References
[1]. Asfarian, A., Herdiyeni, Y., Rauf, A., & Mutaqin, K. H.
(2013, November). Paddy diseases identification with
texture analysis using fractal descriptors based on fourier
spectrum. In 2013, International Conference on
Computer, Control, Informatics and Its Applications
(IC3INA) (pp. 77-81). IEEE. https://doi.org/10.1109/IC3INA.
2013.6819152
[2]. Biswas, S., Jagyasi, B., Singh, B. P., & Lal, M. (2014,
June). Severity identification of Potato Late Blight disease
from crop images captured under uncontrolled
environment. In 2014, IEEE Canada International
Humanitarian Technology Conference (IHTC) (pp. 1-5).
IEEE. https://doi.org/10.1109/IHTC.2014.7147519
[3]. Chouhan, S. S., Kaul, A., Singh, U. P., & Jain, S. (2018).
Bacterial foraging optimization based radial basis function
neural network (BRBFNN) for identification and classification
of plant leaf diseases: An automatic approach towards
plant pathology. IEEE Access, 6, 8852-8863.
[4]. Cui, D., Zhang, Q., Li, M., Hartman, G. L., & Zhao, Y.
(2010). Image processing methods for quantitatively
detecting soybean rust from multispectral images.
Biosystems Engineering, 107(3), 186-193. https://doi.org/
10.1016/j.biosystemseng.2010.06.004
[5]. Jadhav, S. B., Udupi, V. R., & Patil, S. B. (2020).
Identification of plant diseases using convolutional neural
networks. International Journal of Information Technology,
1-10. https://doi.org/10.1007/s41870-020-00437-5
[6]. Jena, T., Rajesh, T. M., & Patil, M. (2019). Elitist TLBO for
identification and verification of plant diseases. In Sociocultural
Inspired Metaheuristics (pp. 41-67). Springer.
https://doi.org/10.1007/978-981-13-6569-0_3 .Springer
[7]. Jothiaruna, N., Sundar, K. J. A., & Karthikeyan, B.
(2019). A segmentation method for disease spot images
incorporating chrominance in comprehensive color
feature and region growing. Computers and Electronics in
Agriculture, 165, 0168-1699. https://doi.org/10.1016/j.
compag.2019.104934
[8]. Khamparia, A., Saini, G., Gupta, D., Khanna, A., Tiwari,
S., & de Albuquerque, V. H. C. (2020). Seasonal crops
disease prediction and classification using deep
convolutional encoder network. Circuits, Systems, and
Signal Processing, 39(2), 818-836. https://doi.org/10.
1007/s00034-019-01041-0
[9]. Kumar, S., Sharma, B., Sharma, V. K., Sharma, H., &
Bansal, J. C. (2018). Plant leaf disease identification using exponential spider monkey optimization. Sustainable
Computing: Informatics and Systems, 28. https://doi.org/
10.1016/j.suscom.2018.10.004
[10]. Li, G., Ma, Z., & Wang, H. (2011, October). Image
recognition of grape downy mildew and grape powdery
mildew based on support vector machine. In International
Conference on Computer and Computing Technologies
in Agriculture (pp. 151-162). Heidelberg, Berlin: Springer.
https://doi.org/10.1007/978-3-642-27275-2_17
[11]. Mahlein, A. K., Rumpf, T., Welke, P., Dehne, H. W.,
Plümer, L., Steiner, U., & Oerke, E. C. (2013). Development
of spectral indices for detecting and identifying plant
diseases. Remote Sensing of Environment, 128, 21-30.
[12]. Majid, K., Herdiyeni, Y., & Rauf, A. (2013, September).
I-PEDIA: Mobile application for paddy disease identification
using fuzzy entropy and probabilistic neural network. In
2013, International Conference on Advanced Computer
Science and Information Systems (ICACSIS) (pp. 403-406).
IEEE.
[13]. Rajesh, T. M., Dalawai, K., & Pradeep, N. (2020).
Automatic data acquisition and spot disease identification
system in plants pathology domain: Agricultural
intelligence system in plant pathology domain. In Modern
Techniques for Agricultural Disease Management and
Crop Yield Prediction (pp. 111-141). IGI Global. https://doi.
org/10.4018/978-1-5225-9632-5.ch006
[14]. Ramesh, S., & Vydeki, D. (2020). Recognition and
classification of paddy leaf diseases using Optimized Deep
Neural network with Jaya algorithm. Information Processing
In Agriculture, 7(2), 249-260.
[15]. Rothe, P. R., & Kshirsagar, R. V. (2015, January). Cotton
leaf disease identification using pattern recognition
techniques. In 2015, International Conference on
Pervasive Computing (ICPC) (pp. 1-6). IEEE.
[16]. Rumpf, T., Römer, C., Plümer, L., & Mahlein, A. K.
(2010, July). Optimal wave lengths for an early
identification of Cercospora beticola with Support Vector
Machines based on hyper spectral reflection data. In 2010,
IEEE International Geoscience and Remote Sensing
Symposium (pp. 327-330). IEEE. https://doi.org/10.1109/
IGARSS.2010.5649924
[17]. Wang, H., Li, G., Ma, Z., & Li, X. (2012, May). Image
recognition of plant diseases based on principal
th component analysis and neural networks. In 2012, 8
International Conference on Natural Computation (pp.
246-251). IEEE. https://doi.org/10.1109/ICNC.2012.
6234701
[18]. Wei, Y., Chang, R., Wang, Y., Liu, H., Du, Y., Xu, J., &
Yang, L. (2011, October). A study of image processing on
identifying cucumber disease. In International Conference
on Computer and Computing Technologies in Agriculture
(pp. 201-209). Heidelberg, Berlin: Springer. https://doi.org/
10.1007/978-3-642-27275-2_22
[19]. Zhu, J., Wu, A., Wang, X., & Zhang, H. (2020).
Identification of grape diseases using image analysis and
BP neural networks. Multimedia Tools and Applications,
79(21), 14539-14551.
