REVIEW ON IDENTIFICATION OF LEAF DISEASE AND DIAGNOSIS

snehal Ghatole; samiksha Ingole; Prof. Barkha Dange; Anshika Shende; kalash Bhujade; Vaibhav Sawarkar

REVIEW ON IDENTIFICATION OF LEAF DISEASE AND DIAGNOSIS

April 2023

Vol-9, Issue-2

Paper ID: 19726

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Science and Engineering

Keywords

identification of the plant trouble some of the disease wheater condition survial of the crop various disease.

Abstract

In India, agriculture provides a living for half of the population. Food security is significantly threatened by microbial infections; however, due to inadequate infrastructure, it is still difficult to identify them quickly. With AI, deep learning and transfer learning may be used to automatically diagnose plant illnesses from raw photos. Utilising a collection of 8,438 photos of healthy and sick leaves from the Plant Village dataset that was locally acquired, this study intends to identify and categorise grape and mango leaf illnesses. To recognise diseases or their absence, the deep convolutional neural network (CNN) is trained. AlexNet is a CNN architecture that has already been trained to automatically extract and classify features. The system, which was created using MATLAB, detects leaves accurately in 99% of cases for grape leaves and 89% of cases for mango leaves, respectively. For Android smartphones, a programme called "JIT CropFix" has been created to implement the same concept.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	snehal Ghatole	Priyadarshini J L College of Engineering
2	samiksha Ingole	Priyadarshini J L College of Engineering
3	Prof. Barkha Dange	Priyadarshini J L College of Engineering
4	Anshika Shende	Priyadarshini J L College of Engineering
5	kalash Bhujade	Priyadarshini J L College of Engineering
6	Vaibhav Sawarkar	Priyadarshini J L College of Engineering

How to Cite

Use the following formats to cite this article in your research.

APA Style

Ghatole, snehal, Ingole, samiksha, Dange, Prof. Barkha, Shende, Anshika, Bhujade, kalash, & Sawarkar, Vaibhav (2023). REVIEW ON IDENTIFICATION OF LEAF DISEASE AND DIAGNOSIS. International Journal of Advance Research and Innovative Ideas In Education, 9(2), 2555-2558.

MLA Style

Ghatole, snehal, et al. "REVIEW ON IDENTIFICATION OF LEAF DISEASE AND DIAGNOSIS." International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 2, 2023, pp. 2555-2558.

IEEE Style

snehal Ghatole, samiksha Ingole, Prof. Barkha Dange, Anshika Shende, kalash Bhujade, and Vaibhav Sawarkar, "REVIEW ON IDENTIFICATION OF LEAF DISEASE AND DIAGNOSIS," International Journal of Advance Research and Innovative Ideas In Education, vol. 9, no. 2, pp. 2555-2558, 2023.

Vancouver Style

Ghatole snehal, Ingole samiksha, Dange Prof. Barkha, Shende Anshika, Bhujade kalash, Sawarkar Vaibhav. REVIEW ON IDENTIFICATION OF LEAF DISEASE AND DIAGNOSIS. International Journal of Advance Research and Innovative Ideas In Education. 2023;9(2):2555-2558.

Harvard Style

Ghatole, snehal, Ingole, samiksha, Dange, Prof. Barkha, Shende, Anshika, Bhujade, kalash, & Sawarkar, Vaibhav (2023) 'REVIEW ON IDENTIFICATION OF LEAF DISEASE AND DIAGNOSIS', International Journal of Advance Research and Innovative Ideas In Education, 9(2), pp. 2555-2558.

Chicago Style

Ghatole, snehal, et al. "REVIEW ON IDENTIFICATION OF LEAF DISEASE AND DIAGNOSIS." International Journal of Advance Research and Innovative Ideas In Education 9, no. 2 (2023): 2555-2558.

Turabian Style

Ghatole, snehal, et al. "REVIEW ON IDENTIFICATION OF LEAF DISEASE AND DIAGNOSIS." International Journal of Advance Research and Innovative Ideas In Education 9, no. 2 (2023): 2555-2558.