SporeDet: A Real-Time Detection of Wheat Scab Spores.

Wheat scab is a destructive plant disease that has caused significant damage to wheat crops worldwide. The detection of wheat scab spores is essential to ensure the safety of wheat production. However, traditional detection methods require expert opinion in their detection processes, leading to less efficiency and higher cost. In response to this problem, this paper proposes a spore detection method, SporeDet, based on a holistic architecture called ‘backbone-FPN-head’. Specifically, the method utilizes RepGhost with FPN to fuse feature information from the backbone while minimizing the model’s parameters and computation. Additionally, a task-decomposition channel attention head (TDAHead) is designed to predict the classification and localization of FPN features separately, thereby improving the accuracy of spore detection. Furthermore, a feature reconstruction loss (RecLoss) is introduced to further learn the features of RGB images during the training process, which accelerates the convergence of the model. The proposed method is evaluated on spore detection datasets collected from the Anhui Academy of Agricultural Sciences. Experimental results demonstrate that the SporeDet method achieves an optimal mean average precision (mAP) of 88%, and the inference time of the model reaches 4.6 ms on a 24 GB GTX3090 GPU. Therefore, the proposed method can effectively improve spore detection accuracy and provide a reference for detecting fungal spores.