CNN Architecture for Classifying Types of Mango Based on Leaf Images

Nur Nafi'iyah, Jauharul Maknun


In such conditions, it is necessary to have a system that can automatically classify plant species or identify types of plant diseases using either machine learning or deep learning. The plant classification system for ordinary people who are not familiar with the field of crops is not an easy job, it requires in-depth knowledge of the field from the experts. This study proposes a system for identifying mango plant species based on leaves using the CNN method. The reason for proposing the CNN method from previous research is that the CNN method produces good accuracy. Most previous studies to classify plant species use the leaves of the plant. The purpose of this study is to propose a CNN architectural model in classifying mango species based on leaf imagery. The input image of colored mango tree leaves measuring 224x224 is trained based on the CNN architectural model that was built. There are 4 CNN architectural models proposed in the study and 1 transfer learning InceptionV4. Based on the evaluation test results of the proposed CNN architectural model, that the best architectural model is the third. The number of parameters of the third CNN architecture is 1,245,989 with loss values and accuracy during evaluation are 1,431 and 0.55. The largest number of parameters is transfer learning InceptionV3 21,802,784, but transfer learning shows the lowest accuracy value and the highest loss, namely 0.2, and 1.61.


Mango leaves; classification; transfer learning; CNN architecture

Full Text:

PDF (Indonesian)


Aakif, A., & Khan, M. F. (2015). Automatic classification of plants based on their leaves. Biosystems Engineering, 139.

Arivazhagan, S., Shebiah, R. N., Ananthi, S., & Vishnu Varthini, S. (2013). Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricultural Engineering International: CIGR Journal, 15(1).

Arya, S., & Singh, R. (2019). A Comparative Study of CNN and AlexNet for Detection of Disease in Potato and Mango leaf. IEEE International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2019.

Chouhan, S. S., Kaul, A., & Singh, U. P. (2019). A deep learning approach for the classification of diseased plant leaf images. Proceedings of the 4th International Conference on Communication and Electronics Systems, ICCES 2019.

Delgado, N. O., Arboleda, E. R., Dioses, J. L., & Dellosa, R. M. (2019). Identification of mango leaves using artificial intelligence. International Journal of Scientific and Technology Research, 8(12), 2864–2868.

Dutta, L., & Basu, T. K. (2013). Extraction And Optimization Of Leaves Images Of Mango Trees And Classification Using Ann. International Journal of Recent Advances in Engineering & Technology (IJRAET) ISSN (Online) 2347-2812, 1(3), 46–51.

Kaur, S., Pandey, S., & Goel, S. (2019). Plants Disease Identification and Classification Through Leaf Images: A Survey. Archives of Computational Methods in Engineering, 26(2).

Madiwalar, S. C., & Wyawahare, M. V. (2017). Plant disease identification: A comparative study. 2017 International Conference on Data Management, Analytics and Innovation, ICDMAI 2017.

Mia, M. R., Roy, S., Das, S. K., & Rahman, M. A. (2020). Mango leaf disease recognition using neural network and support vector machine. Iran Journal of Computer Science, 3(3).

Mishra, S., Ellappan, V., Satapathy, S., Dengia, G., Mulatu, B. T., & Tadele, F. (2021). Identification and classification of mango leaf disease using wavelet transform based segmentation and wavelet neural network model. Annals of the Romanian Society for Cell Biology, 25(2).

Nafi’iyah, N. (2020). Tuber Type Classification Based on Image of Bulbs with Deep Learning. 7th International Conference on ICT for Smart Society: AIoT for Smart Society, ICISS 2020 - Proceeding.

Prasad, S., Peddoju, S. K., & Ghosh, D. (2016). Multi-resolution mobile vision system for plant leaf disease diagnosis. Signal, Image and Video Processing, 10(2).

Prasetyo, E. (2016). Detection of mango tree varieties based on image processing. Indonesian Journal of Science and Technology, 1(2).

Ranjan, M., Weginwar, M. R., Joshi, N., & Ingole, P. A. B. (2015). Detection and Classification of Leaf Disease using Artificial Neural Network. International Journal of Technical Research and Application(IJTRA), 3(3).

Razi, F. A. (2012). An Analysis of COVID-19 using X-ray Image Segmentation based Graph Cut and Box Counting Fractal Dimension. Telematika, 14(1).

Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plümer, L. (2010). Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74(1).

Saldana Ochoa, K., & Guo, Z. (2019). A framework for the management of agricultural resources with automated aerial imagery detection. Computers and Electronics in Agriculture, 162.

Samajpati, B. J., & Degadwala, S. D. (2016). Hybrid approach for apple fruit diseases detection and classification using random forest classifier. International Conference on Communication and Signal Processing, ICCSP 2016.

Singh, U. P., Chouhan, S. S., Jain, S., & Jain, S. (2019). Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease. IEEE Access, 7.

Srunitha, K., & Bharathi, D. (2018). Mango leaf unhealthy region detection and classification. In Lecture Notes in Computational Vision and Biomechanics (Vol. 28).

Yamparala, R., Challa, R., Kantharao, V., & Krishna, P. S. R. (2020). Computerized classification of fruits using convolution neural network. 2020 7th International Conference on Smart Structures and Systems, ICSSS 2020.

Zarrin, I., & Islam, S. (2019). Leaf Based Trees Identification Using Convolutional Neural Network. 2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019.



  • There are currently no refbacks.


Indexed by:       


ISSN 2442-4528 (online) | ISSN 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto Telp (0281) 623321 Fax (0281) 621662


Creative Commons License
This work is licensed under a  Creative Commons Attribution 4.0 International License.