Identifying selected diseases of leaves using deep learning and transfer learning models

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Afsana Mimi
Sayeda Fatema Tuj Zohura
Muhammad Ibrahim
Riddho Ridwanul Haque
Omar Farrok
Taskeed Jabid
Md Sawkat Ali


Leaf diseases may harm plants in different ways, often causing reduced productivity and, at times, lethal consequences. Detecting such diseases in a timely manner can help plant owners take effective remedial measures. Deficiencies of vital elements such as nitrogen, microbial infections and other similar disorders can often have visible effects, such as the yellowing of leaves in Catharanthus roseus (bright eyes) and scorched leaves in Fragaria × ananassa (strawberry) plants. In this work, we explore approaches to use computer vision techniques to help plant owners identify such leaf disorders in their plants automatically and conveniently. This research designs three machine learning systems, namely a vanilla CNN model, a CNN-SVM hybrid model, and a MobileNetV2-based transfer learning model that detect yellowed and scorched leaves in Catharanthus roseus and strawberry plants, respectively, using images captured by mobile phones. In our experiments, the models yield a very promising accuracy on a dataset having around 4000 images. Of the three models, the transfer learning-based one demonstrates the highest accuracy (97.35% on test set) in our experiments. Furthermore, an Android application is developed that uses this model to allow end-users to conveniently monitor the condition of their plants in real time.

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Mimi, A. ., Zohura, S. F. T. ., Ibrahim, M. ., Haque, R. R. ., Farrok, O. ., Jabid, T. ., & Ali, M. S. (2023). Identifying selected diseases of leaves using deep learning and transfer learning models. Machine Graphics and Vision, 32(1), 55–71.

M. Abadi, A. Agarwal, P. Barham, et al. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from

S. Ahlawat and A. Choudhary. Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Computer Science, 167:2554-2560, 2020. (Crossref)

S. I. Ahmed, M. Ibrahim, Md. Nadim, et al. MangoLeafBD: A comprehensive image dataset to classify diseased and healthy mango leaves. Data in Brief, 47:108941, 2023. (Crossref)

J. Amara, B. Bouaziz, and A. Algergawy. A deep learning-based approach for banana leaf diseases classification. In B. Mitschang et al., editors, Datenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband, pages 79-88, Bonn, 2017. Gesellschaft für Informatik e.V.

L. E. C. Antunes and N. A. Peres. Strawberry production in Brazil and South America. International Journal of Fruit Science, 13(1-2):156-161, 2013. (Crossref)

L. E. C. Antunes and N. A. Peres. Sampling non-relevant documents of training sets for learning-to-rank algorithms. International Journal of Machine Learning and Computing, 10(3):406-415, 2020. (Crossref)

J. Chen, D. Zhang, Y. A. Nanehkaran, and D. Li. Detection of rice plant diseases based on deep transfer learning. Journal of the Science of Food and Agriculture, 100(7):3246-3256, 2020. (Crossref)

S. S. Chouhan, U. P. Singh, and S. Jain. Applications of computer vision in plant pathology: A survey. Archives of Computational Methods in Engineering, 27(2):611-632, 2020. (Crossref)

D. Das, M. Singh, S. S. Mohanty, and S. Chakravarty. Leaf disease detection using Support Vector Machine. In Proc. 2020 Int. Conf. Communication and Signal Processing (ICCSP), pages 1036-1040, Chennai, India, 28-30 Jul 2020. IEEE. (Crossref)

Keras Special Interest Group. Keras. simple. flexible. powerful.

Md. M. Hasana, M. Ibrahim, and Md. S. Ali. Speeding up EfficientNet: Selecting update blocks of convolutional neural networks using genetic algorithm in transfer learning. arXiv, 2023. arXiv:2303.00261v1.

