Akademik Veri Yönetim Sistemi
Smart Agricultural Technology, cilt.12, 2025 (Scopus)
The classification of chickpea seeds is essential for agricultural productivity, food processing, and consumer choice. Conventional classification techniques are typically subjective, labor-intensive, and prone to errors. Employing novel methodologies for categorizing nutritionally rich and economically significant items offers efficiency and practicality. This study classified 13 chickpea cultivars, cultivated under similar ecological conditions without chemical inputs, utilizing deep learning (DL) and image processing techniques. ResNet-18 and ConvNeXt_Tiny were employed to evaluate the classification efficacy of two pre-trained convolutional neural network (CNN) models. The chickpea seed images were labeled and resized to dimensions of 403 × 365 pixels, with each variation organized in folders and submitted as entries to the DL models. Experimental findings demonstrated that the ConvNeXt_Tiny and ResNet-18 classifier models successfully classified chickpea varieties into 13 distinct classes, achieving 88.27 % and 80.10 % accuracy, respectively. Furthermore, ConvNeXt_Tiny demonstrated greater sensitivity than ResNet-18 (88.43 %) while achieving superior specificity (99.02 %), accuracy (88.68 %), and F-measure (88.33 %). DL models, particularly ConvNeXt_Tiny, have significant potential for the automated classification of chickpea seeds. This technology may be included in embedded applications as a rapid and precise sorting system for the agriculture and food processing industries.