Image-based Mangifera Indica Leaf Disease Detection using Transfer Learning for Deep Learning Methods
Abstract
Mangifera Indica, ordinarily known as mango, comes from a large tree. The leaf of the mango tree
has human health benefits; the mango leaf extract is used for curing various diseases, including patients
with cancer and diabetes. It also has an anti-oxidant and anti-microbial biological activity. Leaf disease,
including fungal disease, is a severe security threat to nourishment and food paramours. Sometimes, it
leads to decreased productivity and a huge loss for the farmers. Observing and determining whether a
leaf is infected through the naked eye is unreliable and inconsistent. Technology advancement has helped
agriculture people in several ways, and deep learning methods are a promising approach to spotting leaf
diseases with the best accuracy. A mango leaf disease detection model is developed with the pre-trained
model of ResNet18, which is used in transfer learning along with the Fast.ai framework. Around 2000
images were used, including images of healthy and infected leaves. The trained model achieved an accuracy
of 99.88% and performed well compared to the existing state-of-the-art methods.
Keywords
Mango Leaf Disease, Image Classification, ResNet, Transfer Learning, Fast.ai.References
Iqbal, Zahid, et al. ”An automated detection and classification of citrus plant diseases using image processing
techniques: A review.” Computers and electronics in agriculture, 153 (2018): 12-32.
Golhani, Kamlesh, et al. ”A review of neural networks in plant disease detection using hyperspectral data.”
Information Processing in Agriculture, 5.3 (2018): 354-371.
Ma, Juncheng, et al. ”A recognition method for cucumber diseases using leaf symptom images based on
deep convolutional neural network.” Computers and electronics in agriculture, 154 (2018): 18-24.
Ferentinos, Konstantinos P. ”Deep learning models for plant disease detection and diagnosis.” Computers
and electronics in agriculture, 145 (2018): 311-318.
Too, Edna Chebet, et al. ”A comparative study of fine-tuning deep learning models for plant disease identification.”
Computers and Electronics in Agriculture, 161 (2019): 272-279.
Barbedo, Jayme Garcia Arnal. ”A review on the main challenges in automatic plant disease identification
based on visible range images.” Biosystems engineering, 144 (2016): 52-60.
Kamilaris, Andreas, and Francesc X. Prenafeta-Bold. ”Deep learning in agriculture: A survey.” Computers
and electronics in agriculture, 147 (2018): 70-90.
Barbedo, Jayme GA. ”Factors influencing the use of deep learning for plant disease recognition.” Biosystems
engineering, 172 (2018): 84-91.
Ramalingam, Srinivasa Perumal, R. K. Nadesh, and N. C. SenthilKumar. ”Robust face recognition using
enhanced local binary pattern.” Bulletin of Electrical Engineering and Informatics, 7.1 (2018): 96-101.
Kaur, Sukhvir, Shreelekha Pandey, and Shivani Goel. ”Plants disease identification and classification
through leaf images: A survey.” Archives of Computational Methods in Engineering, 26 (2019): 507-530.
Picon, Artzai, et al. ”Deep convolutional neural networks for mobile capture device-based crop disease
classification in the wild.” Computers and Electronics in Agriculture, 161 (2019): 280-290.
Zhang, Xihai, et al. ”Identification of maize leaf diseases using improved deep convolutional neural networks.”
IEEE Access, 6 (2018): 30370-30377.
Lu, Yang, et al. ”Identification of rice diseases using deep convolutional neural networks.” Neurocomputing,
(2017): 378-384.
Mallela, Nikhil Chakravarthy, Rohit Volety, and Nadesh RK. ”Detection of the triple riding and speed
violation on two-wheelers using deep learning algorithms.” Multimedia Tools and Applications 80 (2021):
-8187.
Gandhi, Rutu, et al. ”Plant disease detection using CNNs and GANs as an augmentative approach.” IEEE
International Conference on Innovative Research and Development (ICIRD). IEEE, 2018.
Name1 et al. / Electronic Letters on Computer Vision and Image Analysis 0(0):1-7, 2000 11
Durmu, Halil, Ece Olcay Gne, and Mrvet Krc. ”Disease detection on the leaves of the tomato plants by
using deep learning.” 6th international conference on agro-geoinformatics. IEEE, 2017.
Rao, U. Sanath, et al. ”Deep learning precision farming: grapes and mango leaf disease detection by
transfer learning.” Global Transitions Proceedings, 2.2 (2021): 535-544.
Mohapatra, Madhumini, et al. ”Botanical leaf disease detection and classification using convolutional
neural network: a hybrid metaheuristic enabled approach.” Computers 11.5 (2022): 82
Singh, Uday Pratap, et al. ”Multilayer convolution neural network for the classification of mango leaves
infected by anthracnose disease.” IEEE access 7 (2019): 43721-43729.
Saleem, Rabia, et al. ”Mango leaf disease recognition and classification using novel segmentation and
vein pattern technique.” Applied Sciences 11.24 (2021): 11901.
Published
Downloads
Copyright (c) 2024 Kshitij Dhawan, R. Srinivasa Perumal, NADESH RK
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
