Abstract:

Recent advances in artificial intelligence have opened new opportunities to incorporate Deep Learning tools into experimentation-oriented educational settings. In Agronomy, seed characterization - particularly counting and basic morphological evaluation - remains a task that contributes little to the development of analytical skills, yet its poor execution leads to future problems in crop management and farm profitability.

The objective of this study is to introduce the use of image recognition software - using the Python programming language on coding platforms - to automate agronomic seed characterization within a practical "learning-by-doing" framework. This facilitates a more efficient, technological, and solution-oriented approach to this type of analysis.

Students work with an image recognition model integrated into interactive software, allowing them to adjust model parameters, explore different configurations, and visualize the effect of these modifications on the results on-screen. This direct approach to the computational process enables a better understanding of how algorithms function. By comparing the traditional procedure with the automated one, students acquire a deeper understanding of how to integrate programming, digital tools, and agronomic crop parameters.

Incorporating these technologies allows for the reallocation of time from repetitive, low-value tasks toward activities of greater cognitive complexity, enhancing soft skills such as innovation, creativity, critical thinking, time management, and problem-solving. Consequently, learning methodologies are reinforced by these competencies, making the certification of bachelor’s and master's degrees by agencies clearer and more evident.

The proposed methodology improves accessibility to digital technologies in the classroom and promotes active learning supported by artificial intelligence, which is fully applicable to both real research problems and emerging needs in the agro-food industry. Therefore, this teaching methodology demonstrates significant benefits for educational institutions (universities), their students, and the industry.

Keywords:

Soft skills, Computer vision, Productivity, Agronomy.