Development of a sensor to evaluate seeder performance: An IoT application
With population growth, the demand for food also increases. To maintain environmental sustainability, it is necessary that the search for arable land does not escalate. One factor in achieving this goal is the pursuit of better precision in seeders, which, in addition to the environmental aspect, also contributes to the economic sustainability of farmers. The precision of seeders critically depends on the combination of the metering unit and the seed tube, which is assessed through precision evaluation tests standardized by ISO 7256/1. According to the agronomic reference recommendation for seed distance (Xref) in a given crop, the real measured distance should fall within an acceptance interval between 0.5*Xref and 1.5*Xref. For this evaluation, the standard describes bench tests where the metering unit deposits the seed onto a moving belt. The movement is then stopped, and the distance between the centers of the seeds is measured and analyzed according to the acceptance interval. However, for large test volumes, this task is labor-intensive, and electronic sensors offer a better and faster response. This paper presents the development of a sensor for counting and measuring the interval between seeds, connected to the Internet of Things (IoT). The development uses embedded system design methodologies, and after the prototype was obtained, it underwent statistical tests for accuracy verification. The results demonstrate a good response from the system, with high precision and low error. Therefore, the sensor presented here offers a competitive alternative for seed counting following the standard.
Authors
- Marlon Soares Sigales (ORCID: https://orcid.org/0000-0001-8329-6430)
- Ádamo de Souza Araújo
- Fabrício Ardais Medeiros
- Ângelo Vieira dos Reis (ORCID: https://orcid.org/0000-0002-9765-1816)
- Taciano Ares Rodolfo
- Rafael Dos Santos Esteche
Institutions
- Universidade Federal de Pelotas (BR)
- Brazilian Agricultural Research Corporation (BR)
Publication Details
- Published
- 2025-04-23
- DOI
- https://doi.org/10.1109/lciot64881.2025.11118421
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00