Q3 2024

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

Mohsin Raad Kareem
10.23851/mjs.v35i1.1416 390 المشاهدات 9 الاقتباسات
9
الاقتباسات
390
المشاهدات
الملخص

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.

الاستشهاد بهذا المقال (APA)
Mohsin, R. K. (2024). Image Analysis and Detection of Olive Leaf Diseases Using Recurrent Neural Networks. Al-Mustansiriyah Journal of Science. https://doi.org/10.23851/mjs.v35i1.1416
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نُشر في
الرقم الدولي ISSN 1814-635X
الربعية Q3
درجة المؤشر القياس العربي 72
التخصص Natural Sciences
الناشر Al-Mustansiriyah University
الدولة 🇮🇶 Iraq
عرض ملف المجلة →
المؤلفون
تفاصيل النشر
السنة 2024
اللغة English
أُضيف في 23 Jul 2026