PNN based crop disease recognition with leaf image features and meteorological data

Shi Yun, Wang Xianfeng, Zhang Shanwen, Zhang Chuanlei

Abstract


An automatic crop disease recognition method was proposed in this paper, which combined the statistical features of leaf images and meteorological data. The images of infected crop leaves were taken under different environments of the growth periods, temperature and humidity. The methods of image morphological operation, contour extraction and region growing algorithm were adopted for leaf image enhancement and spot image segmentation. From each image of infected crop leaf, the statistical features of color, texture and shape were extracted by image processing, and the optimal meteorological features with the highest accuracy rate were obtained and selected by the attribute reduction algorithm. The fusion feature vector of the image was formed by combining the statistical features and the meteorological features. Then the probabilistic neural networks (PNNs) classifier was adopted to evaluate the classification accuracy. The experimental results on three cucumber diseased leaf image datasets, i.e., downy mildew, blight and anthracnose, showed that the crop diseases can be effectively recognized by the integrated application of leaf image processing technology, the disease meteorological data and PNNs classifier, and the recognition accuracy rate was higher than 90%, which indicated that the PNNs classifier trained on the disease feature coefficients extracted from the crop disease leaves and meteorological data could achieve higher classification accuracy.
Keywords: image processing, crop disease recognition, disease meteorological data, morphology, probabilistic neural networks (PNNs)
DOI: 10.3965/j.ijabe.20150804.1719

Citation: Shi Y, Wang X F, Zhang S W, Zhang C L. PNN based crop disease recognition with leaf image features and meteorological data. Int J Agric & Biol Eng, 2015; 8(4): 60－68.

Keywords


image processing, crop disease recognition, disease meteorological data, morphology, probabilistic neural networks (PNNs)

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References


Guan Z X, Yao Q, Yang B J, Hu J, Tang J. Application of digital image processing technology in recognizing the diseases, pests, and weeds from crops. Scientia Agricultura Sinica, 2009; 42(7): 2349–2358. (in Chinese with English abstract)

Sun H, Li M Z, Zhang Q. Detection system of smart sprayer: Status, challenges, and perspectives. Int J Agric & Biol Eng, 2012; 5(3): 10–23.

Niphadkar N P, Burks T F, Qin J W, Ritenour M A. Estimation of citrus canker lesion size using hyperspectral reflectance imaging. Int J Agric & Biol Eng, 2013; 6(3): 41–51.

Ananthi S, Vishnu V S. Detection and Classification of Crop Leaf Diseases. International Journal of Research in Engineering & Applied Sciences, 2012; 2(2): 763–773.

Bock C H, Poole G H, Parker P E, Gottwald T R. Crop disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Crop Sciences, 2010; 29(1-3): 59–107.

Tucker C C, Chakraborty S. Quantitative assessment of lesion characteristics and disease severity using digital image processing. J. Phytopathology, 1997; 145(7): 273–278.

Zhang S, Zhang C. Maize disease recognition based on local discriminant algorithm. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(11): 167–172. (in Chinese with English abstract)

Bashish D A, Braik M, Bani–Ahmad S. Detection and classification of leaf diseases using K–means based segmentation and neural–networks–based classification. Inform. Technol. J., 2011; 10(2): 267–275.

Camargo A, Smith J S. An image processing based algorithm to automatically identify plant disease visual symptoms. Bio. Systems Engineering, 2009; 102(1): 9–21.

Hillnhuetter C, Mahlein A K. Early detection and localization of sugar beet diseases: new approaches. Gesunde Pfianzen, 2008; 60 (4): 143–149.

Rumpfa T, Mahleinb A K, Steinerb U, Oerkeb E C, Dehneb H W, Plümera L. Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 2010; 74(1): 91–99.

Al–Hiary H, Bani–Ahmad S, Reyalat M, Braik M, ALRahamneh Z. Fast and accurate detection and classification of crop diseases. International Journal of Computer Applications, 2011; 17(1): 31–38.

Gulhane V A, Gurjar A A. Detection of Diseases on Cotton Leaves and Its Possible Diagnosis. International Journal of Image Processing (IJIP), 2011; 5(5): 590–598.

Jayamala K Patil, Raj Kumar. Color Feature Extraction of Tomato Leaf Diseases. International Journal of Engineering Trends and Technology, 2011; 2(2): 72–74.

Chaudhary P, Chaudhari A K, Cheeran A N, Godara S. Color Transform Based Approach for Disease Spot Detection on Crop Leaf. International Journal of Computer Science and Telecommunications, 2012; 3(6): 65–70.

Li G L, Ma Z H, Huang C, Chi Y W, Wang H G. Segmentation of color images of grape diseases using K–means clustering algorithm. Transactions of the CSAE, 2010; 26(Supp 2): 32–37. (in Chinese with English abstract)

Bauer S D, Korc F, Forstner W. The Potential of Automatic Methods of Classification to identify Leaf diseases from Multispectral images. Precision Agric., 2011; 12(3): 361–377.

Valliammal N, Geethalakshmi S N. Crop leaf segmentation using nonlinear K-means clustering. IJCSI International Journal of Computer Science Issues, 2012; 9(3): 212–218.

Jaware T H, Badgujar R D, Patil P G. Crop disease detection using image segmentation. World Journal of Science and Technology, 2012; 2(4): 190–194.

Wang N, Wang K R, Xie R Z, Lai J C, Ming B, Li S K. Maize leaf disease identification based on Fisher discrimination analysis. Scientia Agricultura Sinica, 2009; 42(11): 3836–3842. (in Chinese with English abstract)

Song K, Sun X Y, Ji J W. Corn leaf disease recognition based on support vector machine method. Transactions of the CSAE, 2007; 23(1): 155–157. (in Chinese with English abstract)

Yang W, Sun C, Du H, Yang J. Feature extraction using Laplacian maximum margin criterion. Neural Processing Letters, 2011; 33(1): 99–110.

Li G L. Preliminary Study on Automatic Diagnosis and Classification Method of Crop Diseases based on Image Recognition Technology. Beijing: China Agricultural University, 2011; pp.1–64. (in Chinese with English abstract)

Chai A L, Li B J, Shi Y X, Cen Z X, Huang H Y, Liu J. Recognition of tomato foliage disease based on computer vision technology. Acta Horticulturae Sinica, 2010; 37(9): 1423–1430. (in Chinese with English abstract)

Dhaygude S B, Kumbhar N. Agricultural crop leaf disease detection using image processing. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2013; 2(1): 599–602.

Phadikar S, Sil J. Rice disease identification using pattern recognition techniques. In Proceedings of 11th International Conference on Computer and Information Technology. Khulna, Bangladesh, 2008; pp.420–423.

Burrascano P. Learning vector quantization for the probabilistic neural network. IEEE Transactions on Neural Networks, 1991; 2 (4): 458–461.

Lin S, Kung S Y, Lin L. Face recognition/detection by probabilistic decision–based neural network, IEEE Transactions on Neural Networks, 1997; 8(1): 114–132.

Mao K Z, Tan K C, Ser W. Probabilistic neural–network structure determination for pattern classification. IEEE Transactions on Neural Networks, 2000; 11(4): 1009–1016.

Yang Z, Yang Z, Lu W, Harrison R G, Eftestol T, Steen P A. A probabilistic neural network as the predictive classifier of out-of-hospital defibrillation outcomes. Resuscitation, 2005; 64(1): 31–36.

Wang H, Li G, Ma Z, Li X. Image recognition of crop diseases based on principal component analysis and neural networks. In Proceedings of 2012 8th International Conference on Natural Computation. Chongqing, China, 2012; pp.246–251.




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