YOLO POD: a fast and accurate multi-task model for dense Soybean Pod counting.

ABSTRACT:

Background

The number of soybean pods is one of the most important indicators of soybean yield, pod counting is crucial for yield estimation, cultivation management, and variety breeding. Counting pods manually is slow and laborious. For crop counting, using object detection network is a common practice, but the scattered and overlapped pods make the detection and counting of the pods difficult.

Results

We propose an approach that we named YOLO POD, based on the YOLO X framework. On top of YOLO X, we added a block for predicting the number of pods, modified the loss function, thus constructing a multi-task model, and introduced the Convolutional Block Attention Module (CBAM). We achieve accurate identification and counting of pods without reducing the speed of inference. The

SUBMITTER: Xiang S 

PROVIDER: S-EPMC9883929 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature