## 1 Introduction  
Pulses constitute one of the most significant crops in the leguminous family. About 90 million metric tonnes of pulses are produced worldwide [1]. Common beans, lentils, chickpeas, dry peas, cowpeas, mung beans, urad beans, and pigeon peas are the main pulses farmed worldwide. Pulses are significant sources of protein, phosphorus, iron, calcium, and other essential minerals, and help millions of individuals in impoverished nations meet their nutritional needs [2, 3]. Pulses have recently acquired popularity on a global scale as an alternative to animal-based sources of protein [4]. With a production of 15 million metric tonnes and a third-place ranking among pulses after beans and peas, chickpeas are one of the high protein pulses grown in more than 57 nations [1]. Depending on the cultivar, agronomic practices, and climatic factors, the protein content of chickpeas varies from 17% to 24% [5]. Additionally, chickpeas are a good source of energy and include vitamins, minerals, fibre, and phytochemicals. According to [PERSON] and [PERSON], 2007 [6], regular consumption of chickpeas lowers the risk factors for diabetes, cancer, and cardiovascular disease. For processors, customers, and other stakeholders, the quality of chickpeas is crucial in influencing their preference [7]. The various factors considered in evaluating the quality of chickpeas are 100-seed weight, ash content, colour, cooking time, cooked chickpea seed stiffness, moisture content, protein content, seed size distribution, starch content, total dietary fibre, water absorption, and others [8]. It is difficult to assess the quality of a sample that contains mixed chickpea varieties, so maintaining varied purity is the first crucial step in determining chickpea quality. Prior to a deeper examination of their visible or internal properties, the identification or classification of chickpea types assumes relevance. The most used imaging method for classifying agricultural products by varietal is RGB imaging [9]. Around the world, different chickpea cultivars are produced in various agroclimatic zones. The nutrient content, physical characteristics, and economic worth of each variety vary. Different chickpea varieties can be identified by their physical characteristics, such as colour and texture, which are visible and aid in classification. However, this classification process is time-consuming, expensive, and frequently performed by human professionals. Due to the intricacy of the task and the abundance of visual and environmental components, research into the creation of tools for the automation of these occupations is still underway. The need for accurate variety identification systems in precision agriculture is also growing as a result of the ramifications for the economy, ecology, and society [10]. Deep learning models are being used more frequently, which has led to significant improvements, notably in classification tasks [11; 12; 13; 14; 15]. Deep learning has been used in recent research for agricultural crop classification due to the increased accuracy and hardware accessibility. In addition, the rapid advancement of open-source hardware in recent years has promoted the creation and use of low-cost agricultural surveillance tools with image processing and AI capabilities. Single-board computers (SBCs) in many configurations, like the Raspberry Pi (RPi), have spread quickly across many different applications like food manufacturing [16] and surveillance monitoring [17]. [PERSON] et al., 2017 [16] detailed the design and construction of a monitoring system that employs thermal and RGB images, built on a Raspberry Pi 3, that is able to operate in difficult real-world scenarios. Raspberry Pi was utilised by [PERSON] et al., 2018 [18] to build a low-cost monitoring network for agricultural applications that is intended for wide adoption. A monitoring environment with many devices and interfaces was developed by [PERSON] et al., 2019 [19], enabling communication in low-cost agricultural equipment.