Analysis of robust weed detection techniques based on the Internet of Things (IoT)

Modern-day agriculture techniques use herbicide to target unwanted weeds in farms. But, using these herbicides, which have acute toxicity, can have harmful effects even after a single episode of ingestion. This herbicide affects respiratory tract irritation, eye, and skin irritation and causes asthma-related problems. Novel and effective approaches using the Internet of Things (IoT) has been proposed for developing weed detection model. To reduce the human intervention on-field and enhance the training model by considering subject-oriented data stored at the server. The selective herbicide will avail in a substantial decrease in the utilization of quantity herbicide, subsequently promoting health care. To achieve this task, authentic-time robots which can precisely detect plant and thereby classify them into crops and weeds by utilizing the categorical trained model. By retrieving and storing the information predicated on the Internet of Things (IoT) which can be accessed by weed detection robot in authentic-time. In this paper, reviews of weed detection for selective herbicide using various approaches for accurate and precise weed detection in farming. In the end, this paper provides a proposed architecture for the Internet of Things (IoT) based smartweed detection.