Plant Disease Detection Using Machine Learning Algorithm


Authors : Pradip Chougala; Dr. Rajashekharappa

Volume/Issue : Volume 7 - 2022, Issue 10 - October

Google Scholar : https://bit.ly/3IIfn9N

Scribd : https://bit.ly/3sRJCFN

DOI : https://doi.org/10.5281/zenodo.7275321

Pest infestations have an impact on the nation's agricultural output when they harm plants and crops. Farmers or experts typically keep a close check on the plants to spot any signs of disease. However, this procedure is frequently time-consuming, expensive, and unreliable. Results from automatic detection employing image processing methods are quick and precise. This study uses deep convolutional networks to establish a new method for developing illness recognition models that is supported by leaf image categorization. The field of precision agriculture has a chance to grow and improve the practise of precise plant protection as well as the market for computer vision applications. A quick and simple system implementation in practise is made possible by a wholly original training methodology. The entire process of putting this disease recognition model into practise, from gathering photos to create a database to having it reviewed by agricultural specialists and using a deep learning framework to carry out the deep CNN training, is comprehensively documented throughout the research. With the help of a deep convolutional neural network that has been trained and fine-tuned to accurately match the database of plant leaves that was compiled independently for various plant illnesses, the technique paper presented here may represent a novel way for identifying plant diseases. The innovation and advancement of the developed model reside in its simplicity; healthy leaves and backdrop images are consistent with other classes, allowing the model to use CNN to differentiate between ill and healthy leaves or from the environment. On earth, food is produced by plants. As a result, plant infections and diseases pose a serious threat, and the most common method of diagnosis is by looking for visible symptoms on the plant's body. Diverse research projects intend to identify workable methods for safeguarding plants as an alternative to the customarily time-consuming practise. The development of technology in recent years has led to the emergence of several alternatives to laborious old procedures. Deep learning methods are particularly effective at solving picture classification issues.

CALL FOR PAPERS


Paper Submission Last Date
31 - March - 2024

Paper Review Notification
In 1-2 Days

Paper Publishing
In 2-3 Days

Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe