Classification of Tomato Plants’ Leaf Diseases Using Image Segmentation and SVM

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Zaka ud Din
Syed M. Adnan
Rao Wakeel Ahmad
Sumair Aziz
Waqar Ismail
Javed Iqbal


Plant Disease recognition and classification play vital role in agriculture field. Product quality, quantity or productivity of plants is harshly affected by slight negligence in this domain. Huge amount of human work load for crops intensive care in vast farms can be reduced by automatic system accomplished of perceiving and classifying plant illnesses at early phases. This paper presents image processing framework for plant disease identification and classification. Proposed framework mainly consists of two stages that are feature extraction and classification. We used GLCM for feature extraction along with Support Vector Machines classifier by using content-based image retrieval feature descriptor. Experiments were conducted on Tomato leaf dataset comprising of 4 different classes. Proposed framework achieved 98.3% overall accuracy.

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