The use of equipment and software dedicated to analysis has proven to be of paramount importance in the pursuit of precise and expedited results. Employing strategies that enable the acquisition and analysis of data in agricultural environments can play a crucial role in optimizing current agricultural practices. This translates into increased productivity, improved quality control processes, and greater flexibility in agricultural management (Vasconez et al., 2020).
Agriculture stands as one of the most prominent activities globally. With the rapid growth of the world population, the demand for food is continually rising. Consequently, new automated methods are being introduced to meet these needs, as traditional methods employed by farmers prove insufficient to address such demands (Zhang et al., 2021; Dharmaraj et al., 2018). In this scenario, the adoption of alternative high-efficiency phenotyping tools becomes indispensable. In this context, the use of images combined with computer vision deserves attention, as it allows for the assessment of qualitative and productivity attributes with reduced costs, lower labor demand, greater speed, efficiency, and precision (De Mesquita et al., 2022).
Agriculture is currently facing significant challenges, including climate change, resource depletion, loss of biodiversity, labor shortages, and the urgent need to increase the production of nutrient-rich foods (Tariq et al., 2023). In this regard, chickpea (Cicer arietinum L.) emerges as a legume of special interest, representing an economically accessible source of protein. Moreover, it is considered the second most consumed legume globally, playing a crucial role in regions facing a shortage of animal-origin protein (Zhang et al., 2020).
The cultivation of chickpeas exhibits attractive attributes in the context of sustainable production, given its remarkable adaptability to various climatic conditions, along with significant potential in terms of both productivity and economic profitability (Grasso et al., 2022). Another important aspect of chickpea cultivation lies in the ability of its roots to establish symbiosis with nitrogen-fixing bacteria, such as those of the Rhizobium ciceri genus, resulting in the formation of nodules capable of fixing atmospheric nitrogen in a form usable by plants. Several research efforts have been directed towards this crop, and the application of high-efficiency phenotyping techniques through image analysis emerges as a tool of significant potential.
The use of images holds considerable potential for enhancing efficiency and precision in phenotyping activities, as indicated by Haque et al. (2021). This approach allows for the assessment of qualitative attributes in a more economical, rapid, efficient, and precise manner. As new technologies aim to increase the accuracy and speed of phenotypic measurements, there is a growing interest in research in this field (Massruhá et al., 2014).
An advantage of these methodologies is the ability to reproduce results even after the elimination of plant material, as the image database can be preserved. This opens the possibility of obtaining detailed information about root development through image analysis (Haque et al., 2021).
Another significant benefit is methodological standardization, which assumes critical relevance by providing consistency, reliability, and a foundation for study comparison. This ensures that processes and procedures are uniformly followed.