K. Dharanipriya, S. Sathyageetha, K. Sowmia, J. Srinidhi
{"title":"Smart Crop Protection System From Animals Using AI","authors":"K. Dharanipriya, S. Sathyageetha, K. Sowmia, J. Srinidhi","doi":"10.1109/ICBSII58188.2023.10181093","DOIUrl":null,"url":null,"abstract":"Animal assaults on crops are one of the main risks to crop production reduction. Crop raiding is one of the most acrimonious disputes as farmed land encroaches on formerly uninhabited areas. Pests, natural disasters, and animal damage pose severe risks to Indian farmers, lowering productivity. Farmers’ traditional tactics are ineffective, and it is not practical to hire guards to watch over crops and keep animals away. Since animal and human safety are equally important, it is crucial to safeguard the crops from harm brought on by creatures without causing any harm, as well as divert the animal. As a result, we employ deep learning to recognize animals that visit our farm utilizing the deep neural network idea, a branch of computer vision, in order to overcome the aforementioned issues and achieve our goal. In this project, we will periodically check in on the entire farm using a camera that will continuously record its surroundings. We are able to recognize when animals are entering with the use of a deep learning model, and then we use an SD card and speaker to play the right sounds to scare them away. The many convolutional neural network libraries and principles that were used to build the model are described in this research.","PeriodicalId":388866,"journal":{"name":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Bio Signals, Images, and Instrumentation (ICBSII)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICBSII58188.2023.10181093","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Animal assaults on crops are one of the main risks to crop production reduction. Crop raiding is one of the most acrimonious disputes as farmed land encroaches on formerly uninhabited areas. Pests, natural disasters, and animal damage pose severe risks to Indian farmers, lowering productivity. Farmers’ traditional tactics are ineffective, and it is not practical to hire guards to watch over crops and keep animals away. Since animal and human safety are equally important, it is crucial to safeguard the crops from harm brought on by creatures without causing any harm, as well as divert the animal. As a result, we employ deep learning to recognize animals that visit our farm utilizing the deep neural network idea, a branch of computer vision, in order to overcome the aforementioned issues and achieve our goal. In this project, we will periodically check in on the entire farm using a camera that will continuously record its surroundings. We are able to recognize when animals are entering with the use of a deep learning model, and then we use an SD card and speaker to play the right sounds to scare them away. The many convolutional neural network libraries and principles that were used to build the model are described in this research.