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Published Paper Details:
Leaf Disease Detection & Removal Approach Using Image Processing
Authors
Shailesh Parmar
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Keywords
Finding Leaf Diseases, Image Manipulation, Image Processing, Artificial Intelligence, Extraction of Features, Neural networks with convolutions (CNN), SVMs, or support vector machines, Agronomic Automation, Classification of Diseases, Ecological Farming, Crop Management, Automated Elimination, Precision Farming, Enhancement of Image, Texture Evaluation.
Abstract
Agricultural productivity is significantly threatened by leaf diseases, which calls for early detection and efficient management techniques. This work uses image processing techniques to propose a novel way of identifying and eliminating leaf diseases. We use computer vision algorithms to identify symptoms of different diseases by analysing photos of leaves taken under controlled conditions. Preprocessing the photos to improve their quality is the first step in the methodology. Machine learning models are then used for feature extraction and classification. Our method shows promise in real time disease detection of leaves, offering farmers and agricultural experts a dependable tool. Furthermore, we investigate robotic technology-integrated automated removal solutions aimed at enhancing crop management operational efficiency. The findings show that this strategy not only improves the precision of disease identification but also
A major problem in agriculture is leaf diseases, which lower yields and cause financial losses. Minimizing these effects requires prompt discovery and efficient treatment. This work presents a thorough method for identifying and eliminating leaf diseases utilizing cutting-edge image processing techniques.
We employ a number of techniques, beginning with taking high-resolution pictures of leaves in various lighting scenarios. Preprocessing is applied to these photos to improve contrast and eliminate noise, emphasizing the important characteristics. Techniques including colour analysis, texture descriptors, and form recognition are used in feature extraction to identify particular disease symptoms.
We use machine learning algorithms, such as support vector machines (SVMs) and convolutional neural networks (CNNs), to categorize the extracted characteristics. These algorithms are trained on a robust dataset that consists of leaf samples that are both diseased and healthy. Metrics including precision, recall, and F1 score are used to assess the categorization accuracy.
The study looks at automated removal techniques that use robotic devices with image processing skills in addition to detection. By precisely identifying and removing impacted leaves, these technologies can lessen the need for chemical treatments and encourage environmentally friendly farming methods.
Our results show that by enabling targeted interventions, this integrated strategy not only improves the accuracy of leaf disease diagnosis but also benefits the general health of crops. This study provides a viable route for more intelligent
IJCRT's Publication Details
Unique Identification Number - IJCRT2410272
Paper ID - 270604
Author type - Indian Author
Page Number(s) - c368-c379
Pubished in - Volume 12 | Issue 10 | October 2024
DOI (Digital Object Identifier) -
No Of Downloads - 140
Author Country - India, 391760, Vadodara, Vadodara, 391760, Science and Technology
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Shailesh Parmar, "Leaf Disease Detection & Removal Approach Using Image Processing", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 10, pp.c368-c379, October 2024, Available at :http://www.ijcrt.org/papers/IJCRT2410272.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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