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Dhanashri Naktode,
Shilpa Jahagirdar,
Prathamesh Kamble,
Vishvajit Bamdale,
- Student, Department of Electronics &Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Maharashtra, India
- Student, Department of Electronics &Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Maharashtra, India
- Student, Department of Electronics &Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Maharashtra, India
- Student, Department of Electronics &Telecommunication Engineering, Smt. Kashibai Navale College of Engineering (SKNCOE), Pune, Maharashtra, India
Abstract document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_114122’);});Edit Abstract & Keyword
The field of agriculture faces significant threats, including diseases that attack plant leaves. To address this issue, our system assists farmers in promptly detecting plant diseases using advanced technology. The user, typically a farmer, only needs to capture an image of the affected leaf and input it into our system. Our system then analyzes the uploaded image to accurately identify the specific disease afflicting the leaf. This analytical process is facilitated by deploying two algorithms: Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). The CNN is particularly adept at capturing intricate visual features, making it highly effective for image-based analysis. It processes the leaf images, identifying patterns and anomalies that are indicative of specific diseases. On the other hand, the ANN excels in unraveling complex data relationships and can complement the CNN by providing a broader context to the detected visual patterns. The ANN processes the features extracted by the CNN and analyzes them further, considering additional data inputs that may be relevant to disease identification. By scrutinizing the outcomes of both algorithms, our system derives an amalgamated result. This result represents an elevated diagnostic insight, informed by the distinctive strengths and perspectives of both CNN and ANN. The combined approach enhances the accuracy and reliability of disease detection. Our system expedites the identification of plant diseases, empowering farmers with timely and precise information essential for effective disease management. Early detection allows for prompt intervention, reducing the spread of diseases and minimizing crop damage. The integration of CNN and ANN ensures that our system not only identifies diseases accurately but also provides a comprehensive understanding of the disease dynamics, aiding in better decision-making for disease control. This technological advancement stands to significantly benefit the agricultural sector by safeguarding crop health and ensuring higher yields.
Keywords: Modern Agriculture, Disease Identification, Fertilizers, Convolutional Neural Network, Deep Learning
[This article belongs to Current Trends in Signal Processing (ctsp)]
Dhanashri Naktode, Shilpa Jahagirdar, Prathamesh Kamble, Vishvajit Bamdale. Deep Learning based Solution for Leaf disease Detection in Crops and Fertilizer Recommendation. Current Trends in Signal Processing. 2024; 14(03):31-40.
Dhanashri Naktode, Shilpa Jahagirdar, Prathamesh Kamble, Vishvajit Bamdale. Deep Learning based Solution for Leaf disease Detection in Crops and Fertilizer Recommendation. Current Trends in Signal Processing. 2024; 14(03):31-40. Available from: https://journals.stmjournals.com/ctsp/article=2024/view=0
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Current Trends in Signal Processing
| Volume | 14 |
| Issue | 03 |
| Received | 19/08/2024 |
| Accepted | 07/10/2024 |
| Published | 18/11/2024 |