Rule based approach to determine nutrient deficiency in paddy leaf images
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Abstract
Rice is one of the most consumed grains among the human society especially in Asia, but is easily affected by the deficiency caused due to lack of nutrient elements. Identifying nutrient deficiencies of paddy crop is very essential in overcoming these and thereby enhancing yield. Color of paddy leaves plays important role in identifying major deficiencies such as NPK (Nitrogen, phosphorus and Potassium) when crop is in its middle of its growth. In order to do so a database of healthy, nitrogen defected, phosphorus defected and potassium defected leaves is created. Color features of both healthy and defected paddy leaves are extracted using HSV color model. Similarly color features of test image is extracted and compared against database properties. Comparison results are validated against the rules set to determine the specific deficiency. The rules are framed based on rigorous experiment. The efficiency can be further increased by including leaf pattern analysis.
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References
Aakanksha, R., Arora, R. and Sharma, S. (2015). Leaf disease detection and grading using computer vision technology and fuzzy logic. International conference on signal processing on integrated networks , IEEE, pp. 500-505.
Baquero, D., Molina, J., Gil, R., Bojacá, C., Franco, H. and Gómez, F. (2014). An image retrieval system for tomato disease assessment. In 2014 XIX Symposium on Image, Signal Processing and Artificial Vision, pp. 1-5.
Dewi, R. K. and Ginardi, R. V. H. (2014). Feature extraction for identification of sugarcane rust disease. In Information, Communication Technology and System (ICTS), International Conference on, pp. 99-104.
Kumar, E. S. and Talasila, V. (2014). Leaf features based approach for automated identification of medicinal plants. International conference on communication and signal processing. IEEE. pp. 210-214.
Ma, L., Fang, J., Chen, Y. and Gong, S. (2010). Color analysis of leaf images of deficiencies and Excess nitrogen content in Soybean Leaves. E-Product E-Science and E-Entertainment, 2010 International conference IEEE. pp 1-3.
Nitin, S. T. and Sagar, S. B. (2014). Image recognition on based crop disease identification system : A survey International Journal of Computer Science and Mobile Computing 3:868-873.
Noor, A. I., Mokhtar, M. H., Rafiqul, Z. K. and Pramod, K M. (2012). Understanding color models: A Review Model 2:265-275.
Parag, B., Doshi, H., Kadakia, M. and Ahmed, R. (2014). . Leafidentification using morphology and structural decomposition. International conference on signal Processing and integrated networks, IEEE. pp. 125-129.
Patil, J. K. and Kumar, R. (2011). Advances in image processing for detection of plant diseases. Journal of Advanced Bioinformatics Applications and Research 2:135-141.
Prasad, S., Peddoju, S. K. and Ghosh, D. (2014). Energy Efficient mobile vision System for plant leaf disease identification, IEEE, WCNC. pp. 3314-3319.
Pujari, J. D., Yakkundimath, R. and Byadgi, A. S. (2014). Identification and classification of fungal disease affected on agriculture/horticulture crops using image processing techniques. In Computational Intelligence and Computing Research (ICCIC), IEEE International Conference. pp. 1-4.
Rothe, P. R and Kshirsagar, R. V. (2015). Cotton leaf disease identification using pattern recognition techniques. International conference on Pervasive computing, IEEE. pp. 1-6.
Shu, W. L. (2013). Soybean Leaves nitrogen elements information collected pre processing based on model identification. International Conference on advanced Computers theory and Engineering IEEE 6:222-226.
Song, K., Liu, Z., Su, H. and Guo, C. (2011). A research of maize disease image recognition of corn based on BP networks. In 2011 third international conference on measuring technology and mechatronics automation. 1:246-249.
Zare, M., Jampour, M. and Farrokhi, I. R. (2011). A heuristic method for gray image pseudo coloring with histogram and RGB layers, In Communication software and network, IEEE. pp. 524-527.
