Comparative Analysis of Deep Learning Architectures for Classifying Soy Seeds

• Vinicius Godoy Marques UNESP
• Lucas Correia Ribas UNESP
• Caetano Mazzoni Ranieri UNESP

DOI: https://doi.org/10.5753/eniac.2025.14087

Abstract

Soybean seed quality plays a critical role in agricultural productivity and economic returns. Recent studies have applied deep learning techniques to automate seed classification. However, these approaches either focus on binary classification or do not thoroughly compare multiple architectures for multiclass soybean seed classification. This work proposes a comparative analysis of deep learning models to classify soybean seeds into five categories. The methodology involves training and evaluating different models using the Soybean Seeds dataset, followed by fine-tuning the best-performing model. Experimental results show that InceptionV3 achieved the highest accuracy, improving from 76.68% with transfer learning to 86% after fine-tuning.

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