Plant Leaf Disease Detection Using Resnet-50 Based on Deep Learning

S. Jayashree () and V. Sumalatha
Additional contact information
S. Jayashree: VISTAS, Research Scholar, Assistant Professor
V. Sumalatha: VISTAS, Research Supervisor, Associate Professor

A chapter in Proceedings of the International Conference on Digital Transformation in Business: Navigating the New Frontiers Beyond Boundaries (DTBNNF 2024), 2024, pp 150-166 from Springer

Abstract: Abstract India’s agriculture permits the world food chain by producing various crops and boosting the country’s economy. Diseases pose a significant challenge to agricultural production. It causes crop disruption, lowers output, and makes it extremely hard for farmers to compensate for planting damage. Early disease detection and rapid action are essential to preventing productivity loss. Currently, several methods for analyzing illness characteristics and figuring out the stage of progression use Machine Learning (ML) for image processing. However, because disease features vary, it is challenging to determine the regional segments. Unbalanced traits can complicate the detection of diseases. To resolve this problem, initially, we collected the plant image dataset from Kaggle. We applied preprocessing steps, including Gaussian and Wiener filters, to normalize plant leaves. Furthermore, plant leaf features can be selected using the Canny Region Extraction (CRE) technique for non-edge and smoothing. Moreover, the Multilevel Threshold Segmentation (MRDS) method can identify pixel groups and classify the optimal values. Finally, the proposed ResNet50 Optimal Convolutional Neural Network (ROCNN) method can categories the results to obtain binary plant classification. As a result, accuracy for plant leaf diseases can be obtained using high false rates, imprecise recognition, high precision, F-measure and low recall efficiency.

Keywords: Plant leaf; Machine Learning; CRE; Gaussian and Wiener filters; MRDS; ROCNN; classification; accuracy; Recall; F-measure (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:advbcp:978-94-6463-433-4_12

Ordering information: This item can be ordered from
http://www.springer.com/9789464634334

DOI: 10.2991/978-94-6463-433-4_12

Access Statistics for this chapter

More chapters in Advances in Economics, Business and Management Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().