CNN based plant disease identification using PYNQ FPGA
This research presents a novel approach for plant disease identification utilizing Convolutional Neural Networks (CNNs) and the PYNQ FPGA platform. The study leverages the parallel processing capabilities of FPGAs to accelerate CNN inference, aiming to enhance the efficiency of plant disease detecti...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Systems and Soft Computing |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000176 |
| _version_ | 1826927271631388672 |
|---|---|
| author | Vivek Karthick Perumal Supriyaa T Santhosh P R Dhanasekaran S |
| author_facet | Vivek Karthick Perumal Supriyaa T Santhosh P R Dhanasekaran S |
| author_sort | Vivek Karthick Perumal |
| collection | DOAJ |
| description | This research presents a novel approach for plant disease identification utilizing Convolutional Neural Networks (CNNs) and the PYNQ FPGA platform. The study leverages the parallel processing capabilities of FPGAs to accelerate CNN inference, aiming to enhance the efficiency of plant disease detection in agricultural settings. The implementation involves optimizing the CNN architecture for deployment on the PYNQ FPGA, considering factors such as image size and learning rates. Through experimentation, the research refines hyper parameters, achieving improved accuracy and F1 scores. Visualizations using heat maps highlight the CNN's reliance on color, shape, and texture for feature extraction in disease identification. The integration of FPGA technology demonstrates promising advancements in real-time, high-performance plant disease classification, offering potential benefits for precision agriculture and crop management. This research contributes to the growing field of FPGA-accelerated deep learning applications in agro technology, addressing challenges in plant health monitoring and fostering sustainable agricultural practices. |
| first_indexed | 2024-04-24T07:21:43Z |
| format | Article |
| id | doaj.art-0ee21a38fa2b46358efa00f05bc16fbd |
| institution | Directory Open Access Journal |
| issn | 2772-9419 |
| language | English |
| last_indexed | 2025-02-17T15:30:14Z |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj.art-0ee21a38fa2b46358efa00f05bc16fbd2024-12-19T11:02:58ZengElsevierSystems and Soft Computing2772-94192024-12-016200088CNN based plant disease identification using PYNQ FPGAVivek Karthick Perumal0Supriyaa T1Santhosh P R2Dhanasekaran S3Department of ECE, Sona College of Technology, Salem, Tamil Nadu, India; Corresponding author:Department of ECE, Sona College of Technology, Salem, Tamil Nadu, IndiaDepartment of ECE, Sona College of Technology, Salem, Tamil Nadu, IndiaDepartment ECE, Sri Eshwar College of Engineering, Coimbatore, IndiaThis research presents a novel approach for plant disease identification utilizing Convolutional Neural Networks (CNNs) and the PYNQ FPGA platform. The study leverages the parallel processing capabilities of FPGAs to accelerate CNN inference, aiming to enhance the efficiency of plant disease detection in agricultural settings. The implementation involves optimizing the CNN architecture for deployment on the PYNQ FPGA, considering factors such as image size and learning rates. Through experimentation, the research refines hyper parameters, achieving improved accuracy and F1 scores. Visualizations using heat maps highlight the CNN's reliance on color, shape, and texture for feature extraction in disease identification. The integration of FPGA technology demonstrates promising advancements in real-time, high-performance plant disease classification, offering potential benefits for precision agriculture and crop management. This research contributes to the growing field of FPGA-accelerated deep learning applications in agro technology, addressing challenges in plant health monitoring and fostering sustainable agricultural practices.http://www.sciencedirect.com/science/article/pii/S2772941924000176CNNImage ProcessingPlant DiseaseFPGAPYNQ |
| spellingShingle | Vivek Karthick Perumal Supriyaa T Santhosh P R Dhanasekaran S CNN based plant disease identification using PYNQ FPGA Systems and Soft Computing CNN Image Processing Plant Disease FPGA PYNQ |
| title | CNN based plant disease identification using PYNQ FPGA |
| title_full | CNN based plant disease identification using PYNQ FPGA |
| title_fullStr | CNN based plant disease identification using PYNQ FPGA |
| title_full_unstemmed | CNN based plant disease identification using PYNQ FPGA |
| title_short | CNN based plant disease identification using PYNQ FPGA |
| title_sort | cnn based plant disease identification using pynq fpga |
| topic | CNN Image Processing Plant Disease FPGA PYNQ |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000176 |
| work_keys_str_mv | AT vivekkarthickperumal cnnbasedplantdiseaseidentificationusingpynqfpga AT supriyaat cnnbasedplantdiseaseidentificationusingpynqfpga AT santhoshpr cnnbasedplantdiseaseidentificationusingpynqfpga AT dhanasekarans cnnbasedplantdiseaseidentificationusingpynqfpga |