K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR 2016), pages 770-778, Las Vegas, NV, USA, 27-30 Jun 2016. (Crossref)

A. G. Howard, M. Zhu, B. Chen, et al. MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv, 2017. arXiv:1704.04861v1.

A. J. Keutgen and E. Pawelzik. Impacts of NaCl stress on plant growth and mineral nutrient assimilation in two cultivars of strawberry. Environmental and Experimental Botany, 65(2-3):170-176, 2009. (Crossref)

A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 - Proc. 25th Conf. NIPS 2012, volume 25, pages 1097-1105, Lake Tahoe, NV, USA, 3-6 Dec 2012. Curran Associates, Inc.

F. Kurtulmuş. Identification of sunflower seeds with deep convolutional neural networks. Journal of Food Measurement and Characterization, 15:1024–1033, 2021. (Crossref)

Large Scale Visual Recognition Challenge 2012 (ILSVRC2012). Stanford Vision Lab, 2012.

U. Mokhtar, N. El Bendary, A. E. Hassenian, et al. SVM-based detection of tomato leaves diseases. In D. Filev, J. Jabłkowski, J. Kacprzyk, et al., editors, Intelligent Systems'2014. Advances in Intelligent Systems and Computing. Proc. 7th IEEE Int. Conf. Intelligent Systems (IS’2014), volume 323 of Advances in Intelligent Systems and Computing, pages 641-652. Springer, Warsaw, Poland, 24-26 Sep 2015. (Crossref)

T. Muhammad, A. B. Aftab, Md. Ahsan, et al. Transformer-based deep learning model for stock price prediction: A case study on Bangladesh stock market. International Journal of Computational Intelligence and Applications, 22(1):2350013, 2023. (Crossref)

S. Nammi, M. K. Boini, S. D. Lodagala, and R. B. S. Behara. The juice of fresh leaves of Catharanthus roseus Linn. reduces blood glucose in normal and alloxan diabetic rabbits. BMC Complementary and Alternative Medicine, 3(1):4, 2003. (Crossref)

X.-X. Niu and C. Y. Suen. A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recognition, 45(4):1318-1325, 2012. (Crossref)

M. Sandler, A. Howard, M. Zhu, et al. MobileNetV2: Inverted residuals and linear bottlenecks. In Proc. 2018 IEEE/CVF Conf. Computer Vision and Pattern Recognitioneedings (CVPR 2018), pages 4510-4520, Salt Lake City, UT, USA, 18-23 Jun 2018. (Crossref)

F. T. Shohan, A. U. Akash, M. Ibrahim, and M. S. Alam. Crime prediction using machine learning with a novel crime dataset. arXiv, 2022.

S. Sladojevic, M. Arsenovic, A. Anderla, et al. Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience, 2016:3289801, 2016. (Crossref)

S. Sun, Z. Wang, Z. Zhang, et al. The roles of entomopathogenic fungal infection of viruliferous whiteflies in controlling tomato yellow leaf curl virus. Biological Control, 156:104552, 2021. (Crossref)

C. Szegedy, W. Liu, Y. Jia, et al. Going deeper with convolutions. arXiv, 2014. arXiv:1409.4842v1.

D. Tiwari, M. Ashish, N. Gangwar, et al. Potato leaf diseases detection using deep learning. In Proc. 2020 4th Int. Conf. Intelligent Computing and Control Systems (ICICCS 2020), pages 461-466, Madurai, India, 13-15 May 2020. IEEE. (Crossref)

M. Vurro, B. Bonciani, and G. Vannacci. Emerging infectious diseases of crop plants in developing countries: impact on agriculture and socio-economic consequences. Food Security, 2(2):113-132, 2010. (Crossref)

P. E. Waggoner. The aerial dispersal of the pathogens of plant disease. Philosophical Transactions of the Royal Society B, Biological Sciences, 302(1111):451-462, 1983. (Crossref)

Y. Zhong and M. Zhao. Research on deep learning in apple leaf disease recognition. Computers and Electronics in Agriculture, 168:105146, 2020. (Crossref)



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