An Automatic Detector for Fungal Spores in Microscopic Images Based on Deep Learning

Abstract

HighlightsA fungal spore dataset with microscopic images was established.A fungal spore detector, FSNet, based on deep learning was proposed to automatically recognize and count fungal spores in microscopic images.A framework based on a CNN for feature extraction was designed to enrich feature maps.A loss was formulated to decrease the number of missed detections due to clustered spores.Abstract. The most common cause of spoiled grain in storage is the growth of fungi on the grain. In China, the level of fungal infection is indicated by the quantity and the categories of fungal spores in grain samples. In most Chinese grain depots, the recognition and counting of fungal spores in grain samples are conducted with microscopes, which are mainly operated manually by professionals. This method is time-consuming, laborious and error-prone. In this article, a fungal spore detector, named FSNet, was proposed to automatically recognize and count fungal spores in microscopic images. To enrich the features in feature maps, a multi-layer fusion structure based on convolutional neural network for feature extraction was designed using deep, intermediate and shallow layers. Moreover, the detection performance of FSNet was further improved by a designed loss for clustered spores, an anchor optimization and region sampling strategy. Our method achieved 91.6% mean average precision [intersection-over-union (IoU) > 0.6]. The experimental results indicated that our method was capable of recognizing and counting fungal spores in microscopic images, and it is very effective for the early detection of fungal infection. Keywords: Fungal spore detection, Deep learning, Microscopic images.