Effect of Hadda beetle, Epilachna vigintioctopunctata Fab. (Coleoptera: Coccinellidae) infestation on eggplant leaf (Solanum melongena L.) and bio-control potential of essential oil formulations

Effects of Delayed Mating on the Reproductive Performance of Henosepilachna vigintioctopunctata (F.) (Coleoptera: Coccinellidae)

Simple Summary

In many Asian countries, Henosepilachna vigintioctopunctata (F.), is seriously harmful to Solanaceae vegetables. With the popularization of green agriculture and the improvement in people’s living standards, biological pest control may become the mainstream. The artificial release of sex pheromones and other methods to delay insect mating, thus affecting population abundance, is an important part of biological control. We took H. vigintioctopunctata collected from Jingzhou, Hubei Province, China, back to the laboratory to establish an experimental population to study the effect of delayed mating on its reproductive behavior. The negative effects on reproduction and changes in population life table parameters, such as net reproductive rate, intrinsic and finite rates of increase, doubling time, and mean generation time, could be estimated by the treatment of delayed mating of males and females, which could be useful for providing important information for pest control in the future.

Abstract

Henosepilachna vigintioctopunctata (F.) is a serious pest of numerous solanaceous crops in many Asian countries. The purpose of this study was to clarify the effects of delayed mating on mating success, fecundity, fertility, pre-oviposition period, oviposition period, adult longevity, and population life table parameters (including net reproductive rate, intrinsic and finite rates of increase, doubling time, and mean generation time) of H. vigintioctopunctata. Beginning three days after emergence for both sexes, mating was delayed an additional 0, 2, 4, 6, or 8 days. We compared the data when mating was delayed for males only with the data when mating was similarly delayed for females only. Reproductive and life table parameters were calculated from the two data sets and compared. The results showed that the preoviposition and oviposition period of adults was significantly reduced by delayed mating, while the preoviposition period was not significantly different in adults mated at older ages. The mating success rate, fecundity, and proportion of hatching eggs decreased with increasing mating age. Longevity was not affected by the age at mating. Mating delay also affected the life table parameters of H. vigintioctopunctata, with a similar trend observed in the net reproductive rate and intrinsic and finite rates of increase, all of which decreased gradually as the number of delay days increased. The population doubling time increased with increases in mating age. The results also showed that delayed mating was an effective measure to consider in controlling H. vigintioctopunctata. It is hoped that our data will provide a scientific basis and contribute technical guidance for forecasting and integrated management of this pest.

Disease Classification in Eggplant Using Pre-trained VGG16 and MSVM

Currently, the application of deep learning in crop disease classification is one of the active areas of research for which an image dataset is required. Eggplant (Solanum melongena) is one of the important crops, but it is susceptible to serious diseases which hinder its production. Surprisingly, so far no dataset is available for the diseases in this crop. The unavailability of the dataset for these diseases motivated the authors to create a standard dataset in laboratory and field conditions for five major diseases. Pre-trained Visual Geometry Group 16 (VGG16) architecture has been used and the images have been converted to other color spaces namely Hue Saturation Value (HSV), YCbCr and grayscale for evaluation. Results show that the dataset created with RGB and YCbCr images in field condition was promising with a classification accuracy of 99.4%. The dataset also has been evaluated with other popular architectures and compared. In addition, VGG16 has been used as feature extractor from 8^th convolution layer and these features have been used for classifying diseases employing Multi-Class Support Vector Machine (MSVM). The analysis depicted an equivalent or in some cases produced better accuracy. Possible reasons for variation in interclass accuracy and future direction have been discussed.