An Intelligent helmet system using IoT and Raspberry Pi

Aravinda, N. L.; Jabirullah, Mohammad; DubasiKirtana,

doi:10.1088/1757-899x/981/4/042076

Cited by 5 publications

(1 citation statement)

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“…These bounding boxes are weighted by the predicted probabilities; YOLO is a faster and an accurate algorithm that is suitable for our operations. Therefore, we try to training the YOLO model and implement it on Raspberry Pi to guarantee a real time detection and identification, because this technological innovation opens up new possibilities for embedded internet of things (IoT) applications in fields such as the automatic weeding system that allows localized spraying with a chosen herbicide and with advanced analysis capability [18].…”

Section: Object Detection Methodsmentioning

confidence: 99%

Deep learning-based decision support system for weeds detection in wheat fields

Jabir

Falih

2022

IJECE

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<span>In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected.</span>

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Section: Object Detection Methodsmentioning

confidence: 99%