Rice is most basic food consumed by billions of people worldwide. it is one of the most widely grown cultivation, and leaf pathogens may possess a significant influence on yield and adequacy. Plant disease spread has increased dramatically in recent years. Plant diseases might be fungal, bacterial or viral and can cause harm to crop. The most well-known and devastating illness of paddy leaves are Black Spot Disease, Blast Disease, and Bacterial Leaf Blight, of which bacterial blight and black spot have been detected in this project. The spread of diseases to crops, as well as loss to farmers, can be significantly reduced if these diseases are identified quickly and accurately at earlier stages. The most critical element is the identification of rice leaf ailments, that possess a significant effect against financial system and food shortages. Human researchers estimate phytopathogens visually, and microscopic examination of morphological characteristics to recognize diseases, are the methods that are commonly used for the diagnosis and Crop diseases identification. This traditional procedures of manually diagnosing is time-consuming and not accurate.

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Paddy Leaf Disease Detection Using Convolutional Neural Network

  • B. Naga Vardhani,
  • K. Krishna Sri,
  • S. D. Gousi Majahar,
  • V. Siri Lakshmi,
  • A. Jyothika

摘要

Rice is most basic food consumed by billions of people worldwide. it is one of the most widely grown cultivation, and leaf pathogens may possess a significant influence on yield and adequacy. Plant disease spread has increased dramatically in recent years. Plant diseases might be fungal, bacterial or viral and can cause harm to crop. The most well-known and devastating illness of paddy leaves are Black Spot Disease, Blast Disease, and Bacterial Leaf Blight, of which bacterial blight and black spot have been detected in this project. The spread of diseases to crops, as well as loss to farmers, can be significantly reduced if these diseases are identified quickly and accurately at earlier stages. The most critical element is the identification of rice leaf ailments, that possess a significant effect against financial system and food shortages. Human researchers estimate phytopathogens visually, and microscopic examination of morphological characteristics to recognize diseases, are the methods that are commonly used for the diagnosis and Crop diseases identification. This traditional procedures of manually diagnosing is time-consuming and not accurate.