Low-cost convolutional neural network for tomato plant diseases classifiation

Soumia Bensaadi, Ahmed Louchene


Agriculture is a crucial element to build a strong economy, not only because of its importance in providing food, but also as a source of raw materials for industry as well as source of energy. Different diseases affect plants, which leads to decrease in productivity. In recent years, developments in computing technology and machine-learning algorithms (such as deep neural networks) in the field of agriculture have played a great role to face this problem by building early detection tools. In this paper, we propose an automatic plant disease classification based on a low complexity convolutional neural network (CNN) architecture, which leads to faster on-line classification. For the training process, we used more than one 57.000 tomato leaf images representing nine classes, taken under natural environment, and considered during training without background subtraction. The designed model achieves 97.04% classification accuracy and less than 0.2 error, which shows a high accuracy in distinguishing a disease from another.


Convolutional neural network; Deep learning; Low complexity architecture; Tomato plant disease classification;

Full Text:


DOI: http://doi.org/10.11591/ijai.v12.i1.pp162-170


  • There are currently no refbacks.

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

IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

View IJAI Stats