| Abstrakt: |
This research delves into the transformation of agriculture through precision techniques and advanced technology. The focus is on an autonomous AI weed cutter, utilising TinyML, the ESP32 camera module, and a proximity sensor to revolutionise weed management. The study introduces a holistic approach by integrating TinyML's advanced computer vision techniques for real-time weed detection and classification. The ESP32 camera captures field images, processed by TinyML algorithms to identify crops and weeds accurately. Augmenting visual perception, a proximity sensor ensures safe navigation, detecting obstacles and maintaining safe distances from crops. This fusion empowers the system to make informed decisions. Field trials confirm exceptional weed detection and removal accuracy. Integration of TinyML-based vision, ESP32 camera tech, and the proximity sensor optimises efficiency, adaptability, and safety. Economic and environmental analyses highlight benefits: sustainable practices, potential yield increase, and reduced herbicide usage. In conclusion, this research showcases an autonomous AI weed cutter, seamlessly integrating TinyML, ESP32, and a proximity sensor. This technology amalgamation offers precise weed management, boosting efficiency, reducing environmental impact, and promoting sustainability in agriculture—a pivotal stride toward precision farming's future. [ABSTRACT FROM AUTHOR] |
