ArticlePlants (Basel, Switzerland)2022
Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model.
Article in Plants (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 222 citations in OpenAlex.
- A hybrid deep learning model with adaptive feature fusion for automated rice leaf disease detection and classification.Scientific reports · 2026Article
- HFA-Net: Explainable Multi-Scale Deep Learning Framework for Illumination-Invariant Plant Disease Diagnosis in Precision Agriculture.Sensors (Basel, Switzerland) · 2026Article
- Utilizing deep learning models for early detection and classification of fruit diseases: towards sustainable agriculture and enhanced food quality.Scientific reports · 2026Article
- AgriX-SENet: Squeeze-and-Excitation-based deep learning framework for explainable plant disease detection in sustainable agriculture.Frontiers in plant science · 2026Article
- Enhanced rice leaf disease classification via contour-driven segmentation and optimized deep transfer learning architectures.PloS one · 2026Article
- AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas.Scientific reports · 2025Article
- Neural Network-Based Study for Rice Leaf Disease Recognition and Classification: A Comparative Analysis Between Feature-Based Model and Direct Imaging Model.Food science & nutrition · 2025Article
- Fusion of Robotics, AI, and Thermal Imaging Technologies for Intelligent Precision Agriculture Systems.Sensors (Basel, Switzerland) · 2025Review
- Integrating Artificial Intelligence and Biotechnology to Enhance Cold Stress Resilience in Legumes.Plants (Basel, Switzerland) · 2025Review
- ADAM-DETR: an intelligent rice disease detection method based on adaptive multi-scale feature fusion.Plant methods · 2025Article
- An automated hybrid deep learning framework for paddy leaf disease identification and classification.Scientific reports · 2025Article
- Rice Disease Detection: TLI-YOLO Innovative Approach for Enhanced Detection and Mobile Compatibility.Sensors (Basel, Switzerland) · 2025Article
- Detection of cotton crops diseases using customized deep learning model.Scientific reports · 2025Article
- Rice leaf disease classification using a fusion vision approach.Scientific reports · 2025Article
- Image-based yield prediction for tall fescue using random forests and convolutional neural networks.Frontiers in plant science · 2025Article
- An enhanced classification system of various rice plant diseases based on multi-level handcrafted feature extraction technique.Scientific reports · 2024Article
- Fiber-Optic Sensor Spectrum Noise Reduction Based on a Generative Adversarial Network.Sensors (Basel, Switzerland) · 2024Article
- An ensemble deep learning models approach using image analysis for cotton crop classification in AI-enabled smart agriculture.Plant methods · 2024Article
- Advancements in rice disease detection through convolutional neural networks: A comprehensive review.Heliyon · 2024Review
- Robust diagnosis and meta visualizations of plant diseases through deep neural architecture with explainable AI.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
Rice is considered one the most important plants globally because it is a source of food for over half the world's population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20-40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.