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 | Plant Disease Identification Through Image Processing Techniques |
|---|---|
| ABSTRACT | Plant diseases significantly affect agricultural productivity and food security worldwide. They reduce both the quantity and quality of crops. Timely and accurate disease detection is vital for effective crop management and sustainable farming. Traditional disease diagnosis relies heavily on visual checks by agricultural experts. This method can be costly, subjective, and impractical for large-scale farming. With rapid advancements in computing technologies, automated plant disease identification using image processing has become a reliable solution [1], [2]. This research paper presents a thorough study of plant disease identification using image processing techniques. The paper outlines the entire workflow, including image acquisition, preprocessing, segmentation, feature extraction, and classification. It discusses both traditional machine learning methods and recent deep learning approaches in detail [3]–[5]. The paper also addresses current challenges, performance evaluation metrics, and future research directions, especially in precision agriculture and smart farming systems [4]. |
| AUTHOR | UJJWAL SHARMA, ANISH RANJAN SHARMA, MAYANK VERMA, SUYASH KUMAR SINGH Department of Computer Science, Galgotias University, Greater Noida, India |
| VOLUME | 183 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1404047 |
| pdf/47_Plant Disease Identification Through Image Processing Techniques.pdf | |
| KEYWORDS | |
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