Comparative Study of the Performance of SqueezeNet and GoogLeNet CNN Models in the Identification of Kazakhstani Potato Varieties
Shynybay Z. Georgieva T. Nedelcheva E. Alikhanov J. Moldazhanov A. Zinchenko D. Bakytova M. Sapargali A. Daskalov P.
January 2026Multidisciplinary Digital Publishing Institute (MDPI)
AgriEngineering
2026#8Issue 1
Kazakhstan’s growing potato industry underscores the need to develop and apply digital solutions that boost grading efficiency. A comparison between two traditional deep neural network architectures used to classify color images of potatoes from Kazakhstan is discussed in the paper. Ten representative varieties of Kazakhstani potatoes were selected as objects of study: Alians, Alians mini, Astana, Astana mini, Edem, Edem mini, Nerli, Nerli mini, Zhanaisan, and Zhanaisan mini. Two convolutional neural network (CNN) models, SqueezeNet and GoogLeNet, were refined via transfer learning employing three optimization approaches. Then, they were used to classify the potato images. A comparison of the two neural networks’ classification performance was conducted using common evaluation criteria—accuracy, precision, F1 score, and recall—alongside a confusion matrix to highlight misclassified samples. The comparative analysis demonstrated that both CNN architectures—SqueezeNet and GoogLeNet—achieve high classification accuracy for Kazakhstani potato varieties, with the best performance on Astana and Zhanaisan (>97%). The study confirms the applicability of lightweight CNNs for digital varietal identification and automated quality assessment of seed potatoes under controlled imaging conditions. The developed approach is the first comparative CNN-based varietal identification of Kazakhstani potato tubers using transfer learning and contributes to the digitalization of potato breeding, and provides a baseline for future real-time sorting systems using deep learning.
classification , deep learning methods , digital image analysis , potatoes , variety
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Department of Power Engineering, K.I. Satbayev Kazakh National Research Technical University, 22 Satbaev St., Almaty, 050013, Kazakhstan
Department of Automatics and Electronics, University of Ruse, 8 Studentska Str., Ruse, 7017, Bulgaria
Department of Energy and Electrotechnics, Kazakh National Agrarian Research University, 8 Abay Str., Almaty, 050010, Kazakhstan
Department of Machines and Devices of Production Processes, Almaty Technological University, 100 Tole Bi St., Almaty, 050012, Kazakhstan
Department of Power Engineering
Department of Automatics and Electronics
Department of Energy and Electrotechnics
Department of Machines and Devices of Production Processes
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