Rice Variety Identification Based on Transfer Learning Architecture Using DENS-INCEP

Rice is a vital staple food for billions of people worldwide, especially in Asia, Africa, and Latin America, where it plays a key role in daily caloric intake and nutrition. With numerous varieties differing in size, shape, colour, texture, and nutritional content, accurate rice variety identification is critical for optimizing production and ensuring food quality. Environmental factors such as soil type and climate further influence these variations, making precise identification essential for improving productivity and reducing waste. However, traditional manual methods of identification, relying on visual characteristics, are prone to human error, resulting in variety mixing, reduced quality, and higher costs. This study addresses these challenges by employing the DENS-INCEP model, a transfer learning approach that integrates DenseNet-201 with the Inception module. DenseNet-201 serves as the backbone for feature extraction, while the Inception module enhances the model’s ability to capture multi-scale shape-related features, significantly improving classification accuracy. The model achieved remarkable performance, with an average accuracy of 99.94% across multiple rice varieties. By implementing the DENS-INCEP model, this study contributes to Sustainable Development Goal (SDG) 2 by improving food security through enhanced rice production and supply chain stability. Additionally, it supports SDG 9 by fostering innovation and advancing sustainable agricultural technologies. Furthermore, by reducing errors, waste, and inefficiencies in production and distribution, the model aligns with SDG 12, which emphasizes sustainable consumption and production. Overall, the DENS-INCEP model offers a robust and efficient solution to rice variety identification, addressing global food security challenges while promoting sustainability.