Detection of Hazelnut Varieties and Development of Mobile Application with CNN Data Fusion Feature Reduction-Based Models

In many crops worldwide, including hazelnuts, the majority of stages in production and delivery to end-users are conducted either manually or with machine equipment lacking the advancements brought by technology. Non-destructive, fast, and reliable methods, particularly deep learning algorithms, have emerged as prominent techniques for determining product quality and classification in fruits, vegetables, and cereal products in recent years. This study aims to classify hazelnuts using deep learning algorithms, thereby minimizing the labor, time, and cost expended during the sorting process. Hazelnut images were obtained from Giresun, Ordu, and Van hazelnut varieties. The dataset consists of 1165 images of Giresun, 1324 images of Ordu, and 1138 images of Van hazelnut varieties. The classification was performed using deep learning models such as InceptionV3 and ResNet50. To combine the classification capabilities of the models, an InceptionV3 + ResNet50 data fusion model was created using the data fusion method. In addition, feature reduction processes were conducted by adding a convolutional layer to the data fusion model to decrease the number of features. The classification was conducted using a total of 3627 images, resulting in a 100% classification accuracy. Furthermore, the classification times of all models were analyzed. Based on these analyses, the 1024 reduced features data fusion model with 100% classification accuracy exhibited the shortest classification time. This model was selected, and a mobile application was developed for easy on-field hazelnut classification. The hazelnut classification performed using deep learning algorithms in the application will facilitate the work of both non-experts and professionals in industrial and personal domains. Through these methods, patents for products and devices developed for use in different industries can be obtained, thereby increasing the economic value added of our country.