Screen Mesh Size for Exclusion of Diaphorina citri (Hemiptera: Liviidae) in Citrus Production.

Huanglongbing is a citrus disease that reduces yield, crop quality, and eventually causes tree mortality. The putative causal agent, Candidatus Liberibacter asiaticus (Rhizobiales: Rhizobiaceae), is vectored by the Asian citrus psyllid, Diaphorina citri Kuwayama. Disease management is largely through vector control, but the insect is developing pesticide resistance. A nonchemical approach to vector management is to grow citrus under screen cages either as bags over individual trees or enclosures spanning many acres. The enclosing screen reduces wind, alters temperature relative to ambient, and excludes a variety of pests that are too large to pass through the screen. Here we evaluated the potential of six screens to exclude D. citri. We conclude that screens with rectangular openings need to limit the short side to no more than 384.3 µm with a SD of 36.9 µm (40 mesh) to prevent psyllids from passing through the screen. The long side can be at least 833 µm, but the efficacy of screens exceeding this value should be tested before using in the field.

Chemical crystal identification with deep learning machine vision.

OBJECTIVE: This study was carried out with the purpose of testing the ability of deep learning machine vision to identify microscopic objects and geometries found in chemical crystal structures. RESULTS: A database of 6994 images taken with a light microscope showing microscopic crystal details of selected chemical compounds along with 180 images of an unknown chemical was created to train and test, respectively the deep learning models. The models used were GoogLeNet (22 layers deep network) and VGG-16 (16 layers deep network), based on the Caffe framework (University of California, Berkeley, CA) of the DIGITS platform (NVIDIA Corporation, Santa Clara, CA). The two models were successfully trained with the images, having validation accuracy values of 97.38% and 99.65% respectively. Finally, both models were able to correctly identify the unknown chemical sample with a high probability score of 93.34% (GoogLeNet) and 99.41% (VGG-16). The positive results found in this study can be further applied to other unknown sample identification tasks using light microscopy coupled with deep learning machine vision.