Separate degree based Otsu and signed similarity driven level set for segmenting and counting anthrax spores Abstract   The statistical information of disease fungal spores is of great significance for precision agriculture. This work proposes a novel image segmentation method to automatically calculate the density and number of anthrax spores from microscopic images. Firstly, we prove that the widely used Otsu threshold has bias towards one class with larger variance, and the bias degree varies with the size difference of classes. The existing Otsu methods only consider the stable unbalanced distributions and are not effective when the data distributions are unbalanced and diverse. To address this problem, we propose a new evaluation index, named as Gaussian Separate Degree (GSD), to evaluate the image clustering results. Based on the index, a simple and effective thresholding method, called as GOtsu, is proposed to segment anthrax spore images. Secondly, we take advantage of the connectivity, shape and area features of anthrax spores to filter out interferences. Next, we propose a signed logarithmic Gaussian Mixture Model similarity (SLGS) based level set model to accurately evolve the spore contours, which can automatically adjust the evolution direction of contours, ensuring the correct evolution of contours. Lastly, the density and the number of spores can be calculated accurately. The experimental results demonstrate that our method achieves 93.0% mean IoU (intersectionoverunion of the calculated spore pixels and the ground truth pixels), and 1.5% overall error rate, which are higher than the existing methods.

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.