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Development of Automatic Conveyor-Belt Insect Pests Inspection Machine for Cut Orchids Flowers |
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| รหัสดีโอไอ | |
| Creator | Parinyawat Yoothongin |
| Title | Development of Automatic Conveyor-Belt Insect Pests Inspection Machine for Cut Orchids Flowers |
| Contributor | Anuchit Chamsing, Jirawat Chiatrakul, Puttinun Jarruwat, Preecha Ananrattanakul, Pongrawee Namwong, Arnon Saicomfu, Pruetthichat Punyawattoe, Srijumnun Srijuntra, Kochathorn Angboonpong, Surachat Rayathong, Nuthasit Youyen |
| Publisher | Thai Socity of Agricultural Engineering |
| Publication Year | 2566 |
| Journal Title | Thai Socity of Agricultural Engineering Journal |
| Journal Vol. | 29 |
| Journal No. | 1 |
| Page no. | 1-7 |
| Keyword | Orchid cut flower, Orchids Insect pests, Automatic conveyor belts, Convolutional neural network |
| URL Website | https://li01.tci-thaijo.org/index.php/TSAEJ/index |
| Website title | Thai Socity of Agricultural Engineering Journal |
| ISSN | 2651-222X |
| Abstract | Exported cut orchids inflorescences need to be inspected to prevent infestation;especially from common cutworms;orchid midges and cotton thrips. Such an inspection task requires specialized skills; prolonged inspection nevertheless results in fatigue and hence lower inspection efficiency. An automatic conveyor-belt inspection machine employing image processing technology was therefore developed in the present research. Orchids inflorescences were transported into the photography chamber via a conveyor belt; photos were taken using several cameras installed at several angles. Image data were analyzed via convolutional neural network (CNN) to classify images into those consisting of common cutworms;orchid midges;cotton thrips and those with no insect pests. Based on the classification results;the image classification efficiencies were 78.6% for common cutworms, 68.0% for orchid midges;39.8% for cotton thrips and 39.1% for non-pest images. Classification errors were due to the excessively smaller sizes of insects;especially in the cases of orchid midges and cotton thrips. These resulted in images with and without insect pests were similar and not distinguishable by the model. Classification accuracy of the machine was not yet at a level suitable for industrial use. The model must be further developed to allow detection of small insect pests with higher accuracy. |
