首页|Reports Outline Machine Learning Study Findings from Federal University Santa Ma ria (Nondestructive Technology for Real-time Monitoring and Prediction of Soybea n Quality Using Machine Learning for a Bulk Transport Simulation)

Reports Outline Machine Learning Study Findings from Federal University Santa Ma ria (Nondestructive Technology for Real-time Monitoring and Prediction of Soybea n Quality Using Machine Learning for a Bulk Transport Simulation)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting from Cachoeira do Sul, Brazil, by NewsRx journalists, research stated, “Grain moisture content and shipping time c an interfere with postharvest logistics on soybean quality. Thus, the study aime d to evaluate the use of a nondestructive technology, equipment including a mech anical-portable sampler with a hardware device and sensors for real-time monitor ing of temperature, relative humidity (RH), and intergranular carbon dioxide (CO 2) to predict the quality of soybean in the function of different moisture conte nts (11 %;, 14%;, and 18%; w.b.), sampling positions in the grain mass profile, and shipping time (0, 60, 480, and 1440 mi n).”

Cachoeira do SulBrazilSouth AmericaCyborgsEmerging TechnologiesMachine LearningTechnologyFederal Universit y Santa Maria

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Mar.28)