An Optoelectronic Sensorring to Detect Arthropods of Significantly Different Body Sizes Arthropods, including pollinators and pests, have high positive and negative impacts on human wellbeing and the economy, and there is an increasing need to monitor their activity and population growth. The monitoring of arthropod species is a timeconsuming and financially demanding process. Automatic detection can be a solution to this problem. Here, we describe the setup and operation mechanism of an infrared optoelectronic sensorring, which can be used for both small and large arthropods. The sensorring consists of 16 infrared (IR) photodiodes along a semicircle in front of an infrared LED. Using 3D printing, we constructed two types of sensorring: one with a wider sensing field for detection of large arthropods (flying, crawling, surfaceliving) in the size range of 2xe2x80x9335 mm; and another one with a narrower sensing field for soil microarthropods in the size range of 0.1xe2x80x932 mm. We examined the detection accuracy and reliability of the two types of sensorring in the laboratory by using particles, and dead and living arthropods at two different sensitivity levels. For the wider sensorring, the 95% detectability level was reached with grain particles of 0.9 mm size. This result allowed us to detect all of the macroarthropods that were applied in the tests and that might be encountered in pest management. In the case of living microarthropods with different colors and shapes, when we used the narrower sensorring, we achieved the 95% detectability level at 1.1 mm, 0.9 mm, and 0.5 mm in the cases of F. candida, H. nitidus, and H. aculeifer, respectively. The unique potential of arthropoddetecting sensors lies in their realtime measurement system; the data are automatically forwarded to the server, and the enduser receives pest abundance data daily or even immediately. This technological innovation will allow us to make pest management more effective.

Fusing multiscale contextaware information representation for automatic infield pest detection and recognition Abstract   Automatic infield pest detection and recognition using mobile vision technique is a hot topic in modern intelligent agriculture, but suffers from serious challenges including complexity of wild environment, detection of tiny size pest and classification of multiple classes of pests. While recent deep learning based mobile vision techniques have shown some success in overcoming above issues, one key problem is that towards largescale multiple species of pest data, imbalanced classes significantly reduce their detection and recognition accuracy. In this paper, we propose a novel twostages mobile vision based cascading pest detection approach (DeepPest) towards largescale multiple species of pest data. This approach firstly extracts multiscale contextual information of the images as prior knowledge to build up a contextaware attention network for initial classification of pest images into crop categories. Then, a multiprojection pest detection model (MDM) is proposed and trained by croprelated pest images. The role of MDM can combine pest contextual information from lowlevel convolutional layers with these in highlevel convolutional layers for generating the superresolved feature. Finally, we utilize the attention mechanism and data augmentation to improve the effectiveness of infield pest detection. We evaluate our method on our newly established largescale dataset InField Pest in Food Crop (IPFC) and sufficient experimental results show that DeepPest proposed in this paper outperforms stateoftheart object detection methods in detecting infield pest.