We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Image Analysis and Detection of Olive Leaf Diseases using Recurrent Neural Networks.
- Authors
Kareem, Mohsin Raad
- 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.
- Subjects
MOROCCO; AUSTRALIA; RECURRENT neural networks; OLIVE leaves; IMAGE analysis; OLIVE; IMAGE recognition (Computer vision); PLANT classification
- Publication
Al-Mustansiriyah Journal of Science, 2024, Vol 35, Issue 1, p60
- ISSN
1814-635X
- Publication type
Article
- DOI
10.23851/mjs.v35i1.1416