Automatic Detection of Stored Grain Fungal Spores Based on Deep Learning.

Ensuring the quantity and quality safety of grain storage is very important for social stability and human health. However, fungal spore infection has become a challenge for grain storage, affecting grain quality and causing economic losses. The traditional fungal spore detection method is mainly operated manually by professionals, which is time-consuming and tedious. This study establishes an image dataset containing 36,643 fungal spores and proposes an automatic detection algorithm for fungal spores based on deep learning and image processing techniques. Due to the similar morphological characteristics of different fungal species, the insufficient visibility of color features, and the occurrence of overlapping and gathering of fungal spores, the detection of fungal spores in microscopic images faces challenges. Thus, an attention mechanism is incorporated into the YOLOv5 algorithm, along with optimizations in the loss function and post-processing of the prediction results. The proposed algorithm achieves the mAP of 0.899 in fungal spores detection, The experimental results demonstrate the efficacy of the proposed algorithm in detecting fungal spores within microscopic images and thereby offer substantial evidence supporting the early detection of fungal infection.