A hybrid image processing algorithm to examine diseases in citrus leaves and fruits

Agricultural productivity is problematic while the plant attacks several micro-organisms, viruses, and bacterial infections. Earlier disease identification is a unity regarding the challenging solutions for increasing plant production. The signs from these attacks are generally identified in the leaves, fruit, and stems inspection. Nowadays, the need for computational diagnosis of plant diseases increases to gain efficient plant productivity. Here, the process identifies and analyzes the Citrus leaf and Fruits disease literally from the infected images by adopting image-processing procedures to distinguish leaf infections from digital-images. The suggested hybrid algorithm includes pre-processing and RGB HSI conversion. The initial step practices CLAHE segments the features of affected areas applying K-means clustering and statistical GLCM. The SVM classifier has recognized the diseased image and performs those methods in citrus disease detection. Finally, the Fuzzy-based estimation has been convoluted to measure the disease grade severity.