Abstract
Pomacea canaliculata feeds on seedlings that have been planted less than three weeks ago. This study aimed to construct an imaging system that can eliminate the egg masses of P. canaliculata before they hatch and multiply. An image classification method is proposed that can recognize the state of hatching of the egg masses. As hatching process proceeds, this state changes from "freshly laid" to "maturing" and then to "mature." In the proposed method, first, egg image pixels are detected using a four-label semantic segmentation model that includes the background label. Next, the egg masses are classified by analyzing the distribution of the labeled pixels in the egg masses. We conducted an experiment in which we verified the effectiveness of the proposed method on images of egg masses from an agricultural canal and evaluated its classification accuracy. The F1-score of the proposed method was 1.00 when the weather was cloudy and 0.842 when it was sunny, demonstrating that the state of hatching could be identified accurately regardless of the brightness of the day. Using this classification method, only newly laid eggs can be dropped into water and eliminated, which is a step forward in the automation of this method for P. canaliculata control.