Greenhouse crop production is growing throughout the world and early pest detection is of particular importance in terms of productivity and reduction of the use of pesticides. Conventional eye observation methods are nonefficient for large crops. Computer vision and recent advances in deep learning can play an important role in increasing the reliability and productivity. This paper presents the development and comparison of two different approaches for vision based automated pest detection and identification, using learning strategies. A solution that combines computer vision and machine learning is compared against a deep learning solution. The main focus of our work is on the selection of the best approach based on pest detection and identification accuracy. The inspection is focused on the most harmful pests on greenhouse tomato and pepper crops, Bemisia tabaci and Trialeurodes vaporariorum. A dataset with a huge number of infected tomato plants images was created to generate and evaluate machine learning and deep learning models. The results showed that the deep learning technique provides a better solution because (a) it achieves the disease detection and classification in one step, (b) gets better accuracy, (c) can distinguish better between Bemisia tabaci and Trialeurodes vaporariorum, and (d) allows balancing between speed and accuracy by choosing different models.

Background The ATP-binding cassette (ABC) transporter superfamily is comprised predominantly of proteins which directly utilize energy from ATP to move molecules across the plasma membrane. Although they have been the subject of frequent investigation across many taxa, arthropod ABCs have been less well studied. While the manual annotation of ABC transporters has been performed in many arthropods, there has so far been no systematic comparison of the superfamily within this order using the increasing number of sequenced genomes. Furthermore, functional work on these genes is limited. Results Here, we developed a standardized pipeline to annotate ABCs from predicted proteomes and used it to perform comparative genomics on ABC families across arthropod lineages. Using Kruskal-Wallis tests and the Computational Analysis of gene Family Evolution (CAFE), we were able to observe significant expansions of the ABC-B full transporters (P-glycoproteins) in Lepidoptera and the ABC-H transporters in Hemiptera. RNA-sequencing of epithelia tissues in the Lepidoptera Helicoverpa armigera showed that the 7 P-glycoprotein paralogues differ substantially in their tissue distribution, suggesting a spatial division of labor. It also seems that functional redundancy is a feature of these transporters as RNAi knockdown showed that most transporters are dispensable with the exception of the highly conserved gene Snu, which is probably due to its role in cuticular formation. Conclusions We have performed an annotation of the ABC superfamily across > 150 arthropod species for which good quality protein annotations exist. Our findings highlight specific expansions of ABC transporter families which suggest evolutionary adaptation. Future work will be able to use this analysis as a resource to provide a better understanding of the ABC superfamily in arthropods.

The intensive use of insecticides in global agricultural production has attracted much attention due to its many adverse effects on human health and the environment. In recent years, the utilization of nanotechnology has emerged as a tool to overcome these adverse effects. The aim of this work was to test different microparticles (zinc oxide (ZnO MPs) and silicon dioxide microparticles (SiO2 MPs)), and silver nanoparticles (Ag NPs) and to study their toxicity on a model organism, Tenebrio molitor. A comprehensive comparative study, which included more than a thousand mealworms divided into nine separate groups, was conducted. In addition to pure nano/microparticle solutions, the effect of particles mixed with the microalgae extract Chlamydomonas reinhardtii was also observed. Pure Ag NPs and SiO2 MPs resulted in larval mortality of more than 70% compared to that of pure ZnO MPs, in which the mortality rate was approximately 33%. A mixture of the algal extract with zinc oxide microparticles resulted in mortality that was double compared to that observed with pure ZnO MPs. In parallel, atomic absorption spectrometry (AAS) was used to determine the difference in the concentration of trace elements in the bodies of dead and live larvae.