An efficient approach for age-wise rice seeds classification using SURF-BOF with modified cascaded-ANFIS algorithm

Abstract

It is a well-known fact that the quality of a seed highly impacts the germination of a rice seed. The age of the seed is one of the primary key points in assessing the seed quality. Therefore, this study aims to develop an AI-based machine-learning model to classify age-wise rice seeds. This study employs the SURF-BOF-based Cascaded-ANFIS algorithm for the implementation of the classifier. The proposed model performances were compared to the VGG16. Moreover, this research contributes a novel Japanese rice seed dataset to the scientific community. Furthermore, a 10-Fold cross-validation is performed to evaluate the robustness of the novel approach. The K-fold cross-validation's mean accuracy confirmed the proposed algorithm's higher robustness in the age-wise classification of rice seeds. Nevertheless, the results were evaluated using the confusion matrix and metrics such as precision, recall, and F1-Score. The Accuracy of Akitakomachi, Koshihikari, Yandao-8, and rice variety classification is 99%, 99%, 92%, and 97%. Analysis of the results determines the ability to classify rice by age and the general robustness of the algorithm.

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“…It is worth noting that the default setting in Matlab is fivefold cross-validation. However, the potential for bias and unreliability in prediction results when employing a random split of data in an 80:20 ratio during training and testing is recognized 30 , 31 . To mitigate this concern and enhance the robustness of the models, 10-fold cross-validation was applied for training the ML algorithms in this study.…”

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“…Food production is a critical component of global agricultural systems, and understanding the factors influencing it has been the focus of extensive research. Over the years, numerous studies have investigated the relationship between food production and various methods, including statistical and machine-learning approaches [10,11]. The following paragraphs aim to provide an overview of key research efforts in this area.…”