A rapid, low-cost wheat spike grain segmentation and counting system based on deep learning and image processing

Abstract

Spike grain number is a crucial parameter when estimating wheat yield. However, most methods predominantly focus on a single period, lacking universality. To achieve rapid and intelligent wheat spike grain counting from the filling stage to the mature stage, this study used different smartphones to obtain wheat ear images of different varieties and constructs the spike grain dataset required for deep learning segmentation model through image normalization and data enhancement. Then we propose a segmentation model called Multi-Attention TransUNet (MATransUNet) and a spike grain counting model (SGCountM) to achieve wheat spike grain counting. Our results indicate that, comparing with other models, MATransUNet achieves the best segmentation performance with strong robustness and generalization capabilities, achieving a mean intersection over union(mIoU) of 85.93%. Through the comparative analysis with manual counting results, our Method I, doubling the number of grains on one side of the ears, results in a root mean squared error (RMSE) of 4.38 and a coefficient of determination(R2) of 0.85; while the Method II, adding the grains on both sides of the ears, yields a RMSE of 3.13 and a R2 of 0.93. The counting accuracy significantly improves compared to not using the SGCountM. Moreover, transfer learning notably enhances the accuracy of the segmentation and counting model, enabling accurate spike grain counting from the filling stage to the mature stage. Finally, we integrate MATransUNet and SGCountM to design and implement a wheat spike grain segmentation and counting system (WeChat mini program). In practical tests, the mini program can effectively, accurately segments and counts wheat spike grain, demonstrating its feasibility and effectiveness. This research provides valuable insights into the intelligent application of wheat spike grain counting.