Abstract:

Computer applications are essential digital tools for obtaining accurate data in agronomy. Their use helps students develop soft skills like teamwork, problem-solving, critical thinking, and data-driven decision-making. Moreover, implementing software like MATLAB reduces labor dependence, minimizes human error, and enhances efficiency in processes related to fruit quality, shelf life, and the determination of marketable attributes such as size, color, and weight. Altogether, these contributions highlight the relevance of integrating digital tools into practical agronomy training.

Quality parameters in fruits play a decisive role in their commercial value and consumer acceptance, as these attributes are perceived immediately and strongly influence the purchasing decision. In this context, traditional measurement techniques—such as using a ruler, digital caliper, or colorimeter—present limitations in terms of speed, objectivity, and scalability.

The aim of the activity is to introduce students to the use of MATLAB as a digital analysis tool, teaching them how to process images and automatically extract parameters such as size and color. The teaching approach focuses on helping them understand the practical value of these tools for their future professional roles. By linking digital skill development with real-world agronomic applications, the activity strengthens students’ capacity to interpret and analyze data efficiently.

MATLAB enables faster and more accurate work, reduces errors associated with manual measurements, and allows larger datasets to be handled efficiently. Through this activity, students develop practical skills and become familiar with a tool widely used in engineering and research, giving them an important advantage as they prepare for the workplace. Consequently, the use of MATLAB promotes a learning environment aligned with current technological demands in agriculture and biological sciences.

This proposal constitutes a teaching innovation activity, designed to integrate computational and digital tools into agronomy and biological sciences education, strengthening students’ technological competencies and promoting active learning in data-driven laboratory environments.

Keywords:

Digital learning, Image processing, Active methodologies, Agronomy education, Skill development, Quantitative assessment.