Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm

By M. Sardoğan, Adem Tuncer, Yunus Ozen. 2018 3rd International Conference on Computer Science and Engineering (UBMK), 2018

398 citations recorded by the source corpus.

The early detection of diseases is important in agriculture for an efficient crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes. Automatic methods for classification of plant diseases also help taking action after detecting the symptoms of leaf diseases. This paper presents a Convolutional Neural Network (CNN) model and Learning Vector Quantization (LVQ) algorithm based method for tomato leaf disease detection and classification. The dataset contains 500 images of tomato leaves with four symptoms of diseases. We have modeled a CNN for automatic feature extraction and classification. Color information is actively used for plant leaf disease researches. In our model, the filters are applied to three channels based on RGB components. The LVQ has been fed with the output feature vector of convolution part for training the network. The experimental results validate that the proposed method effectively recognizes four different types of tomato leaf diseases.

Subfields: Plant Science

View Paper: https://www.semanticscholar.org/paper/4705118486ee16e58e2b1b208a1430e62c7132c1

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Plant Science

1. Wubetu Barud Demilie
2. Paulo Ferreira
3. Flavio de Barros Vidal
4. Andre da Silva Abade

Citations in the corpus

• Plant disease detection and classification techniques: a comparative study of the performances (2024, background): “Early detection and classification of plant diseases is critical for increasing agricultural productivity [8, 9].”
• Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, background): “Studies that investigate only one type of crop in particular highlight those that address the phytopathology of Tomato crop with 18 investigations (S3, S9, S27, S44, S48, S55, S58, S61, S67, S70, S75, S78, S86, S93, S99, S102, S105, S108).”