Identification of weeds in sugarcane fields through images taken by UAV and Random Forest classifier

IFAC-PapersOnLine

Volumn 49, Issue 16, 2016, Pages 415-420

  Yano, Inacio H ^a,b   Alves, Jose R ^c   Santiago, Wesley E ^d   Mederos, Barbara J T ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b EMBRAPA INFORMÁTICA AGROPECUÁRIA   (Brazil)

^c PONTIFÍCIA UNIVERSIDADE CATÓLICA DE CAMPINAS   (Brazil)

^d UNIVERSIDADE FEDERAL DOS VALES DO JEQUITINHONHA E MUCURI   (Brazil)

Author keywords

images; machine learning; pattern recognition; sugarcane; UAV; weed

Indexed keywords

ANTENNAS; DECISION TREES; HERBICIDES; IMAGE CLASSIFICATION; LEARNING SYSTEMS; PATTERN RECOGNITION; PATTERN RECOGNITION SYSTEMS; RANDOM FORESTS; SUGAR CANE; UNMANNED AERIAL VEHICLES (UAV);

HERBICIDE APPLICATION; IMAGE PATTERN RECOGNITION; IMAGES; RANDOM FOREST CLASSIFIER; SATELLITE IMAGE ANALYSIS; SPECIES RECOGNITION; UAV (UNMANNED AERIAL VEHICLE); WEED;

WEED CONTROL;

EID: 84994779289     PISSN: None     EISSN: 24058963     Source Type: Conference Proceeding    
DOI: 10.1016/j.ifacol.2016.10.076     Document Type: Conference Paper

References (23)

1. Aslan, Ü., Özdemir, İ., Separation of Agricultural Aimed Plantations From The Forest Cover by Using The LANDSAT-5TM and SPOT-4 HRVIR Data in Turkey. International Archives of Photogrammetry. Remote Sensing and Spatial Information Sciences, 36 (2004), 324–327.
2. Azania, A.A.P.M, Azania, C.A.M., Gravena, R., Pavani, M.C.M.D., Pitelli, R.A., Interferência da palha de cana-de-açúcar (Saccharum spp.) na emergência de espécies de plantas daninhas da família Convolvulaceae. Planta daninha, 2002, 207–212.
3. Belgiu, M, Drăguţ, L., Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114 (2016), 24–31.
4. Benlloch, J.V., Sanchez, A., Christensen, S., Walger, M., Weed mapping in cereal crops using image analysis techniques. AgEng96 2 (1996), 1059–1060.
5. Cavalli, R.M., Laneve, G., Fusilli, L., Pignatti, S., Santini, F., Remote sensing water observation for supporting Lake Victoria weed management. Journal of environmental management 90:7 (2009), 2199–2211.
6. Christensen, S., Søgaard, H.T., Kudsk, P., Nørremark, M., Lund, I., Nadimi, E.S., Jørgensen, R., Site‐specific weed control technologies. Weed Research 49:3 (2009), 233–241.
7. Christoffoleti, P.J., Borges, A., Nicolai, M., Carvalho, S.J.P., López-Ovejero, R.F., Monquero, P.A., Carfentrazone-ethyl applied in post-emergence to control Ipomoea spp. and Commelina benghalensis in sugarcane crop. Planta Daninha 24:1 (2006), 83–90.
8. Dixit, A., Hegde, N., & Hiremath, P. S. (2016). Cluster Analysis of Satellite (LISS-III) Images of Earth surface.
9. Fehr, D., Beksi, W.J., Zermas, D., Papanikolopoulos, N., Covariance based point cloud descriptors for object detection and recognition. Computer Vision and Image Understanding, 142 (2016), 80–93.
10. Fukuda, K., & Brown, J. A. (2007). Classification rule extraction by Ant-Miner for weed risk assessment.
11. He, D. C., Wang, L., Baulu, T., & Morin, D. (1993, August). Integration of multispectral and textural analysis applied to SPOT imagery in urban areas. In Geoscience and Remote Sensing Symposium, 1993. IGARSS'93. Better Understanding of Earth Environment., International (pp. 1865-1867). IEEE.
12. Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification.
13. Johnson, L.F., Trout, T.J., Satellite NDVI assisted monitoring of vegetable crop evapotranspiration in California's San Joaquin Valley. Remote Sensing 4:2 (2012), 439–455.
14. Landis, J.R., Koch, G.G., The measurement of observer agreement for categorical data. biometrics, 1977, 159–174.
15. Metre, V., & Ghorpade, J. (2013). An overview of the research on texture based plant leaf classification. arXiv preprint arXiv:1306.4345.
16. Peña, J.M., Torres-Sánchez, J., de Castro, A.I., Kelly, M., López-Granados, F., Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS One, 8(10), 2013, e77151.
17. Rahman, M., Blackwell, B., Banerjee, N., Saraswat, D., Smartphone-based hierarchical crowdsourcing for weed identification. Computers and Electronics in Agriculture 113 (2015), 14–23.
18. Rodrigues, J. D. (1995). Fisiologia da cana-de-açúcar. Botucatu: Unesp.
19. Safavian, S. R., & Landgrebe, D. (1990). A survey of decision tree classifier methodology.
20. Sun, Y.V., Multigenic modeling of complex disease by random forests. Advances in genetics, 72 (2009), 73–99.
21. Viera, A.J., Garrett, J.M., Understanding interobserver agreement: the kappa statistic. Fam Med 37:5 (2005), 360–363.
22. Yun, M., Kim, J., Seo, D., Lee, J., Choi, C., Application possibility of smartphone as payload for photogrammetric UAV system. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 39:B4 (2012), 349–352.
23. Zimmermann, H., Von Wehrden, H., Damascos, M.A., Bran, D., Welk, E., Renison, D., Hensen, I., Habitat invasion risk assessment based on Landsat 5 data, exemplified by the shrub Rosa rubiginosa in southern Argentina. Austral Ecology 36:7 (2011), 870–880.