Conventional weed management strategies encounter various obstacles that hinder effective crop protection and jeopardize agricultural sustainability. Farmers frequently depend on a narrow spectrum of herbicides, resulting in heightened resistance within weed populations, which in turn requires elevated treatment rates and exacerbates environmental hazards. Furthermore, these traditional methods generally lack the accuracy necessary for efficient site-specific management, leading to excessive chemical application and adverse ecological consequences. The historical dependence on broad-spectrum herbicides fails to consider the regional variability of weed proliferation. As the agricultural landscape transforms, the integration of sophisticated technologies is essential. The digitalization of agriculture, encompassing the integration of IoT and AI, has the capacity to mitigate traditional difficulties by facilitating real-time monitoring and customized treatments, thereby fostering more sustainable practices. The adoption of these technologies is crucial for improving efficiency and scalability in weed management. The incorporation of the Internet of Things (IoT) and Artificial Intelligence (AI) into robotic systems presents interesting methods for tackling the issues of weed management. The amalgamation of IoT and AI technology into robots offers a revolutionary method for site-specific weed management, markedly improving agricultural efficiency and sustainability. For example, a remote-controlled robot has been developed for weeding operation, capable of functioning in small rows of vegetable crops. A novel IoT and sensor-based data collecting device has also been developed to collect location-specific information on weeds in the field, which is subsequently utilized for the creation of AI models for weed detection. Furthermore, a variable swath herbicide applicator (VarSHA) robot has been developed to apply the herbicide in a regulated manner. Cameras are utilized for the detection of weeds in the field. These technologies have considerably decreased the use of chemicals and drudgery.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

IoT and AI-Integrated Robots for Site-Specific Weed Management

  • Dilip Kumar Kushwaha,
  • Pramod Kumar Sahoo,
  • Aruna T. N.,
  • Gourab Choudhury,
  • A. K. Mishra,
  • Amit Gupta

摘要

Conventional weed management strategies encounter various obstacles that hinder effective crop protection and jeopardize agricultural sustainability. Farmers frequently depend on a narrow spectrum of herbicides, resulting in heightened resistance within weed populations, which in turn requires elevated treatment rates and exacerbates environmental hazards. Furthermore, these traditional methods generally lack the accuracy necessary for efficient site-specific management, leading to excessive chemical application and adverse ecological consequences. The historical dependence on broad-spectrum herbicides fails to consider the regional variability of weed proliferation. As the agricultural landscape transforms, the integration of sophisticated technologies is essential. The digitalization of agriculture, encompassing the integration of IoT and AI, has the capacity to mitigate traditional difficulties by facilitating real-time monitoring and customized treatments, thereby fostering more sustainable practices. The adoption of these technologies is crucial for improving efficiency and scalability in weed management. The incorporation of the Internet of Things (IoT) and Artificial Intelligence (AI) into robotic systems presents interesting methods for tackling the issues of weed management. The amalgamation of IoT and AI technology into robots offers a revolutionary method for site-specific weed management, markedly improving agricultural efficiency and sustainability. For example, a remote-controlled robot has been developed for weeding operation, capable of functioning in small rows of vegetable crops. A novel IoT and sensor-based data collecting device has also been developed to collect location-specific information on weeds in the field, which is subsequently utilized for the creation of AI models for weed detection. Furthermore, a variable swath herbicide applicator (VarSHA) robot has been developed to apply the herbicide in a regulated manner. Cameras are utilized for the detection of weeds in the field. These technologies have considerably decreased the use of chemicals and drudgery.