Benchmarking of CNN Models and MobileNet-BiLSTM Approach to Classification of Tomato Seed Cultivars

Abstract

In the present study, a deep learning-based two-scenario method is proposed to distinguish tomato seed cultivars. First, images of seeds of four different tomato cultivars (Sacher F1, Green Zebra, Pineapple, and Ozarowski) were taken. Each seed was then cropped on the raw image and saved as a new image. The number of images in the dataset was increased using data augmentation techniques. In the first scenario, these seed images were classified with four different CNN (convolutional neural network) models (ResNet18, ResNet50, GoogleNet, and MobileNetv2). The highest classification accuracy of 93.44% was obtained with the MobileNetv2 model. In the second scenario, 1280 deep features obtained from MobileNetv2 fed the inputs of the Bidirectional Long Short-Term Memory (BiLSTM) network. In the classification made using the BiLSTM network, 96.09% accuracy was obtained. The results show that different tomato seed cultivars can be distinguished quickly and accurately by the proposed deep learning-based method. The performed study is a great novelty in distinguishing seed cultivars and the developed innovative approach involving deep learning in tomato seed image analysis, and can be used as a comprehensive procedure for practical tomato seed classification.