Disease detection through corn leaves using DEEP Learning for farmers in the Curahuasi Abancay district-2024
Article Sidebar
Main Article Content
Abstract
maize is an important crop in Perú, essential for food security. Howevewr, its cultivation faces significant chakkenges due to debilitating diseases such as corn thips and corn rust virus, which can lead to severe yield losses. Traditional methods of plant disease disease diagnosis are often time consuming and error prone, requiring mode efficient approach. This study explores the application of deep learning, especially convolutional neural networks (CNNs), in the automatic detection and classification of maize diseases. The objective of this study is to compare the accuracy of two architectures. Basic CNN and ResNet18, the test image used a data set consisting of 3087 images comprising of maize leaf diseases, classes comsisting of, Thirips, Rust and Healthy Leaves, in addition, we performed hyperparameter adjustment to improve model performance and gradient-weighted class activation mapping for model interpretability. Qur results show that the ResNet18 model demonstrated an accuracy of 99.38% in distinguishing between blankets. The results of this study contribute to the advancement of al applications in agriculture, particulary in the diagnosis of maize diseases in Curahuasi Perú.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
When an author creates an article and publishes it in a journal, the copyright passes to the journal as part of the publishing agreement. Therefore, the journal becomes the owner of the rights to reproduce, distribute and sell the article. The author retains some rights, such as the right to be recognized as the creator of the article and the right to use the article for his or her own scholarly or research purposes, unless otherwise agreed in the publication agreement.
How to Cite
References
V. Singh, Varsha, and A. K. Misra, “Detection of unhealthy region of plant leaves using image processing and genetic algo-rithm,” in 2015 International Conference on Advances in Com-puter Engineering and Applications, 2015, pp. 1028–1032. doi: 10.1109/ICACEA.2015.7164858.
Y. Li and J. Yang, “Few-shot cotton pest recognition and termi-nal realization,” Comput. Electron. Agric., vol. 169, p. 105240, Feb. 2020, doi: 10.1016/J.COMPAG.2020.105240.
F. Saeed, M. A. Khan, M. Sharif, M. Mittal, L. M. Goyal, and S. Roy, “Deep neural network features fusion and selection based on PLS regression with an application for crops diseases classi-fication,” Appl. Soft Comput., vol. 103, p. 107164, May 2021, doi: 10.1016/J.ASOC.2021.107164.
P. J. Garcia Mendoza, “El cultivo del maíz en el mundo y en Perú ‘The cultivation of corn in the world and in Peru,’” Peru, vol. 1, no. 0, pp. 1–7, 2017.
J. K. Patil, “Color Feature Extraction of Tomato Leaf Diseases,” Int. J. Eng. Trends Technol., vol. 2, no. 2, pp. 72–74, 2011.
L. C. G. Clotilde Quispe Bustamante, Franco Oviedo Anguis, Maiz Amiláceo. 2011.
J. R. León-Murillo et al., “Tortillas Made With Extruded Flours of Blue Maize and Chía Seeds As an Nutritious and Nutraceutical Food Option,” Agrociencia, vol. 55, no. 6, pp. 487–506, 2021, doi: 10.47163/agrociencia.v55i5.2555.
D. G. Gabriela, “Trips: otra plaga más que ataca al maíz,” 16-05-2013. [Online]. Available: https://semillastodoterreno.com/2013/05/trips-otra-plaga-mas-que-ataca-al-maiz#:~:text=El trips (Frankliniella spp.),las primeras etapas del cultivo.
D. N. S. and D. P. M. and A. V. and J. A. T. and E. P. R. and H. H. Klump, “Restoration of native folding of single-stranded DNA sequences through reverse mutations: An indication of a new epigenetic mechanism,” Arch. Biochem. Biophys., vol. 453, no. 0003–9861, pp. 108–122, 2006, doi: https://doi.org/10.1016/j.abb.2005.12.009.
