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 | Automatic Recognition and Classification of Diseases in Citrus Fruit |
|---|---|
| ABSTRACT | This study introduces an approach based on convolutional neural networks (CNNs) that automatically identifies and classifies various diseases affecting citrus plants. An assorted dataset that includes images of citrus leaves and fruits exhibiting seven major diseases — anthracnose, brown rot, citrus black spot, citrus canker, citrus scab, melanose, and sooty mold — was collected for training and evaluation of the model. The CNN model identifies disease types by accurately detecting and extracting differentiating features from the images, achieving a recognition accuracy of 88% and exhibiting ability to perform reliably even when images are of varying quality and taken in different environmental climates. The early disease diagnosis enabled by the framework can drastically cut down losses and improve outcomes in citrus production. This research demonstrates the promising abilities of deep learning, specifically CNNs, as efficient, fully automated, and adaptable solutions for disease management in plants. |
| AUTHOR | SHAIK SAFEENA, A C JYOTHSNA Junior Intermediate (11th Grade), Amaravathi Junior College, Srikalahasti, Andhra Pradesh, India Lecturer in Mathematics, Amaravathi Junior College, Srikalahasti, Andhra Pradesh, India |
| VOLUME | 160 |
| DOI | DOI: 10.15680/IJIRCCE.2024.1208069 |
| pdf/69_Automatic Recognition and Classification of Diseases in Citrus Fruit.pdf | |
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