Abstract
Plant diseases are a major cause of reduced crop yield and poor quality, which can lead to significant economic losses for farmers. In recent years, the need for quick and accurate methods to detect these diseases has increased, especially for large farming areas. However, many farmers still struggle to adapt to new disease control methods. Traditionally, disease detection has depended on experts visually checking the leaves and other parts of plants. This process takes a lot of time and can sometimes lead to mistakes. This paper focuses on developing an easy-to-use and reliable system to detect diseases in plant leaves, helping to improve agricultural productivity. Early identification of plant diseases gives farmers useful information to manage problems in time and prevent further damage. The proposed system includes several main steps: capturing images of the leaves, processing the images to improve their quality, extracting key features, and then classifying the disease using a neural network. The paper also discusses the advantages and limitations of existing detection methods to identify areas where further improvements can be made.
Citations
IRE Journals:
Prof. Dr. S. P. Khandait, Chetna Vijay Bhati, Minal Najukrao Jadhao, Pranjali Ajay Lambat, Punam Amrut Khadse "Implementation of Plant Leaf Disease Detection & Classification" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 971-977 https://doi.org/10.64388/IREV9I6-1712778
IEEE:
Prof. Dr. S. P. Khandait, Chetna Vijay Bhati, Minal Najukrao Jadhao, Pranjali Ajay Lambat, Punam Amrut Khadse
"Implementation of Plant Leaf Disease Detection & Classification" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1712778
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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