P. Boddupalli et al., “Maize lethal necrosis (MLN): Efforts to-ward containing the spread and impact of a devastating trans-boundary disease in sub-Saharan Africa,” Virus Res., vol. 282, no. October 2019, p. 197943, 2020, doi: 10.1016/j.virusres.2020.197943.
J. Chai, H. Zeng, A. Li, and E. W. T. Ngai, “Deep learning in computer vision: A critical review of emerging techniques and application scenarios,” Mach. Learn. with Appl., vol. 6, no. March, p. 100134, 2021, doi: 10.1016/j.mlwa.2021.100134.
K. Aruleba et al., “Applications of computational methods in biomedical breast cancer imaging diagnostics: A review,” J. Im-aging, vol. 6, no. 10, 2020, doi: 10.3390/JIMAGING6100105.
A. Waheed, M. Goyal, D. Gupta, A. Khanna, A. E. Hassanien, and H. M. Pandey, “An optimized dense convolutional neural network model for disease recognition and classification in corn leaf,” Comput. Electron. Agric., vol. 175, no. January, p. 105456, 2020, doi: 10.1016/j.compag.2020.105456.
T. O’Halloran, G. Obaido, B. Otegbade, and I. D. Mienye, “A deep learning approach for Maize Lethal Necrosis and Maize Streak Virus disease detection,” Mach. Learn. with Appl., vol. 16, no. May, p. 100556, 2024, doi: 10.1016/j.mlwa.2024.100556.
F. S. I. and I. A. R. and R. N. Said, “Identification of maize leaves infected by fall armyworms using UAV-based imagery and convolutional neural networks,” Comput. Electron. Agric., vol. 184, no. 0168–1699, p. 106124, 2021, doi: https://doi.org/10.1016/j.compag.2021.106124.
E. L. and L. W. and Q. X. and R. G. and Z. S. and Y. Li, “A novel deep learning method for maize disease identification based on small sample-size and complex background datasets,” Ecol. In-form., vol. 75, no. 1574–9541, p. 102011, 2023, doi: https://doi.org/10.1016/j.ecoinf.2023.102011.
K. Golhani, S. K. Balasundram, G. Vadamalai, and B. Pradhan, “A review of neural networks in plant disease detection using hyperspectral data,” Inf. Process. Agric., vol. 5, no. 3, pp. 354–371, 2018, doi: 10.1016/j.inpa.2018.05.002.
G. LeCun, Yann; Bengio, Yoshua; Hinton, “Deep learning,” Nature, vol. 436, no. 7553, p. 444, 2015, doi: 10.1038/nature14539.
L. Yann and B. Yoshua, “Convolutional Networks for Images, Speech, and Time-Series,” Handb. brain theory neural networks, pp. 255–258, 1995, [Online]. Available: https://www.researchgate.net/profile/Yann_Lecun/publication/2453996_Convolutional_Networks_for_Images_Speech_and_Time-Se-ries/links/0deec519dfa2325502000000.pdf%0Ahttps://www.researchgate.net/profile/Yann_Lecun/publication/2453996_Convolutional_Networks_fo
S. R. G. Reddy, G. P. S. Varma, and R. L. Davuluri, “Resnet-based modified red deer optimization with DLCNN classifier for plant disease identification and classification,” Comput. Electr. Eng., vol. 105, no. November 2022, p. 108492, 2023, doi: 10.1016/j.compeleceng.2022.108492.
I. A. and B. A. M. and H. Faris, “Oriented stochastic loss descent algorithm to train very deep multi-layer neural networks with-out vanishing gradients,” Knowledge-Based Syst., vol. 230, no. 0950–7051, p. 107391, 2021, doi: https://doi.org/10.1016/j.knosys.2021.107391.
E. Mortaz, “Imbalance accuracy metric for model selection in multi-class imbalance classification problems,” Knowledge-Based Syst., vol. 210, no. 0950–7051, p. 106490, 2020, doi: https://doi.org/10.1016/j.knosys.2020.106490.