{"title":"A Portable Real Time Health Inspection system for Cotton and Potato Crop using Drone","authors":"Saman Khan, Nimra Latif, Huzafa Adnan, I. Khosa","doi":"10.1109/ETECTE55893.2022.10007247","DOIUrl":null,"url":null,"abstract":"Agriculture is the backbone of Pakistan economy. According to an estimate, 38% of total labor is connected with agriculture. The quality of agricultural yield is ensured employing multiple procedures including soil preparation, proper cultivation, fertilization, and applying pesticide spray to avoid contamination. The last step involves manual visual inspection of the crop to detect any infection which is a lengthy procedure as well as tiring. To facilitate the farmer, we propose a computer vision-based automatic health assessment system for crops mounted on a drone. For evaluation of the system, we opted for two of the major crops of Pakistan: potato and cotton where the experiment is performed in the field for real time testing. The purpose build single board computer Jetson Nano developed by Nvidia Inc. is used for real time processing. The developed system showed 99% accuracy overall for both the crops.","PeriodicalId":131572,"journal":{"name":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ETECTE55893.2022.10007247","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
Agriculture is the backbone of Pakistan economy. According to an estimate, 38% of total labor is connected with agriculture. The quality of agricultural yield is ensured employing multiple procedures including soil preparation, proper cultivation, fertilization, and applying pesticide spray to avoid contamination. The last step involves manual visual inspection of the crop to detect any infection which is a lengthy procedure as well as tiring. To facilitate the farmer, we propose a computer vision-based automatic health assessment system for crops mounted on a drone. For evaluation of the system, we opted for two of the major crops of Pakistan: potato and cotton where the experiment is performed in the field for real time testing. The purpose build single board computer Jetson Nano developed by Nvidia Inc. is used for real time processing. The developed system showed 99% accuracy overall for both the crops.