Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
Volume-3 | Issue-3
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
Volume-3 | Issue-3
Probabilistic Neural Network based Managing Algorithm for Building Automation System
Volume-3 | Issue-4
Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Volume-3 | Issue-1
Multi-scale CNN Approach for Accurate Detection of Underwater Static Fish Image
Volume-3 | Issue-3
Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4
Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4
ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
Volume-1 | Issue-2
Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert
Volume-3 | Issue-2
Volume - 5 | Issue - 3 | september 2023
Published
27 July, 2023
Rice cultivation is essential to the global economy, particularly in India, where it holds the distinction of being the largest rice exporter and the second-largest rice producer. However, the agricultural sector faces significant challenges due to diseases and pests that negatively impact the crops, by hindering the plant growth, reducing the yield, and, in extreme cases, leading to famine. The use of pesticides, intended to increase production, often results in a decline in crop quality. Prompt as well as precise disease identification in plants is requisite for prevention and control of disease, enabling timely implementation of pesticide control measures. This has spurred research at the intersection of computer science and agriculture, specifically focused on identifying diseases in rice through collected and real-time images. Deep learning (DL) has emerged as a key area of study within this domain, addressing various aspects of agricultural plant protection, including disease detection and pest control. Pretrained models have proven to be invaluable tools in the realm of rice plant disease identification and monitoring. These models leverage transfer learning, enhance feature extraction, reduce training time and resource requirements, improve generalization and resilience, and facilitate knowledge sharing and collaboration. This article examines rice plant diseases, explores deep learning and pre-trained models for diagnosis, reviews relevant publications, and presents a comparative analysis of research studies to assess advancements in rice plant disease detection.
KeywordsDeep learning Rice disease detection CNN models Transfer learning
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