OPERATIONAL EFFICIENCY OF THE PNEUMATIC PROBE IN GRAINS SAMPLING AND DECISION-MAKING WITH PYTHON

Abstract

Soybean is an essential commodity to maintain a high gross domestic product of Brazilian agribusiness, being a versatile crop used both in human food and in the production of animal feed and biofuels. However, to ensure the quality and health of grains for storage, it is essential that producers and dealers of soybeans follow rigorous standards of harvesting and storage. The regulations for the exportation of the commodity are quite demanding, which makes the use of advanced technologies fundamental to improve operational efficiency in the selection of post-harvest grains and correct direction to other countries. In this context, the pneumatic probe and the Python computer language make possible the automation of processes and quick data analysis at a specific level, which can contribute to a more efficient and safe selection of grains and assertive decision making, avoiding contamination and ensuring the quality and purity of grains after harvesting. The present study was carried out in a grain cooperative where two grain sampling technologies were used, the pneumatic probe and the manual probe. Soybean qualitative data were evaluated in a grain laboratory and quantitative analyses were performed through the Python language. The pneumatic probe demonstrated to have greater operational efficiency compared to the quality parameters and classification of soybean grains, standing out in greater accuracy in collecting toxic seeds and contaminants. The use of Python language in real-time monitoring proved to be an efficient tool for decision making.