A Novel Framework for Detection of Harmful Snakes Using YOLO Algorithm

• DOI: 10.1007/s42979-023-02366-z
• Publication Date: 2023-12-02T14:02:04Z
• Authors: E. Naresh; J. Ananda Babu; S. L. Shiva Darshan; S. V. N. Murthy; N. N. Srinidhi
• Keywords: Ignorance

Abstract The spotlight of our discussion here is on developing a model for detecting snakes present in the agricultural fields using cameras and machine learning algorithms. idea to construct identify snakes, which will be trained vast set images snakes. These carefully selected include snake patterns, skin color different species (extraction unique features). This done train properly types that farmer encounters fields. Most famers are unaware have need way detect these so they can either avoid them or get rid them. Hence, this system implemented prevent thousands deaths help alleviate repercussions bites. Mistakes recognizing potentially harmful animal based only visual cues major contributors high death toll from venomous attacks. Since spend much time fields, where rice wheat cultivated, farmers at higher risk being bitten by than general population. Because their ignorance, illiterate more inclined believe superstitions, lead untimely snakebites despite medical intervention. Animals would normally pose no threat humans responsible people every year because environmental factors. However, it hard recognize dangers, new design paradigm has been developed make simpler. Researchers field biology use search endangered species. Predators may enter gardens green areas like tea coffee plantations. There not yet plan place implement automated sorting discovering distinctions. By applying suggested framework photographs dangerous animals, several factors useful studying organization easily identified. For classifier run real-time, YOLO utilized double processing within framework. Furthermore, technique enables quick recognition similar fashion. First, method used determine whether proposed work. Here, 87 percent precision also reached detection