Image Analysis and Detection of Olive Leaf Diseases Using Recurrent Neural Networks

Authors

  • Mohsin Raad Kareem Department of Computer Science, College of Basic Education, Mustansiriyah University, IRAQ.

DOI:

https://doi.org/10.23851/mjs.v35i1.1416

Keywords:

Recurrent Neural Network (RNN), Texture Based Image Retrieval (TBIR), Disease diagnosis, Image analysis, Deep learning

Abstract

The widespread adoption of DL has led to a rise in academic interest in image recognition approaches, enabling applications such as automated image classification and the detection of plant diseases. The world's largest producer of olives is Morocco. Plant health might be harmed by illnesses, which therefore affects its development. Numerous illnesses affecting olive leaves specifically target crop growth rate. The objective of this research is to create deep RNNs to identify olive plant illnesses using a collection of leaf images, collected from various sources (Disease note The peacock eye falls on olive trees, Field Guide to Olive Pests, Diseases and Disorders in Australia. Thus, this technique is the best RNN model and is employed in further applications to enhance diagnostic measurements regarding olive leaves and other plant leaves.

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Key Dates

Received

26-05-2023

Revised

05-07-2023

Accepted

16-08-2023

Published

30-03-2024

Issue

Section

Original Article

How to Cite

[1]
M. R. Kareem, “Image Analysis and Detection of Olive Leaf Diseases Using Recurrent Neural Networks”, Al-Mustansiriyah Journal of Science, vol. 35, no. 1, pp. 60–65, Mar. 2024, doi: 10.23851/mjs.v35i1.1416.

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