Deep learning-based detection of aphid colonies on plants from a reconstructed Brassica image dataset

Plant pathogenic colony-forming aphids are serious pests of agricultural crops. Most aphid species develop colonies, which are several to many aphids that are functionally organized to extract food from plants. Early and efficient detection of aphid colonies enables the implementation of control measures to reduce crop damage. Aphid colonies exhibit different shapes, sizes, and numbers of individuals, and their distribution makes it hard to detect them correctly. This paper investigates machine learning-based aphid colony detection from imagery. To the best of our knowledge, it is the first study that uses artificial intelligence-based computing algorithms to detect aphid colonies from images. As such, no aphid colony image dataset is currently available publicly. To mitigate this, we have relabelled an existing insect and pest dataset and repurposed it as an aphid colony dataset (AphColDat). For labelling, first, we automatically identify the regions of interest for colonies based on the locations and distributions of aphids. A novel bounding box merging technique is proposed to generate regions potential colony boxes. These colony boxes are a collection of single or overlapping aphid boxes that co-locate together in a colony. Once the dataset is constructed, a convolutional neural network (CNN)-based binary classification algorithm is applied to the images to create AphColDat. This paper evaluates several object detecting deep learning models on the newly developed dataset. The results demonstrate mean average precisions (mAP) of 56.9%, 53.4%, 53.1%, and 48.7% respectively by Faster R-CNN, SSD, YOLOv3, and EfficientNet. In terms of average detection speed (computational time), SSD and EfficientNet are faster than Faster R-CNN and YOLOv3.