Robotic Weed Control and Biodiversity Preservation: IoT Solutions for Sustainable Farming

Abstract

Emerging technology is a crucial approach in the quest for environmentally responsible farming methods. This research represents an integrated method for transforming farming by combining robots, the Internet of Things (IoT), and environmental protection. This method offers a novel strategy for weed control and biodiversity conservation at a time that concerns about environmental damage and the desire to maximize resource utilization are at the forefront of people's minds. The Machine learning Convolutional Neural Networks (CNNs) algorithm has been used with cameras on autonomous robotic platforms to identify crop and weed types for focused and efficient treatment. loT device's ability to capture real-time data allows remote monitoring and evidence-based decision-making. This system helps proactively manage weed infestations, reducing the need for chemical pesticides and reducing environmental damage. This innovative technology increases crop production and enhances biodiversity preservation with the coordinated use of hardware components and machine learning techniques. The findings have significant consequences and highlight the possibility of technology-driven solutions to combine farming yield with environmental parameters for sustainable agriculture.