2023
DOI: 10.3390/agronomy13082155
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Research on Insect Pest Identification in Rice Canopy Based on GA-Mask R-CNN

Sitao Liu,
Shenghui Fu,
Anrui Hu
et al.

Abstract: Aiming at difficult image acquisition and low recognition accuracy of two rice canopy pests, rice stem borer and rice leaf roller, we constructed a GA-Mask R-CNN (Generative Adversarial Based Mask Region Convolutional Neural Network) intelligent recognition model for rice stem borer and rice leaf roller, and we combined it with field monitoring equipment for them. Firstly, based on the biological habits of rice canopy pests, a variety of rice pest collection methods were used to obtain the images of rice stem … Show more

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Cited by 5 publications
(2 citation statements)
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“…Through its intricate network structure and effective multi-task learning strategy, high-precision object detection and segmentation have been achieved. With ongoing research, Mask R-CNN has been widely applied in multiple domains [51], giving rise to numerous variants and improved models, thereby further expanding its application range and performance. For the research presented in this paper, Mask R-CNN, combining the functionalities of object detection and semantic segmentation, precisely locates and accurately segments disease spots in peach images.…”
Section: Maskrcnnmentioning
confidence: 99%
“…Through its intricate network structure and effective multi-task learning strategy, high-precision object detection and segmentation have been achieved. With ongoing research, Mask R-CNN has been widely applied in multiple domains [51], giving rise to numerous variants and improved models, thereby further expanding its application range and performance. For the research presented in this paper, Mask R-CNN, combining the functionalities of object detection and semantic segmentation, precisely locates and accurately segments disease spots in peach images.…”
Section: Maskrcnnmentioning
confidence: 99%
“…Traditional target detection methods, such as sliding windows and manual feature extraction, are exemplified by techniques like Haar [3], HOG [4], Hu moment [5], SIFT [6], SURF [7], and DPM [8]. The evolution of computer vision and deep learning has ushered target detection into agricultural production prominence, with algorithms bifurcated into single-stage (e.g., YOLO series [9][10][11], SSD series [12][13][14], RetinaNet series [15,16]) and two-stage detection algorithms (e.g., RCNN series [17], FasterRCNN series [18]). Apple target detection, melding computer vision and agriculture, automates apple identification and localization in images.…”
Section: Introductionmentioning
confidence: 99%