International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI Based Automatic Plant Disease Detection System |
|---|---|
| ABSTRACT | Plant diseases represent a serious challenge to agricultural productivity, resulting in substantial economic losses and deterioration of crop quality. Conventional methods of disease identification rely Mostly depends on manual work inspection, which is labor-intensive, time-consuming, and dependent on expert knowledge, making them impractical for large-scale farming operations. It accurately identifies plant diseases from leaf images.The proposed system includes image steps such as noise reduction and enhancement techniques image enhancement, followed by robust classification techniques such as Support Vector Machines (SVM), Radial Basis Function SVM , ID3, and Random Forest to enhance detection accuracy. By automating |
| AUTHOR | MADHURI DEEKSHITH S, VINAY N KADLE, AYUSH D C Project Guide, Department of Computer Science and Design, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India Department of Computer Science and Design, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312125 |
| pdf/125_AI Based Automatic Plant Disease Detection System.pdf | |
| KEYWORDS | |
| References | 1. Models & Trends - s2025: “Deep Learning & Computer Vision in Plant Disease Detection” 2. Research Gate Review - 2025: “Drone-Assisted Disease Detection in Large-Scale Agriculture” 3. RajKumar et al. - 2024: “AI Assisted Plant Detection, Crop and Fertilizer Recommendation System” 4. Demilie et al. - 2024: “Plant Disease Detection & Classification Techniques — Comparative Study” 5. Saha et al. - 2024: “Semantic Segmentation for Plant Leaf Disease Classification & Damage Detection” 6. Gohil & Bhattacharjee et al. - 2024: “Hybrid Technique for Real-Time Plant Disease Identification” 7. R. Alzubaidi & M. Zhang - 2024: “Vision Transformer-Based Plant Disease Recognition Using Field Images” 8. Ibrahim & Elghamrawy - 2023: “Plant Disease Detection Using AI Techniques for Agricultural Productivity Enhancement in Egypt” 9.Adiba Khan & Atul Srivastava - 2023: “PlantDoc – Plant Disease Detection Using AI” 10. Frontiers in Plant Science Review - 2023: “Advanced Deep Learning Models-Based Plant Disease Detection” 11.Yasin & Fatima - 2023: “Image-Based Detection of Tomato and Corn Leaf Diseases — Comparative Experiments” 12. Kumar, Suhasini & Anand - 2023: “2D CNN-Based Crop Disease Detection System” 13.Hamed & Hussein - 2023: “Plant Disease Detection Using EfficientNetV2S-Based CNN” 13.Varshney & Saxena - 2022: “Plant Automated Disease Detection Using Deep Learning and Machine Learning Approaches Techniques” 14.AI-Based Drone System - 2022: “Multiclass Plant Disease Detection” 15.Sunidhi N & Dr. Jalaja S - 2021: “AI Deep Learning-Based Automatic Crop Disease Detection System” 16. 2021: “Plant Disease Detection Using a Hybrid Model Based on CAE and CNN” 17.Alzubaidi & Zhang - 2021: “Vision Transformer-Based Plant Disease Recognition Using Field Images” 18. Chen, Li & Wang - 2021: “Lightweight MobileNetV3-Based Real-Time Plant Disease Detection System” 19.Rahman, Singh & Kaur - 2021: “Lightweight CNN-Based Early Plant Leaf Disease Detection System” 20.Sharma & Patil – 2021: “Transfer Learning-Based Plant Disease Classification Using ResNet50” |
