การจำแนกความสุกของทะลาย ปาล์มสด ด้วยการเรียนรู้เชิงลึก

Authors

นายcoauthor coauthor, ผศ.ดร.ฐิติรัตน์ ศิริบวรรัตนกุล

Published

วารสารวิจัยและพัฒนา มจธ.

Abstract

Currently, palm-oil mills in Thailand employ staff to visually assess
the ripeness of fresh palm bunches at point of purchase. This practice
suffers from some limitations; the staff sometimes misjudges the ripeness of fresh palm bunches. As a result, palm-oil mills have a higher
than usual cost of purchasing palm bunches. The present research
therefore aimed to develop a deep-learning model that can be used
to analyze photographs of oil palm fresh fruit bunches and accurately
classify their ripeness. The results showed that the ResNet50(C) model
provided the best adjusted accuracy at 90%, where the F1 score of
each category was noted to be higher than 80% ripeness. However, it
is a large model and requires a longer average testing time (405 MB,
2.48 seconds (GPU), 3.27 seconds (CPU)). If a smaller model size is desired and a faster average testing time is needed, DenseNet121 (Train
from Scratch) model can instead be considered. Although the model
provided the adjusted accuracy at 86%, slightly less than that of the
ResNet50(C) model, its F1 score for each class was as well above 80%;
the model is smaller and requires a shorter average testing time (100
MB, 1.76 seconds (GPU), 2.56 seconds (CPU)). The model is also highly
robust to changes in the brightness of the photographs (range -70 to
+70 from normal sunlight).

(2566). การจำแนกความสุกของทะลายปาล์มสดด้วยการเรียนรู้เชิงลึก. วารสารวิจัยและพัฒนา มจธ., 2023(1), 81-104.