Digital Identification of Different Varieties of Kazakhstan Apples Using Deep Learning Techniques

Application of pre-trained CNN and transfer learning approach for identification of different varieties of Kazakhstan apples is presented in the paper. The study used the most popular Kazakhstan apple varieties as: Aport Alexander, Ainur, Sinap Almaty, Nursat and Kazakhskij Yubilejnyj. In this study, two pre-trained CNN networks, SqueezeNet and GoogLeNet, were fine-tuned using different values of the Initial Learning Rate (ILR) parameter of 0.0001, 0.0002, 0.00025, 0.0003, 0.00035, 0.0004, 0.0005 and three network tuning algorithms, respectively The Stochastic Gradient Descent with moment solver (Sgdm), the Adam optimization algorithm (Adam), and Root Mean Square Propagation (RMSprop). The performance of the networks was evaluated by analyzing the values of Training Accuracy (TA), Training Loss (TL), Validation Accuracy (VA), Validation Loss (VL), and Confusion Matrix. The training of the networks was performed in 1900 iterations and 30 epochs. The developed approach for the identification of five varieties of Kazakhstan apples using deep learning techniques achieved 100% correct classification of fruits for the Ainur variety with a GoogLeNet network, solver RMSprop and ILR= 0.0005. For the varieties Aport, Kazakhski Yubileinyi and Nursat, one of the three network evaluation indicators achieves 100% accuracy, and for Sinap Almatynski – all three indicators are with and above the value of 95%. For varieties Ainur, Aport, Kazakhski Yubileinyi and Sinap Almatynski as the best (with the highest optimal accuracy values) is the GoogLeNet network, with the following settings: solver RMSprop and ILR=0.0005. Only for variety Nursat is the SqeezeNet network suitable, with the following settings: solver Sgdm and ILR= 0.0003. The proposed approach for recognizing the varietal affiliation of apples using deep learning neural networks is suitable for the analyzed apple varieties and could be easily implemented and used in industrial conditions for sorting fruits. The achieved recognition accuracy meets the requirements in the field.