Deep transfer learning driven model for mango leaf disease detection

Yogendra Pratap Singh, Brijesh Kumar Chaurasia () and Man Mohan Shukla
Additional contact information
Yogendra Pratap Singh: Pranveer Singh Institute of Technology
Brijesh Kumar Chaurasia: Pranveer Singh Institute of Technology
Man Mohan Shukla: Pranveer Singh Institute of Technology

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 10, No 7, 4779-4805

Abstract: Abstract India exports a big volume of mangoes, the mango fruit holds significant economic and ecological worth in India. Plant diseases are a very typical occurrence that reduces production of mangoes and results in significant losses for farmers. In this regard, healthy output depends on the early detection of plant diseases. It is quite challenging to identify the disease with the naked eye. Artificial intelligence and machine learning have, therefore, been widely utilized in the agriculture sector for automatic monitoring of food and agricultural goods and have proven to be a scientific and powerful instrument for intensive study over decades. In this paper, we have developed the deep transfer learning driven (DTLD) model to identify mango leaf disease. The suggested model is trained and tested using a variety of complex algorithms, datasets, and validation methods. After performing some preprocessing on the data, we divide it into training and testing datasets. We use the softmax activation function to classify diseases of mango in model’s training and testing. The outcomes demonstrate that the proposed model has obtained 99.76% accuracy to prove the efficacy. Moreover, a dataset containing 4000 images has been used in this endeavor. The proposed DTLD model can successfully classify the image of the mango leaf into different disease.

Keywords: Convolutional neural network (CNN); Artificial intelligence (AI); Machine learning (ML); Deep learning (DL) (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-024-02480-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:10:d:10.1007_s13198-024-02480-y

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-024-02480-y

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().