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An Efficient Adaptive Weighted Neural Network-based Leaves Disease Detection Model Using Improved Cheetah Optimizer

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An Efficient Adaptive Weighted Neural Network-based Leaves Disease Detection Model Using Improved Cheetah Optimizer


Amrita Arjun Kindalkar | Dr. Janapati Venkata Krishna



Amrita Arjun Kindalkar | Dr. Janapati Venkata Krishna "An Efficient Adaptive Weighted Neural Network-based Leaves Disease Detection Model Using Improved Cheetah Optimizer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-3, June 2024, pp.385-391, URL: https://www.ijtsrd.com/papers/ijtsrd64882.pdf

Agriculture is the primary source of the economy and food in various regions. The influence of the plant productivity has a high impact on the nation’s economy. The weather conditions and the climate changes result in the food crops agonizing from the latest disorders and creating them highly in danger to the pests. The identification of the plant disorder is significant as it has a high influence on the profits and productivity of the farmers. Early identification of the plant disorder can support the farmers in creating the essential activities to secure the productivity. Conventional mechanisms of plant disorder identification utilize the knowledge of the human for evaluating the plant disorder. But, the manual detection of the plant disorder is time-consuming and tiresome operation and needs the knowledge of the experts for better detection; hence the necessary remedy can be performed. Therefore, the machine learning-aided plant disorder approach requires to be enhanced for the correct estimation of the plant disorder. In this task, an efficient plant disease identification system is improved by applying a machine learning model. The images of the plants are collected from the benchmark data assets. The collected image is given to the watershed algorithm. Watershed is an advanced image segmentation technique utilized to segment the abnormality image. From the segmented image, the color and texture features are extracted. The segmented image from the watershed algorithm is now provided as input to the Adaptive Weighted Neural Network (AWNN). The parameters and the weights in the NN are optimized with the aid of a Modified Random Number of Cheetah Optimizer (MRNCO). The final detected plant disease is obtained from the AWNN architecture.

Plant Disease Detection; Watershed Algorithm; Texture And Color Features; Modified Random Number Of Cheetah Optimizer; Adaptive Weighted Neural Network


IJTSRD64882
Volume-8 | Issue-3, June 2024
385-391
IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470
Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

International Journal of Trend in Scientific Research and Development - IJTSRD having online ISSN 2456-6470. IJTSRD is a leading Open Access, Peer-Reviewed International Journal which provides rapid publication of your research articles and aims to promote the theory and practice along with knowledge sharing between researchers, developers, engineers, students, and practitioners working in and around the world in many areas like Sciences, Technology, Innovation, Engineering, Agriculture, Management and many more and it is recommended by all Universities, review articles and short communications in all subjects. IJTSRD running an International Journal who are proving quality publication of peer reviewed and refereed international journals from diverse fields that emphasizes new research, development and their applications. IJTSRD provides an online access to exchange your research work, technical notes & surveying results among professionals throughout the world in e-journals. IJTSRD is a fastest growing and dynamic professional organization. The aim of this organization is to provide access not only to world class research resources, but through its professionals aim to bring in a significant transformation in the real of open access journals and online publishing.

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