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Intelligent Leaf Disease Classification using Machine Learning Technique
Abstract
Agriculture is the major occupation and plays a key function in India. The rural people rely upon on agriculture as their major livelihood. In the plant growing stage, there are typically contaminated with extraordinary diseases. The farmers have ability to perceive the ailment in the early stage and take precautions. But it is impossible to become aware of the ailment caused with the aid of the crop with naked eye. A novel way of training and methodology was used to expedite a quick and easy implementation of the system in practice. The developed model was able to recognise various types of tea leaf disease sout of healthy leaves.
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References
S. J. G. A. Barbedo, “A new automatic method for disease symptom segmentation in digital photographs of plant leaves,” European Journal of Plant Pathology, vol. 147, no. 2, pp. 349–364, 2016.
J. G. A. Barbedo, “A novel algorithm for semi-automatic segmentation of plant leaf disease symptoms using digital image processing,” Tropical Plant Pathology, vol. 41, no. 4, pp. 210–224, 2016.
J. G. A. Barbedo, L. V. Koenigkan, and T. T. Santos, “Identifying multiple plant diseases using digital image processing,” Biosystems Engineering, vol. 147, pp. 104–116, 2016.
J. Pang, Z.-Y. Bai, J.-C. Lai, and S.-K. Li, “Automatic segmentation of crop leaf spot disease images by integrating local threshold and seeded region growing,” 2011 International Conference on Image Analysis and Signal Processing, 2011.
V. Singh and A. Misra, “Detection of plant leaf diseases using image segmentation and soft computing techniques,” Information Processing in Agriculture, 2016.
S. Prasad, S. K. Peddoju, and D. Ghosh, “Unsupervised resolution independent based natural plant leaf disease segmentation approach for mobile devices,” Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop on - I-CARE '13, 2013.
M. G. Du and S. W. Zhang, “Crop Disease Leaf Image Segmentation Based on Genetic Algorithm and Maximum Entropy,” Applied Mechanics and Materials, vol. 713-715, pp. 1670–1674, 2015.
B. Dhaygude & P. Kumbhar, “Agricultural plant Leaf Disease Detection Using Image Processing”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, no. 1, 2013, pp. 599-602.
Z. H. Diao, Y. M. Song, H. Wang, and Y. P. Wang, “Study Surveys on Image Segmentation of Plant Disease Spot,” Advanced Materials Research, vol. 542-543, pp. 1047– 1050. 2012.
J. Y. Bai and H. E. Ren, “An Algorithm of Leaf Image Segmentation Based on Color Features,” Key Engineering Materials, vol. 474-476, pp. 846–851, 2011.
J. Y. Bai and H. E. Ren, “An Algorithm of Leaf Image Segmentation Based on Color Features,” Key Engineering Materials, vol. 474-476, pp. 846–851, 2011.
N. Valliammal and S. S.n.geethalakshmi, “A Novel Approach for Plant Leaf Image Segmentation using Fuzzy Clustering,” International Journal of Computer Applications, vol. 44. no. 13, pp. 10–20, 2012.
L. Kaiyan, W. Junhui, C. Jie, and S. Huiping, “A Real Time Image Segmentation Approach for Crop Leaf,” 2013 Fifth International Conference on Measuring Technology and Mechatronics Automation, 2013.
K. R. Gavhale, U. Gawande, and K. O. Hajari, “Unhealthy region of citrus leaf detection using image processing techniques,” International Conference for Convergence for Technology-2014, 2014.
R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610–621, 1973.
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