According to a study published in Computers and Electronics in Agriculture, Osaka Metropolitan University Assistant Professor Takuya Fujinaga has created an algorithm that allows robots to drive autonomously in two modes: to a predetermined location and beside raised cultivation beds.
Research indicates that RPAAS with low spray volumes and drift-reduction nozzles can match the effectiveness of conventional ground sprayers in weed management.
Ecorobotix, a Swiss company specializing in AI-driven agricultural technology, is expanding into Australia through a strategic dealer partnership with RDO Equipment Australia.
Sergius, an autonomous tractor developed by the University of Córdoba, integrates cutting-edge technology for improved agricultural productivity and efficiency.
Researchers developed a non-destructive VIS-NIR hyperspectral imaging method to assess sorghum's nutritional composition, enhancing food quality control.
XYZ Media, a media and marketing company at the intersection of education and technology, is proud to introduce the latest feature in its "Next Generation of Innovators" series. This series highlights young leaders solving real-world global challenges through innovative solutions.
Rivulis, the leader in innovative micro-irrigation solutions, takes pride in being the world's first irrigation company to introduce its market-leading AI technology, Rivulis AI.
A study in Discover Applied Sciences introduces a novel approach combining LoRa communication, digital twins, and IoRT to remotely control agricultural robots. This method enhances robot navigation in complex environments, improves efficiency, and reduces dependency on high-end networks, showcasing significant potential for precision farming and beyond.
Researchers have developed a deep learning and laser system to detect and repel wild birds, effectively protecting crops and poultry. The system, tested on a duck farm in Taiwan, significantly reduced bird presence with a repulsion rate of up to 97.1% per hour.
Researchers introduced HDMNet, a deep learning-based visual system for robotic pear picking, enhancing YOLOv8 with three key features for better accuracy and efficiency in complex environments. HDMNet demonstrated superior performance in real-time detection, achieving a high mean average precision and operating seamlessly in real-world applications, promising significant advancements in agricultural automation.
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