Abstract
The continuous change in the environment is harmful to the crops and leads farmers toward debt and suicide. Most of the science students intend to provide solutions to the farmers who are involved in major crop production neglecting small-scale farmers. This project aims to develop a framework for the classification of diseases that can be seen in marigold flowers. The addition of global mobile phone utilization and recent enhancement in computer vision made possible by deep learning has floored the way for disease detection. 97% accuracy is achieved by the model using a convolutional neural network in conjunction with a fully connected layer. This project summarizes the need for an application to provide a background about a disease, its symptoms, the different disease aetiologia, and its treatment.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gurjar P, Meena L, Verma AK (2021) Plant disease detection using convolutional neural network. Int J Adv Sci Res Manag (IJASRM), ISSN 2455-6378
Hugh (2012). https://www.whatgrowsthere.com/grow/2012/04/10/botrytis-disease-in-marigolds-can-be-avoided/
Boulent J, Foucher S, Theau J, St-Charles P-L (2019) Convolution neural networks for the automatic identification of plant diseases. Front Plant Sci
Srivastava P, Mishra K, Awasthi V, Sahu VK, Pal PK (2021) Plant disease detection using convolutional neural network. Int J Adv Res (IJAR), ISSN 2320-5407
Kolli J, Vamsi DM, Manikandan VM (2021) Plant disease detection using convolutional neural network. In: IEEE Bombay section signature conference (IBSSC), Gwalior, India, pp 1–6
Almogdady H, Manaseer S, Hiary H (2018) A flower recognition system based on image processing and neural networks. Int J Sci Technol Res 7(11) 2277-8616
FatihahSahidan N, Juha AK, Mohammad N, Ibrahim Z (2019) Flower and leaf recognition for plant identification using convolutional neural network. Indonesian J Electr Eng Comput Sci 16(2):737–743
Kumar M, Kumar A, Palaparthy VS (2021). Soil sensors-based prediction system for plant diseases using exploratory data analysis and machine learning. IEEE Sens J 21(16):17455–17468
Priya LR, Ignisha Rajathi G, Vedhapriyavadhana R (2019) Crop disease detection and monitoring system. Int J Recent Technol Eng (IJRTE), 8(4):3050–3053, ISSN: 2277-3878
Jiang P, Chen Y, Liu B, He D, Liang C (2019) Real-time detection apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access, pp 59069–59080
Chopvitayakun S, Nuanmeesri S, Poomhiran L, Kadmateekarun P (2021) Marigold flower disease prediction through deep-neural network with multi model image. Int J Eng Trends Technol, pp 174–180
Kiran SM, Chandrappa DN (2019) Current trends in plant disease detection. Int J Sci Technol Res 8, 2277-8616
Sujawat GS, Chouhan JS (2021) Application of artificial intelligence in detection of diseases in plants. Turk J Comput Math, pp 3301–3305
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sri, V.V., Angel, G., Chowdary, Y.M. (2023). Flower Disease Detection Using CNN. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_49
Download citation
DOI: https://doi.org/10.1007/978-981-99-5166-6_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5165-9
Online ISBN: 978-981-99-5166-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)