An Integrated Deep Learning Framework for Fruits Diseases Classification

Abstract

Agriculture has been an important research area in the field of image processing for the last five years. Diseases affect the quality and quantity of fruits, thereby disrupting the economy of a country. Many computerized techniques have been introduced for detecting and recognizing fruit diseases. However, some issues remain to be addressed, such as irrelevant features and the dimensionality of feature vectors, which increase the computational time of the system. Herein, we propose an integrated deep learning framework for classifying fruit diseases. We consider seven types of fruits, i.e., apple, cherry, blueberry, grapes, peach, citrus, and strawberry. The proposed method comprises several important steps. Initially, data increase is applied, and then two different types of features are extracted. In the first feature type, texture and color features, i.e., classical features, are extracted. In the second type, deep learning characteristics are extracted using a pretrained model. The pretrained model is reused through transfer learning. Subsequently, both types of features are merged using the maximum mean value of the serial approach. Next, the resulting fused vector is optimized using a harmonic threshold-based genetic algorithm. Finally, the selected features are classified using multiple classifiers. An evaluation is performed on the PlantVillage dataset, and an accuracy of 99% is achieved. A comparison with recent techniques indicate the superiority of the proposed method.