Crop and weed discrimination using Laws’ texture masks
Keywords:
precision agriculture, crop, weed, texture analysis, classifierAbstract
Computers have become an integral part of human lives. Computers are used in almost every field even in agriculture. Technologies like computer vision-based pattern recognition are being used to detect diseases and pests like weeds affecting the crop. The Weeds are unwanted plants growing among crops competing for nutrients, water, and sunlight. It can significantly reduce the quality and yield of the crops incurring a huge loss to the farmers. This paper investigates the use of texture features extracted from Laws’ texture masks for discrimination of Carrot crops and weeds in digital images. Laws’ texture method is one of the popular methods used to extract texture features in medical image processing, though not much explored in plant-based images or agricultural images. This experiment was carried out on two categories of benchmark digital image datasets of Carrot crop and Carrot weed respectively, which are publicly available. A total of 70 texture features were extracted. The dimensionality reduction technique was used to get the optimal features. These features were then used to train the Random Forest classifier. The results and observations from the experiment showed that the classifier achieved above 94% accuracy. Keywords: precision agriculture, crop, weed, texture analysis, classifier DOI: 10.25165/j.ijabe.20201301.4920 Citation: Kamath R, Balachandra M, Prabhu S. Crop and weed discrimination using Laws’ texture masks. Int J Agric & Biol Eng, 2020; 13(1): 191–197.References
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[34] Suh H K, Hofstee J W, van Henten E J. Improved vegetation segmentation with ground shadow removal using an HDR camera. Precision Agriculture, 2018; 19(2): 218–237.
[35] Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Information Processing & Management, 2009; 45(4): 427–437.
[36] Tharwat A. Classification assessment methods. Applied Computing and Informatics, 2018.
[2] Ivanova Y. From cows to crops: Computer vision for precision agriculture. http://blog.re-work.co/future-of-food-ian-hales-bristol- robotics-lab-agtech-computer-vision. Accessed on [2016-06-12].
[3] Mandal S K, Maity A. Precision farming for small agricultural farm: Indian scenario. American Journal of Experimental Agriculture, 2013; 3(1): 200–217.
[4] Young S L, Meyer G E, Woldt W E. Future directions for automated weed management in precision agriculture. Automation: The future of weed control in cropping systems, 2014: 249–259.
[5] Lottes P, Khanna R, Pfeifer J, Siegwart R, Stachniss C. UAV-based crop and weed classification for smart farming. 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017: 3024–3031.
[6] Gée C, Bossu J, Jones G, Truchetet F. Crop/weed discrimination in perspective agronomic images. Computers and Electronics in Agriculture, 2008; 60(1): 49–59.
[7] Potena C, Nardi D, Pretto A. Fast and accurate crop and weed identification with summarized train sets for precision agriculture. Proceedings of the 14th International Conference on Intelligent Autonomous Systems (IAS-14), Shanghai, China, July 2016; pp.105–121.
[8] Wu X W, Xu W Q, Song Y Y, Cai M X. A detection method of weed in wheat field on machine vision. Procedia Engineering, 2011; 15: 1998–2003.
[9] Burgos-Artizzu X P, Ribeiro A, Guijarro M, Pajares G. Real-time image processing for crop/weed discrimination in maize fields. Computers and Electronics in Agriculture, 2011; 75(2): 337–346.
[10] Jeon H Y, Tian L F, Zhu H P. Robust crop and weed segmentation under uncontrolled outdoor illumination. Sensors, 2011; 11(6): 6270–6283.
[11] Hemming J, Rath T. PA—Precision agriculture: Computer-vision-based weed identification under field conditions using controlled lighting. Journal of agricultural engineering research, 2001; 78(3): 233–243.
[12] Kumar D A, Prema P. A novel approach for weed classification using Curvelet transform and Tamura texture feature (CTTTF) with RVM classification. International Journal of Applied Engineering Research, 2016; 11(3): 1841–1848.
