The quality of seeds is important for enhancing chickpea production and productivity. Currently, variety identification of chickpeas can be performed via expert opinion or laboratory analysis. Expert-based classifications are often subjective. It may not always be attainable to elicit expert opinions. This may also cause inaccuracies and challenges when chickpea varieties have highly similar morphological characteristics. Although laboratory-analysis-based classification (especially for certification) is reliable, it has two important disadvantages: (1) the integrity and usability of the analyzed chickpeas can be affected, and (2) the analyzed chickpeas are only a sample and it is debatable how much they reflect the whole. In addition, both methods are time-consuming and impractical when classifying large quantities of chickpeas and/or quick results are desired. To classify chickpea varieties accurately, quickly, and non-subjectively, innovative approaches that can replace the traditional methods or overcome their limitations are needed in the field.  
The integration of artificial intelligence into agriculture, particularly crop production, has become an increasing trend. Machine learning, a subset of artificial intelligence, has emerged as a key technology for enabling smart agriculture solutions and has been successfully applied in many areas, such as efficiency [4; 5], production quality [6; 7; 8], crop monitoring [9; 10; 11], animal welfare [12; 13; 14; 15], disease management [16; 17; 18], and sustainability [19; 11], leading to significant advances in the agricultural sector. Upon collecting data from the field, these data are analyzed with machine learning or deep learning methods to provide actionable insights. Thereby, irrigation requirements can be managed according to the needs of different plant species [4; 9; 11], the milk yield for each milking animal can be evaluated individually [5], and plant diseases and pests can be detected [17; 20]. The use of resources such as farmland can be optimized [10], productivity can be ensured [11], and renewable energy can be integrated [4] for sustainability goals. By accurately and quickly detecting mature crops or plants with small sizes, the specific requirements for smart harvesting can be met [21; 22; 23]. In addition, product quality and efficiency can be increased by classifying seed samples according to their quality levels and varieties [7; 8; 24]. This research aims to contribute to addressing the problem of classifying chickpea seed varieties and to support experts during the identification process by proposing a hybrid approach. The major contributions of this research are summarized as follows:  
* A novel image dataset \"TRCS_8_SET\" consisting of eight different certified chickpea seeds grown in Turkiye;
* A novel hybrid methodology that integrates pre-trained deep learning models for feature extraction, feature selection, and classical machine learning methods for classification to achieve superior performance in chickpea seed variety classification;
* Chickpea seed variety classification with a success rate of 94% and above to increase productivity in agricultural production and contribute to seed purity.  
This research is organized as follows. The next section summarizes the related works on seed classification. The third section provides a detailed description of the dataset prepared, the methodology proposed, the transfer learning and machine learning methods employed, the feature selection technique applied, and the performance metrics used. In the fourth section, the research findings are reported, exhaustively assessed, and discussed. The final section concludes this study and outlines potential future research on the topic.