Statistical Image Analysis Based Automated Leaves Classification

Plants recognition and classification is a challenging process due to the high variability in the plants' features and shapes. Numerous methodologies incorporating image processing were improved to tackle this process for early stage recognition of diseases. Leaf recognition has popular/wide range of agriculture practical applications. Consequently, the current work is interested in the recognition and classification of parsley and basil leaves along with the recognition of their infected parts. An image analysis is used to extract different statistical features from the leaves' dataset. From such statistical features a recognition/classification processes are performed to classify the fresh and infected leaves in each leaf type as well as to classify the two-leave species. The classification process was performed using neural network. The experimental results depicted that the classification accuracies for the three tested cases, namely fresh/infected basil, fresh/infected parsley, and fresh basil/parsley were 80%, 80.0%, and 100.0%; respectively.