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Publication
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| Title of Article |
Cassava stalk detection for a cassava harvesting robot based on YOLO v4 and Mask R-CNN |
| Date of Acceptance |
6 July 2023 |
| Journal |
| Title of Journal |
Journal of Agricultural Engineering |
| Standard |
SCOPUS |
| Institute of Journal |
Italian Society of Agricultural Engineering. |
| ISBN/ISSN |
2239-6268 |
| Volume |
54 |
| Issue |
2 |
| Month |
8 |
| Year of Publication |
2023 |
| Page |
7-21 |
| Abstract |
The quality of fresh cassava roots can be increased through the
use of precision equipment. As a first step towards developing an
automatic cassava root cutting system, this study demonstrates the
use of a computer vision system with deep learning for cassava
stalk detection. An RGB image of a cassava tree mounted on a
cassava-pulling machine was captured, and the YOLO v4 model
and two Mask R-CNN models with ResNet 101 and ResNet 50
base architectures were employed to train the weights to predict
the position of the cassava stalk. One hundred test images of stalks
of various shapes and sizes were used to determine the grasping
point and inclination, and the results from manual annotation were
compared with the predicted results. Regarding localisation, Mask
R-CNN with ResNet 101 gave a significantly higher performance
than the other models, with an F1 score and a mean IoU of 0.81
and 0.70, respectively. YOLO v4 showed the highest correlation
for the x- and y-coordinates for the prediction of the grasping
point, with values for R2 of 0.89 and 0.53, respectively. For inclination prediction, Mask R-CNN with ResNet 101 and Mask
R-CNN with ResNet 50 gave the same level of correlation, with
values for R2 of 0.50 and 0.61, respectively. These results were
acceptable for use as design criteria for developing a cassava root cutting robot. |
| Keyword |
automatic harvester, cassava root, computer vision, crop detection, stalk detection. |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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