no code implementations • 7 May 2024 • Raiyan Rahman, Christopher Indris, Goetz Bramesfeld, Tianxiao Zhang, Kaidong Li, Xiangyu Chen, Ivan Grijalva, Brian McCornack, Daniel Flippo, Ajay Sharda, Guanghui Wang
In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection.
no code implementations • 10 Aug 2023 • Tianxiao Zhang, Kaidong Li, Xiangyu Chen, Cuncong Zhong, Bo Luo, Ivan Grijalva, Brian McCornack, Daniel Flippo, Ajay Sharda, Guanghui Wang
To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151, 380 image patches.
no code implementations • 17 Jul 2023 • Raiyan Rahman, Christopher Indris, Tianxiao Zhang, Kaidong Li, Brian McCornack, Daniel Flippo, Ajay Sharda, Guanghui Wang
We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids.
no code implementations • 12 Jul 2023 • Tianxiao Zhang, Kaidong Li, Xiangyu Chen, Cuncong Zhong, Bo Luo, Ivan Grijalva Teran, Brian McCornack, Daniel Flippo, Ajay Sharda, Guanghui Wang
Aphids are one of the main threats to crops, rural families, and global food security.
1 code implementation • 23 Jan 2022 • Tianxiao Zhang, Bo Luo, Ajay Sharda, Guanghui Wang
For anchor-based detection models, the IoU (Intersection over Union) threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold.