Rasti, P., Vu, J. L., Buitink, J., & Rousseau, D. (2020). Deep learning-based detection of seedling development. Plant Methods, 16(1), 1‒11.
11. Jiang, Y., Li, C., Paterson, A. H., & Robertson, J. S. (2019). DeepSeedling: deep convolutional network and Kalman filter for plant seedling detection and counting in the field. Plant methods, 15(1), 1‒19.
12. Feduck, C., McDermid, G. J., & Castilla, G. (2018). Detection of coniferous seedlings in UAV imagery. Forests, 9(7), 432
13. Quan, L., Feng, H., Lv, Y., Wang, Q., Zhang, C., Liu, J., & Yuan, Z. (2019). Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN. Biosystems Engineering, 184, 1‒23
14. Khirade, S. D., & Patil, A. B. (2015). Plant disease detection using image processing. In 2015 International conference on computing communication control and automation IEEE, 768‒771.
15. Singh, V., & Misra, A. K. (2015). Detection of unhealthy region of plant leaves using image processing and genetic algorithm. In 2015 International Conference on Advances in Computer Engineering and Applications IEEE, 1028‒1032.
16. Pujari, J. D., Yakkundimath, R., & Byadgi, A. S. (2015). Image processing based detection of fungal diseases in plants. Procedia Computer Science, 46, 1802‒1808.
17. Oo, Y. M. & Htun, N. C. (2018). Plant leaf disease detection and classification using image processing. International Journal of Research and Engineering, 5(9), 516‒523.
18. Zhang Yibin & Gu Linling (2018). Global sunflower planting area and pesticide market and varieties in recent years. modern pesticide 17(01), 16‒18.
19. Yinjianjun, Shenbaoguo & Chen Shuren (2010). Field weed localization technology based on machine vision. Journal of agricultural machinery, 41(06), 163‒166+192
20. Gong Lixiong (2014). Crop image recognition based on COM VI and double threshold Otsu algorithm. Journal of drainage and irrigation machinery engineering, 000(004), 363‒368.
21. Chen Xiaobang, Zuo Yayao, & Wang Mingfeng (2019). A method of crop image recognition by UAV. Cn109241817a
22. Tian Haifeng, Wu Mingquan, Niu Zheng, Wang Changyao, & Zhao Xin (2015). Recognition of upland crops under complex planting structure based on radarsat-2 image. Acta AGRICULTURAE engi neering Sinica, 31(023), 154‒159.
23. Sun Ming & Ling Yun (2002). Automatic recognition technology of radish seedling based on computer vision. Journal of agricultural machinery, 05, 75‒77.
24. Wang sile, Yang Wenzhu & Lu sukui (2015). Identification methods of green plants in the monitoring image of crop growth in field. Jiangsu Agricultural Sciences, 43(11), 487‒492.
25. Ke Qiuhong, Zhang Junmei & Tian Ye (2013). Fast extraction of green plants from digital images. Computer applications and software, 30(10), 266‒268 + 283.
26. Zhang Zhibin, Luo Xiwen, Hou Fuxiang & Xu Xiaodong (2010). A fast segmentation algorithm for ridge and row structures based on color features. 2010 International Conference on Agricultural Engineering, Shanghai, China.
27. Wang Xue & Guo Xinxin (2018). The method of green crop segmentation based on GR color characteristics. Heilongjiang Science, 9(16), 14‒15 + 19
28. Liu Lijuan, liuzhongpeng and Cheng Fang (2013). Study on image recognition and preprocessing of maize leaf disease during growing period. Henan Agricultural Science, 42(10), 91‒94.
29. Liu Liqiang, Xiang Jianting and Wu Zequan (2012). Research on rapid identification method of healthy seedlings based on color features. Agricultural science and technology and equipment, 06, 26‒28.
30. Zhou Jun, Wang Mingjun & Shao Qiaolin (2013). Adaptive segmentation method of green plants in farmland image. Journal of agricultural engineering, 29(18), 163‒170.
31. Su Boni, Hua Xiyao & Fan Zhenqi (2018). Research on image segmentation of rice diseases based on color features. Computer and Digital Engineering, 08, 1638‒1642.