PREDICTION OF PADDY CROP DISEASES USING CONVOLUTIONAL NEURAL NETWORKS

Registration ID: IJNRD_222862

Published ID: IJNRD2406011

DOI: http://doi.one/10.1729/Journal.39798

G. RAVINDER , Dr CH.V. Phani Krishna

Index Terms : Paddy Crop Diseases, , Prediction , ResNet 101 , vgg16 , Plant Disease Detection , Convoloutional Neural Networks, , Crop Diseases , Support Vector Machine

Abstract : In most countries like in Asia , Africa , Latin America, PADDY Crop is mostly Consumable or Cultivated Crop, Paddy plants can reach heights of 85 to 150 cm [1], With the global population projected to reach 8.5 billions by 2040 , ensuring food security has become an urgent global challenge , The environment (frost, wind , temperature, humidity, and drought ) and pathogens (bacteria, nematodes, viruses, and fungi) are the main causes of the increase and spread of disease yet it faces significantly several diseases threats from various diseases throughout its growth cycle. Identifying these manually is challenging, especially for farmers with limited expertise. Leveraging advancements in deep learning research, automated picture identification systems based on Convolutional Neural Network (CNN) models offer a promising solution. This study presents a CNN model for disease identification in Paddy Crops plants, developed through Transfer Learning on a small dataset due to limited availability of rice leaf disease images. Specifically, the VGG-16 architecture is employed for training and evaluation purposes. The model utilizes a combination of rice field and internet datasets, demonstrating its adaptability to real-world scenarios. Despite the challenges posed by the scarcity of labeled data, the proposed CNN architecture achieves a commendable accuracy rate of 95 percent in disease classification. The utilization of Transfer Learning allows for efficient knowledge transfer from pre-trained models, enhancing the model's performance even with limited training samples. This research contributes to the field of agricultural technology by offering a reliable and accessible tool for disease identification in rice plants. By empowering farmers with automated systems capable of accurate disease diagnosis, this work aims to mitigate crop losses and improve agricultural productivity, ultimately benefiting food security and livelihoods in rice-growing regions

Published Paper   E-Certificate

G. RAVINDER & Dr CH.V. Phani Krishna (June-2024). PREDICTION OF PADDY CROP DISEASES USING CONVOLUTIONAL NEURAL NETWORKS. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(6), a113-a118. http://doi.one/10.1729/Journal.39798

Volume 9 Issue 6, June-2024

Pages : a113-a118

Paper Reg. ID: IJNRD_222862

Published Paper Id: IJNRD2406011

Downloads: 000121992

Research Area: Computer Engineering 

Country: Hyderabad, telangana, India

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Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2406011

Crossref DOI: http://doi.one/10.1729/Journal.39798

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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