{"title":"基于机器学习模型的植物叶片病害检测与分类","authors":"Aashish Jha, Madhavi Purohit, Vivek Maurya, Amiyakumar Tripathy","doi":"10.1109/IBSSC56953.2022.10037470","DOIUrl":null,"url":null,"abstract":"Many industries today have benefited from developing new technologies, particularly data science, machine learning, artificial intelligence, and deep learning. This includes agriculture. Previous research have shown that plant leaf diseases are losing productivity at an increasing pace, which accounts for 40-42% of agricultural production losses (Cost: 12.42 billion euros; Source: United Nations Food and Agriculture Organization (FAO)). This big issue may be resolved by employing this method for recognizing plant leaf disease from the input photographs. This technique involves steps including feature extraction, image segmentation, and image preprocessing. Next, a convolutional neural network-based classification approach is applied. The suggested implementation was 98.3% accurate in predicting plant leaf diseases.","PeriodicalId":426897,"journal":{"name":"2022 IEEE Bombay Section Signature Conference (IBSSC)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Plant Leaf Disease Detection And Classification Based On Machine Learning Model\",\"authors\":\"Aashish Jha, Madhavi Purohit, Vivek Maurya, Amiyakumar Tripathy\",\"doi\":\"10.1109/IBSSC56953.2022.10037470\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many industries today have benefited from developing new technologies, particularly data science, machine learning, artificial intelligence, and deep learning. This includes agriculture. Previous research have shown that plant leaf diseases are losing productivity at an increasing pace, which accounts for 40-42% of agricultural production losses (Cost: 12.42 billion euros; Source: United Nations Food and Agriculture Organization (FAO)). This big issue may be resolved by employing this method for recognizing plant leaf disease from the input photographs. This technique involves steps including feature extraction, image segmentation, and image preprocessing. Next, a convolutional neural network-based classification approach is applied. The suggested implementation was 98.3% accurate in predicting plant leaf diseases.\",\"PeriodicalId\":426897,\"journal\":{\"name\":\"2022 IEEE Bombay Section Signature Conference (IBSSC)\",\"volume\":\"48 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Bombay Section Signature Conference (IBSSC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IBSSC56953.2022.10037470\",\"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 IEEE Bombay Section Signature Conference (IBSSC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IBSSC56953.2022.10037470","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Plant Leaf Disease Detection And Classification Based On Machine Learning Model
Many industries today have benefited from developing new technologies, particularly data science, machine learning, artificial intelligence, and deep learning. This includes agriculture. Previous research have shown that plant leaf diseases are losing productivity at an increasing pace, which accounts for 40-42% of agricultural production losses (Cost: 12.42 billion euros; Source: United Nations Food and Agriculture Organization (FAO)). This big issue may be resolved by employing this method for recognizing plant leaf disease from the input photographs. This technique involves steps including feature extraction, image segmentation, and image preprocessing. Next, a convolutional neural network-based classification approach is applied. The suggested implementation was 98.3% accurate in predicting plant leaf diseases.