[13] Li Z C, An Q, Ji C Y. Classification of weed species using artificial neural networks based on color leaf texture feature. International Conference on Computer and Computing Technologies in Agriculture 2008: Computer and Computing Technologies in Agriculture II, 2008; 2:
1217–1225.
[14] Kumar D A, Prema P. A review on crop and weed segmentation based on digital images. Multimedia Processing, Communication and Computing Applications, 2013: 279–291.
[15] Agrawal K N, Singh K, Bora G C, Lin D Q. Weed recognition using image-processing technique based on leaf parameters. Journal of Agricultural Science and Technology. B, 2012; 2(8B): 899.
[16] Ab Jabal M F, Hamid S, Shuib S, Ahmad I. Leaf features extraction and recognition approaches to classify plant. Journal of Computer Science, 2013; 9(10): 1295–1304.
[17] Ahmed F, Bari A H, Hossain E, Al-Mamun H A, Kwan P. Performance analysis of support vector machine and bayesian classifier for crop and weed classification from digital images. World Applied Sciences Journal, 2011; 12(4): 432–440.
[18] Slaughter D C, Giles D K, Fennimore S A, Smith R F. Multispectral machine vision identification of lettuce and weed seedlings for automated weed control. Weed Technology, 2008; 22(2): 378–384.
[19] Kadir A, Nugroho L E, Susanto A, Santosa P I. Leaf classification using shape, color, and texture features. arXiv preprint arXiv:1401.4447. 2013 Nov 20.
[20] Wu Y J, Tsai C M, Shih F. Improving leaf classification rate via background removal and ROI extraction. Journal of Image and Graphics, 2016; 4(2): 93–98.
[21] Caglayan A, Guclu O, Can A B. A plant recognition approach using shape and color features in leaf images. International Conference on Image Analysis and Processing 2013: Image Analysis and Processing-ICIAP 2013, 2013: 161–170.
[22] Milioto A, Lottes P, Stachniss C. Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs. 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018: 2229–2235.
[23] Wäldchen J, Mäder P. Plant species identification using computer vision techniques: A systematic literature review. Archives of Computational Methods in Engineering, 2018; 25(2): 507–543.
[24] Haug S, Ostermann J. A crop/weed field image dataset for the evaluation of computer vision based precision agriculture tasks. European Conference on Computer Vision, Springer, Cham, 2014; pp.105–116.
[25] Laws K I. Texture energy measures. Proc. Image understanding workshop, 1979: 47-51.
[26] Lameski P, Zdravevski E, Trajkovik V, Kulakov A. Weed detection dataset with RGB images taken under variable light conditions. International Conference on ICT Innovations 2017: 112–119.
[27] Zuiderveld K. Contrast limited adaptive histogram equalization. Graphics Gems, 1994: 474–485.
[28] Gholami B, Norton I, Tannenbaum A R, Agar N Y. Recursive feature elimination for brain tumor classification using desorption electrospray ionization mass spectrometry imaging. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012: 5258–5261.
[29] Chang C C, Lin C J. LIBSVM: A library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2011; 2(3): 27.
[30] Platt J C. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, 1999; 10(3): 61–74.
[31] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 2011; 12(Oct): 2825–2830.
[32] Breiman L. Random forests. Machine Learning, 2001; 45(1): 5–32.
[33] Fernández-Delgado M, Cernadas E, Barro S, Amorim D. Do we need hundreds of classifiers to solve real world classification problems?. Journal of Machine Learning Research, 2014; 15(1): 3133–3181.
[34] Suh H K, Hofstee J W, van Henten E J. Improved vegetation segmentation with ground shadow removal using an HDR camera. Precision Agriculture, 2018; 19(2): 218–237.
[35] Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Information Processing & Management, 2009; 45(4): 427–437.
[36] Tharwat A. Classification assessment methods. Applied Computing and Informatics, 2018.
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Published
2020-03-02
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Kamath, R., Balachandra, M., & Prabhu, S. (2020). Crop and weed discrimination using Laws’ texture masks. International Journal of Agricultural and Biological Engineering, 13(1), 191–197. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/4920
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Information Technology, Sensors and Control Systems
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