A Robust Pest Identification System using Morphological Analysis in Neural Networks

Monalisa Mishra, Pradeep Kumar Singh, Aditya Brahmachari, Narayan C Debnath, Prasenjit Choudhury

Abstract


Timely detection of pests play a major role in agriculture. There exist many pest identification systems, but almost all of them suffer from the misclassification due to lighting, background clutter, heterogeneous capturing devices as well as the pest being partially visible or in the different orientation. This misclassification may cause tremendous yield loss. To mitigate this situation, we proposed an architecture to provide high classification accuracy under the aforementioned conditions using morphology and skeletonization along with neural networks as classifiers. We have considered the crop rice as a use case as it is the staple food grain of almost the entire population of India. The amount of pesticides used is highest in rice as compared to all other food grains. This paper offers a robust technique to identify the pests in rice crops. The performance of the proposed architecture is tested with an image dataset, and the experimental results reveal that our proposed approach provides better classification accuracy than the existing pest detection approaches in the literature. Furthermore, the experimental results also provide the performance comparison among the popular classifiers.

Keywords


Image Classification, Orientation, Neural Network, Morphology, Pest identification

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References


A. Salve, How many Farmers does India really have? May 2018.

URL http://www.indiaspend.com/special-reports/how-many-farmers-does- india-really-have-46359/

Sector-Wise Contribution of GDP of India, May, 2018.

URL http://statisticstimes.com/economy/sectorwise-gdp-contribution-of-india.php

Indian Pesticide Industry Report - Pesticide Import Export Statistic, May 2018.

URL https://www.exportgenius.in/blog/indian-pesticides-industry-report-with-import-export-statistics-166.php.

S. K. Balyan, Adverse Effects of Pesticides, April, 2016.

C. Wen, D. E. Guyer, W. Li, “Local Feature based Identification and Classification of Orchard Insects”, Elsevier, Biosystems Engineering, vol. 104, issue 3, pp. 299-307, November, 2009.

K. C. Swain, M. Norremark, R. N. Jorgensen, H. S. Midtiby, O. Green, “Weed Identification using an Automated Active Shape Matching (AASM) Technique”, Elsevier, Biosystems Engineering, vol. 110, no. 4, pp. 450-457, 2011.

T. Liu, W. Chen, W. Wu, C. Sun, W. Guo, X. Zhu, “Detection of Aphids in Wheat Fields using a Computer Vision Technique”, Elsevier, Biosystems Engineering, vol. 141, pp. 82-93, January 2016.

Directorate General of Commercial Intelligence and Statistics, May, 2018.

URL http://dgciskol.gov.in/

Monitoring of Pesticide Residues at National Level, March, 2015.

L. Sun, ZebinWu, J. Liu, L. Xiao, Z. Wei, “Supervised Spectralspatial Hyperspectral Image Classification with Weighted Markov Random Fields”, IEEE Transactions on Geoscience and Remote Sensing, vol. 53, issue 3, pp. 1490-1503, March 2015.

A. M. Afonso, R. Guerra, A. M. Cavaco, P. Pinto, A. Andrade, A. Duarte, D. M. Power, N. T. Marques, “Identification of Asymptomatic Plants infected with Citrus Tristeza Virus from a time series of Leaf

Spectral Characteristics”, Elsevier, Computers and Electronics in Agriculture, vol. 141, pp. 340-350, September 2017.

G. Cavallaro, M. Riedel, M. Richerzhagen, J. A. Benediktsson, A. Plaza, “On Under- standing Big Data Impacts in Remotely Sensed Image Classification using Support Vector Machine Methods”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, issue 10, pp.4634-4696, October 2015.

M. F. A. Jabal, S. Hamid, S. Shuib, I. Ahmad, “Leaf Features Extraction and Recognition Approaches to Classify Plant”, Journal of Computer Science 9(10), pp. 1295-1304, August 2013.

D. S. D. Thepade, M. M. Kalbhor, “Novel Data Mining based Image Classification with Bayes, Tree, Rule, Lazy and Function Classifiers using Fractional Row Mean of Cosine, Sine and Walsh Column Transformed Images”, International Conference on Communication, Information and Computing Technology(ICCICT), January 2015.

R. Girshick, J. Donahue, T. Darell, J. Malik, ‘Rich Feature Hierarchies for Object Detection and Semantic Segmentation”, IEEE, June, 2014.

M. Sujaritha, S. Annadurai, J. Satheeshkumar, S. K. Sharan, L. Mahesh, “Weed Detecting Robot in Sugarcane Fields using Fuzzy Real Time Classifier”, Elsevier, Computers and Electronics in Agriculture, vol. 134, pp. 160-171, March 2017.

P. Wspanialy, M. Moussa, “Early Powdery Mildew Detection System for Application in Greenhouse Automation”, Elsevier, Computers and Electronics in Agriculture, vol. 127, issue C, pp. 487-494, September 2016.

A. Johanne, A. P. , A. Alvarez-Gila, J. Echazarra, S. Rodriguez-Vaamonde, A. D. Navajas, A. Ortiz-Barredo, “Automatic Plant Disease Diagnosis using Mobile Capture Devices, applied on a Wheat use case”, Elsevier, Computers and Electronics in Agriculture, vol. 138, issue C, pp.200-209, June 2017.

A. Kawai, “Prospect for Integrated Pest Management in Tea Cultivation in Japan”, JARQ, vol. 31, issue 3, pp. 213-217, 1997.

International Rice Research Institute(IRRI), May, 2018.

URL http://irri.org/

C. Grigorescu, N. Petkov, M. A. Westenberg, “Contour and Boundary Detection Improved by Surround Suppression of Texture Edges”, Elsevier, Image and Vision Computing, vol. 22, issue 8, pp. 609-622, 2004.

Jothi, H. Inbarani, “Hybrid Tolerance Rough Set Firefly based Supervised Feature Selection for MRI BrainTumor Image Classification”, Elsevier, Applied Soft Computing, vol. 46, issue C, pp. 639-651,

September 2016.




DOI: http://dx.doi.org/10.21533/pen.v7i1.377

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Copyright (c) 2019 Monalisa Mishra, Pradeep Kumar Singh, Aditya Brahmachari, Narayan C Debnath, Prasenjit Choudhury

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License