Narayana Darapaneni, S. Sreekanth, A. Paduri, Anthony Shohan Roche, V. Murugappan, Keisham Kiron Singha, Amey V Shenwai
{"title":"基于人工智能的养殖场鱼病检测系统,帮助微型和小型养殖户","authors":"Narayana Darapaneni, S. Sreekanth, A. Paduri, Anthony Shohan Roche, V. Murugappan, Keisham Kiron Singha, Amey V Shenwai","doi":"10.1109/irtm54583.2022.9791553","DOIUrl":null,"url":null,"abstract":"Micro and small scale Fish Farmers play a crucial role in the inland Fish farming Industry. Fish farmers in this segment face certain unique problems. One of which is the diseases affecting the fishes being cultured on their farm. Maintaining sustained Health of the fishes is essential, failing which, these farmers are liable to suffer heavy losses. Manual observation by the trained farmers, plays a key role at present, to maintain sustained observation to detect the onset of a disease in the fish farming pen or pond. This method has a severe drawback, in that it has an inherently high level of error and also a higher time lag between observations that is practically possible. In order to remove these drawbacks and increase overall efficiency in timely detection of the onset of diseases in the fishes, in any given pen or pond, an AI-based disease detection system is envisaged. This system covers periodical optical monitoring of the fishes in the farm, detecting the onset of any disease, with a minimum time lag and sending instant messages to all the stakeholders to enable them to initiate remedial action. This approach is bound to pre-empt suffering of financial loss by the farmers, due to the death of the fish.","PeriodicalId":426354,"journal":{"name":"2022 Interdisciplinary Research in Technology and Management (IRTM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"AI Based Farm Fish Disease Detection System to Help Micro and Small Fish Farmers\",\"authors\":\"Narayana Darapaneni, S. Sreekanth, A. Paduri, Anthony Shohan Roche, V. Murugappan, Keisham Kiron Singha, Amey V Shenwai\",\"doi\":\"10.1109/irtm54583.2022.9791553\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Micro and small scale Fish Farmers play a crucial role in the inland Fish farming Industry. Fish farmers in this segment face certain unique problems. One of which is the diseases affecting the fishes being cultured on their farm. Maintaining sustained Health of the fishes is essential, failing which, these farmers are liable to suffer heavy losses. Manual observation by the trained farmers, plays a key role at present, to maintain sustained observation to detect the onset of a disease in the fish farming pen or pond. This method has a severe drawback, in that it has an inherently high level of error and also a higher time lag between observations that is practically possible. In order to remove these drawbacks and increase overall efficiency in timely detection of the onset of diseases in the fishes, in any given pen or pond, an AI-based disease detection system is envisaged. This system covers periodical optical monitoring of the fishes in the farm, detecting the onset of any disease, with a minimum time lag and sending instant messages to all the stakeholders to enable them to initiate remedial action. This approach is bound to pre-empt suffering of financial loss by the farmers, due to the death of the fish.\",\"PeriodicalId\":426354,\"journal\":{\"name\":\"2022 Interdisciplinary Research in Technology and Management (IRTM)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Interdisciplinary Research in Technology and Management (IRTM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/irtm54583.2022.9791553\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Interdisciplinary Research in Technology and Management (IRTM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/irtm54583.2022.9791553","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AI Based Farm Fish Disease Detection System to Help Micro and Small Fish Farmers
Micro and small scale Fish Farmers play a crucial role in the inland Fish farming Industry. Fish farmers in this segment face certain unique problems. One of which is the diseases affecting the fishes being cultured on their farm. Maintaining sustained Health of the fishes is essential, failing which, these farmers are liable to suffer heavy losses. Manual observation by the trained farmers, plays a key role at present, to maintain sustained observation to detect the onset of a disease in the fish farming pen or pond. This method has a severe drawback, in that it has an inherently high level of error and also a higher time lag between observations that is practically possible. In order to remove these drawbacks and increase overall efficiency in timely detection of the onset of diseases in the fishes, in any given pen or pond, an AI-based disease detection system is envisaged. This system covers periodical optical monitoring of the fishes in the farm, detecting the onset of any disease, with a minimum time lag and sending instant messages to all the stakeholders to enable them to initiate remedial action. This approach is bound to pre-empt suffering of financial loss by the farmers, due to the death of the fish.