  Identification of plant disease is usually done through visual inspection or
during laboratory examination which causes delays resulting in yield loss by
the time identification is complete. On the other hand, complex deep learning
models perform the task with reasonable performance but due to their large size
and high computational requirements, they are not suited to mobile and handheld
devices. Our proposed approach contributes automated identification of plant
diseases which follows a sequence of steps involving pre-processing,
segmentation of diseased leaf area, calculation of features based on the
Gray-Level Co-occurrence Matrix (GLCM), feature selection and classification.
In this study, six color features and twenty-two texture features have been
calculated. Support vector machines is used to perform one-vs-one
classification of plant disease. The proposed model of disease identification
provides an accuracy of 98.79% with a standard deviation of 0.57 on 10-fold
cross-validation. The accuracy on a self-collected dataset is 82.47% for
disease identification and 91.40% for healthy and diseased classification. The
reported performance measures are better or comparable to the existing
approaches and highest among the feature-based methods, presenting it as the
most suitable method to automated leaf-based plant disease identification. This
prototype system can be extended by adding more disease categories or targeting
specific crop or disease categories.
