Plant Aliment Detection Using Deep Learning And Conventional Neural Network

Registration ID: IJNRD_184893

Published ID: IJNRD2212132

DOI: Click Here to Get

Pentapati Venkata Surya Kamal , Narava Chitti Dedeepya , Gullapalli Jaswanth Chowdary , Palla Sesha Ratna Kumari , Pillaka Suvarna

CNN (CONVENTIONAL NEURAL NETWORK), SVM (SUPOORT VECTOR MACHINE), K-NN (K- NEAREST NEIGHBOURS), DEEP LEARNING, AUTO PLANT PEST DETECTION, PLANT LEAF DISEASE DETECTION

Plant disease detection is an emerging field in which imaging techniques are applied to the field, using images that can detect differences between healthy and diseased plants. The main goal of this research is to quickly capture images of a disease on a plant, with the hope that it can be used to find a cure for it. This ailment can be caused by several factors including insects, bacteria and viruses. The plant disease detection tool detects plant diseases at the genetic level. The tool identifies the causative agent, initial symptoms and pathogen of a crop. It also provides early warning about new crop varieties with unknown genetic resistance for common diseases. Detecting plant diseases using deep learning is an important step toward developing a smart network capable of proactively identifying and monitoring the health of crops, and to ultimately allow farmers to predict future deterioration. We have developed a model that has already been validated on three pipelines of different sizes. A second goal was to use this model's power with high accuracy to distinguish between different classes or types of pathogens such as fungal and oomycetes. We’ve identified three different classes of pathogens that can cause disease in crops. The first class is bacterial, which are single-celled organisms that can be harmful to plants. The second class is fungal, which are also single-celled organisms but with more complex features than bacteria. In order to classify illnesses in plants and illustrate the model's importance, CNN was employed in conjunction with histogram approaches. To diagnose tomato leaf illnesses, simple CNN architectures including AlexNet, GoogLeNet, and ResNet were built. Plots of training and validation accuracy were used to display the model's performance; ResNet was judged to be the best CNN architecture.

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Pentapati Venkata Surya Kamal, Narava Chitti Dedeepya, Gullapalli Jaswanth Chowdary, Palla Sesha Ratna Kumari, & Pillaka Suvarna (December-2022). Plant Aliment Detection Using Deep Learning And Conventional Neural Network. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(12), b326-b329. https://ijnrd.org/papers/IJNRD2212132.pdf

Volume 7 Issue 12, December-2022

Pages : b326-b329

Paper Reg. ID: IJNRD_184893

Published Paper Id: IJNRD2212132

Downloads: 000121976

Research Area: Computer Engineering 

Country: Visakhapatnam, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2212132.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2212132

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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