A Vision Based Crop Monitoring System Using Segmentation Techniques
The characterization of health status for a plant using a non-destructive method is one of the challenging problems. In this study, the number of leaves and discoloration properties have been estimated using the images obtained from nine saplings of Solanum melongena (eggplant or brinjal) grown in...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Stefan cel Mare University of Suceava
2020-05-01
|
| Series: | Advances in Electrical and Computer Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.4316/AECE.2020.02011 |
| _version_ | 1818292527362473984 |
|---|---|
| author | KRISHNASWAMY RANGARAJAN, A. PURUSHOTHAMAN, R. |
| author_facet | KRISHNASWAMY RANGARAJAN, A. PURUSHOTHAMAN, R. |
| author_sort | KRISHNASWAMY RANGARAJAN, A. |
| collection | DOAJ |
| description | The characterization of health status for a plant using a non-destructive method is one of the challenging
problems. In this study, the number of leaves and discoloration properties have been estimated using the
images obtained from nine saplings of Solanum melongena (eggplant or brinjal) grown in the laboratory.
The images were obtained using a mobile phone camera fitted on an automated device. A particle wave
algorithm and contour grow technique was used for the segmentation of leaves which resulted in a
segmentation accuracy of 89%. The defective percentage was estimated based on which saplings were
ranked. Validation of healthy and defective regions was done by applying linear regression analysis
on the estimated Normalized Green Red Difference Index (NGRDI) from images obtained using an automated
device and a Foldscope (new paper-based microscope). The analysis resulted in R squared value and
Least Mean Square Error (LMSE) of 0.86 and 0.1 respectively. |
| first_indexed | 2024-12-13T03:01:23Z |
| format | Article |
| id | doaj.art-528fc801ddad4758800ad539d360f84a |
| institution | Directory Open Access Journal |
| issn | 1582-7445 1844-7600 |
| language | English |
| last_indexed | 2024-12-13T03:01:23Z |
| publishDate | 2020-05-01 |
| publisher | Stefan cel Mare University of Suceava |
| record_format | Article |
| series | Advances in Electrical and Computer Engineering |
| spelling | doaj.art-528fc801ddad4758800ad539d360f84a2022-12-22T00:01:49ZengStefan cel Mare University of SuceavaAdvances in Electrical and Computer Engineering1582-74451844-76002020-05-012028910010.4316/AECE.2020.02011A Vision Based Crop Monitoring System Using Segmentation TechniquesKRISHNASWAMY RANGARAJAN, A.PURUSHOTHAMAN, R.The characterization of health status for a plant using a non-destructive method is one of the challenging problems. In this study, the number of leaves and discoloration properties have been estimated using the images obtained from nine saplings of Solanum melongena (eggplant or brinjal) grown in the laboratory. The images were obtained using a mobile phone camera fitted on an automated device. A particle wave algorithm and contour grow technique was used for the segmentation of leaves which resulted in a segmentation accuracy of 89%. The defective percentage was estimated based on which saplings were ranked. Validation of healthy and defective regions was done by applying linear regression analysis on the estimated Normalized Green Red Difference Index (NGRDI) from images obtained using an automated device and a Foldscope (new paper-based microscope). The analysis resulted in R squared value and Least Mean Square Error (LMSE) of 0.86 and 0.1 respectively.http://dx.doi.org/10.4316/AECE.2020.02011agricultural engineeringcropsimage processingfoldscopeimage segmentation |
| spellingShingle | KRISHNASWAMY RANGARAJAN, A. PURUSHOTHAMAN, R. A Vision Based Crop Monitoring System Using Segmentation Techniques Advances in Electrical and Computer Engineering agricultural engineering crops image processing foldscope image segmentation |
| title | A Vision Based Crop Monitoring System Using Segmentation Techniques |
| title_full | A Vision Based Crop Monitoring System Using Segmentation Techniques |
| title_fullStr | A Vision Based Crop Monitoring System Using Segmentation Techniques |
| title_full_unstemmed | A Vision Based Crop Monitoring System Using Segmentation Techniques |
| title_short | A Vision Based Crop Monitoring System Using Segmentation Techniques |
| title_sort | vision based crop monitoring system using segmentation techniques |
| topic | agricultural engineering crops image processing foldscope image segmentation |
| url | http://dx.doi.org/10.4316/AECE.2020.02011 |
| work_keys_str_mv | AT krishnaswamyrangarajana avisionbasedcropmonitoringsystemusingsegmentationtechniques AT purushothamanr avisionbasedcropmonitoringsystemusingsegmentationtechniques AT krishnaswamyrangarajana visionbasedcropmonitoringsystemusingsegmentationtechniques AT purushothamanr visionbasedcropmonitoringsystemusingsegmentationtechniques |