Freshness Classification of Hog Plum Fruit Using Deep Learning
V. R. Arunachalaeshwaran, Hussain Falih Mahdi, Tanupriya Choudhury, Tanmay Sarkar, Bikram Pratim Bhuyan
Abstract
Automatic freshness classification of fruits and veg-etables is an active area of research in the modern era. In order to maintain and preserve the grade quality of the fruits, the need to speed up processing is in a great urge. The fruit Hog Plum is noted to have high demand in Indian subcontinents. In this work we have utilized deep learning based techniques for performing freshness classification on hog Plum fruit. In this work, four convolutional neural network based models viz. ResNet18, MobileNetV2, MobileNetV3-Small, MobileNetV3-Large is pre-trained on the ImageNet dataset which were fine-tuned to perform binary classification on Hog Plum dataset cre-ated by us. The dataset was augmented for training purpose and hyperparameter tuning was performed using grid search and k-fold cross validation. The results of the model are then evaluated and compared in terms of several metrics like accuracy, precision, recall, Fl-Score, ROC-AUC Score and balanced accuracy score. Our results demonstrate that deep learning based techniques are effective for performing freshness classification of Hog Plum fruit. The models trained by us are lightweight in nature, hence are suitable to run on low-end and mobile devices increasing the applicability and adaptability of our proposed method.