Deep Learning Based Multi Crop Disease Detection System
Journal Title: International Journal of Innovations in Science and Technology - Year 2024, Vol 6, Issue 3
Abstract
This research explores the integration of deep learning, computer vision, and edge computing to revolutionize crop disease detection. In response to the pressing need for prompt and accurate disease identification, this work leverages the capabilities of edge computing devices deployed within agricultural fields. Real-time data processing at the edge facilitates quick disease classification across various crops, enabling timely interventions. At the heart of the methodology lies a fine-tuned ResNet50 deep learning model, specifically chosen for its proficiency in handling complex visual data. Trained on a specialized dataset derived from the ImageNet database, the model exhibits promising accuracy rates in preliminary testing. Integrating edge computing into precision agriculture, this research presents a significant advancement toward sustainable agricultural practices. By empowering farmers with early detection and timely interventions, this endeavor equips agricultural communities with the knowledge and tools necessary to safeguard their crops, ensuring both food security and economic stability.
Authors and Affiliations
Gul Munir, M. Ahsan Ansari, SammerZai, Irfan Bhacho
Keywords
Related Articles
- EP ID EP760362
- DOI -
- Views 33
- Downloads 0
Performance Evaluation of Fake News Detection Using Artificial Intelligence Techniques
Introduction/Importance of Study: As the proliferation of fake news poses significant challenges to traditional fact-checking methods, there is a growing need for robust and automated approaches to combat misinformatio...
Effects of Exogenous Calcium and Magnesium on Physio-Hormonal Attributes of Trigonella Foenum-Graecum L:Under Polyethylene Glycol (PEG) Induce Drought Stress
Drought stress is one of the abiotic stresses that adversely affect the plant growth parameters and physio-hormonal attributes. In the current work, we study the adverse effects of induced PEG drought stress in Trigone...
Modified Convolutional Neural Networksfor Facial Emotion Classification
Facial expression analysis is a fascinating yet challenging problem in the realm of artificial intelligence. The vast variability in human expressions poses a significant hurdle for machine learning methods to detect t...
Machine Translation of Quranic Verses: A Transformer-Based Approach to Urdu Rendering
Translate Quranic Arabic into Urdu is a Challenge due to linguistics and theological differences. While machine translation has advanced significantly, transformer-based Neural Machine Translation (NMT)...
Deep Learning-Based Image Captioning for Visual Impairment Using a VGG16 and LSTM Approach
Visually impaired people face the challenge of gathering information about their surroundings. They are unable to make sense of visually presented information such as capturing images, reading sign boards, moving aroun...