Abstract: Most of the countries are depends on agriculture, where Tamil Nadu is theland of agriculture. Here paddy cultivation is major source of earning. People in Tamil Nadu, consumes rice as main meal for three times in a day. Various factors such as diseases on paddy leaf, pest attack etc., the production of paddy will be affected approximately 40% to 50%, commonly rice related diseases should be detected in early stage to protect the paddy because it will destroy the entire farm land. If the diseases are identified in initial stage there is no need to spray a high dose fertilizer on the paddy crops. To overcome this, the proposed system uses pre-processing, transfer learning Inception_V3 method, neural network is trained by deep learning based Convolutional Neural Network(CNN) classification algorithm to identify the paddy leaf diseases like bacterial leaf blight, brown spot and rice blast. This method produces good accuracy. Scope of this project is to detect disease on paddy crops and to notify the types of diseases to farmer so that the farmers can take early action to protect the paddy crops.

Index Terms: Convolutional Neural Network (CNN), Digital Agriculture, Internet-of-Agro-Things (IoAT), Machine Learning (ML), Deep Learning.

PDF | DOI: 10.17148/IJARCCE.2023.12515

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