editedbook
Publication: IIP Proceedings Year: 2022, Month: November Page No: 159-167, ISSN/ISBN: 978-81-959356-1-1 , DOI/Link: https://rsquarel.org/assets/docupload/rsl202366219404FE9E4D8.pdf
CROP QUALITY PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS (CNNS)
Area/Stream: Network & Communication Technologies, Authors: Dr. I. Hemalatha,Bh. Hema Sai Harshini Keywords: Deep Learning; Convolutional Neural Networks; Plant Disease Detection. Book Name /series: Futuristic Trends in Network & Communication Technologies ,Volume 2, Book 19, Part 2, Chapter 6Publication: IIP Proceedings Year: 2022, Month: November Page No: 159-167, ISSN/ISBN: 978-81-959356-1-1 , DOI/Link: https://rsquarel.org/assets/docupload/rsl202366219404FE9E4D8.pdf
Abstract:
Deep learning acts as an important element for predicting crop quality, including assisting with crop selection and crop management decisions. Determining the quality of the plant as well as the type of plant based on the leaf of a plant is the goal. Dataset used is Plant Village dataset, which include images of the plants leaves like potato, tomato etc. In order to identify quality and the category of plant we can use Deep Learning, more specifically it can be done by building a convolution neural network. Crop diseases have grown significantly in recent years due to drastic climate changes and crop immunity deficiencies. This leads to widespread crop damage, less cultivation, and, ultimately, economically loss for farmers. Crop disease recognition and dealing have become a challenge as a result of rapid disease growth and insufficient farmer knowledge. The texture and visual similarities of the leaves aid in disease identification. As a result, the combination of deep learning and computer vision provides a solution for this problem. The proposed deep learning model is trained on plants images of healthy and diseased from a publicly available dataset. The model predicts the whether the images of leaves a diseased or healthy.
Cite this: Dr. I. Hemalatha,Bh. Hema Sai Harshini,"CROP QUALITY PREDICTION USING CONVOLUTIONAL NEURAL NETWORKS (CNNS)", Futuristic Trends in Network & Communication Technologies ,Volume 2, Book 19, Part 2, Chapter 6 , November, 2022, 159-167, 978-81-959356-1-1 , https://rsquarel.org/assets/docupload/rsl202366219404FE9E4D8.pdf
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