Feature Fusion Approach with Inception-RestNet and Canonical Correlation Analysis for Strawberry Leaf Disease Detection

Strawberries are one of the most demanding horticulture crops due to their flavor and nutritional value. Therefore, the demand for strawberries growing yearly and production of the same is very low. The prime factor of low production is leaf disease because the crop is susceptible to various types of leaf diseases. This causes huge financial losses every year to the farmers. Therefore, adequate action is needed to save strawberry crops from diseases and effectively manage their spread. Farmers were completely dependent on expert skilled personnel to identify crop diseases that may be the reason for the loss of crop yield. Therefore, this research proposed a novel deep-learning method to recognize plant leaf disease to optimize losses. However, with the invention of this method, it is easy to recognize different disease patterns within the plant leaf. The research proposed a feature fusion-based deep learning model named FFIR (Feature Fusion with Inception-RestNet) for recognizing leaf diseases. The features were extracted with a composition of the Inception-RestNet model. Later, the extracted features are fused in a fusion matrix using canonical correlation analysis (CCA). Afterward, the classification was performed with a Convolution Neural network. The proposed model outperformed with an accuracy of 99.34%.