Image-Based Plant Disease Detection with Deep Learning

International Journal of Computer Trends and Technology (IJCTT)          
© 2018 by IJCTT Journal
Volume-61 Number-1
Year of Publication : 2018
Authors : Ashwin Dhakal, Prof. Dr. Subarna Shakya


MLA Style: Ashwin Dhakal, Prof. Dr. Subarna Shakya "Image-Based Plant Disease Detection with Deep Learning" International Journal of Computer Trends and Technology 61.1 (2018): 26-29.

APA Style: Ashwin Dhakal, Prof. Dr. Subarna Shakya, (2018). Image-Based Plant Disease Detection with Deep Learning. International Journal of Computer Trends and Technology, 61(1), 26-29.

Deep Learning becomes the most accurate and precise paradigms for the detection of plant disease. Leaves of Infected crops are collected and labelled according to the disease. Processing of image is performed along with pixel-wise operations to enhance the image information. It is followed with feature extraction, segmentation and the classification of patterns of captured leaves in order to identify plant leaf diseases. Four classifier labels are used as Bacterial Spot, Yellow Leaf Curl Virus, Late Blight and Healthy Leaf. The features extracted are fit into the neural network with 20 epochs. Several artificial neural network architectures are implemented with the best performance of 98.59% accuracy in determining the plant disease. This was a great success, demonstrating the feasibility of this approach in the field of Plant Disease Diagnosis and high crop yielding.

[1] FAO. (2009, September 23). Retrieved from Food and Agriculture Organization of the Unites States:
[2] Bock C. H., Poole G. H., Parker P. E., Gottwald T. R. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Sciences. 2010;29(2):59–107. doi: 10.1080/07352681003617285.
[3] Mutka A. M., Bart R. S. Image-based phenotyping of plant disease symptoms. Frontiers in Plant Science. 2015;5, article no. 734 doi: 10.3389/fpls.2014.00734.
[4] P.Chaudhary, A. K. Chaudhari, A. N. Cheeran, and S. Godara, “Color transform based approach for disease spot detection on plant leaf,” International Journal of Computer Science and Telecommunications, vol. 3, no. 6, pp. 65–69, 2012.
[5] Qin F., Liu D., Sun B., Ruan L., Ma Z., Wang H. Identification of alfalfa leaf diseases using image recognition technology. PLoSONE. 2016;11(12) doi: 10.1371/journal.pone.0168274.e0168274.
[6] Al Hiary H., Bani Ahmad S., Reyalat M., Braik M., ALRahamneh Z. Fast and Accurate Detection and Classification of Plant Diseases. International Journal of Computer Applications. 2011;17(1):31–38. doi: 10.5120/2183-2754.
[7] Omrani E., Khoshnevisan B., Shamshirband S., Saboohi H., Anuar N. B., Nasir M. H. N. M. Potential of radial basis function-based support vector regression for apple disease detection. Measurement: Journal of the International Measurement Confederation. 2014;55:512–519. doi: 10.1016/j.measurement.2014.05.033.
[8] Hernández-Rabadán D. L., Ramos-Quintana F., Guerrero Juk J. Integrating SOMs and a Bayesian Classifier for Segmenting Diseased Plants in Uncontrolled Environments. Scientific World Journal. 2014;2014 doi: 10.1155/2014/214674.214674.
[9] Barbedo J. G. A. A new automatic method for disease symptom segmentation in digital photographs of plant leaves. European Journal of Plant Pathology. 2016;147(2):349–364. doi: 10.1007/s10658-016-1007-6.
[10] J.G. ArnalBarbedo, “Digital image processing techniques for detecting, quantifying and classifying plant diseases,” SpringerPlus, vol. 2, article 660, pp. 1–12, 2013.
[11] H. Cartwright, Ed., Artificial Neural Networks, Humana Press, 2015.
[12] I.Steinwart and A. Christmann, Support Vector Machines, Springer Science & Business Media, New York, NY, USA, 2008.
[13] LeCun Y., Bengio Y., Hinton G. Deep learning. Nature. 2015;521(7553):436–444. doi: 10.1038/nature14539. Plant Polyphenols, Prenatal Development and Health Outcomes. Biological Systems: Open Access. 2014;03(01) doi: 10.4172/2329-6577.1000e111.
[14] D.M. Hawkins, “The problem of over-fitting,” Journal of Chemical Information and Computer Sciences, vol. 44, no. 1, pp. 1–12, 2004.
[15] A.Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems, 2012.

Convolutional neural network, Deep learning,Plant disease detection, Image processing, Machine learning