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- W4312823003 abstract "According to various studies, the world population will exceed 10 billion in 2050, and with the increasing demand for food quantity, maintaining quality will become more difficult. On-field, some undesirable plants grow along with the crops, which affects the crop yields. They share the crop’s resources; therefore, the actual crop generally lacks the necessary nutrients and other factors. To treat such weed plants, farmers use herbicides, which affect the food quality, soil condition, and environment. A real-time weed identification in the field is required at this time so that a proper diagnosis can be made to stop the growth of such weed plants. In this work, soybean images are captured using a UAV device, and weed images are labelled to generate a dataset. Total, 3324 images are labelled in the form of bounding boxes to localize the weed area. To evaluate the performance, two more datasets are taken into consideration. A comparison of three different object detection models, YOLO v3, YOLO v4, and Faster RCNN, has been performed. As per the results, YOLO v4 achieved the best results among the three methods on all three datasets, with 90.00% mAP on the soybean dataset." @default.
- W4312823003 created "2023-01-05" @default.
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- W4312823003 date "2022-01-01" @default.
- W4312823003 modified "2023-09-30" @default.
- W4312823003 title "An In-Field Real-Time Automatic Weed Detection Using Deep Learning Techniques" @default.
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- W4312823003 doi "https://doi.org/10.1007/978-981-19-4687-5_12" @default.
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