Remote monitoring of Cydia pomonella adults among an assemblage of nontargets in sex pheromone-kairomone-baited smart traps

Potential of an Attractive High-Rate Navel Orangeworm (Lepidoptera: Pyralidae) Pheromone Dispenser for Mating Disruption or for Monitoring

Mating disruption is used to manage Amyelois transitella (Walker) (Lepidoptera: Pyralidae). Current mating disruption uses a single compound, but at least one other is necessary for source contact in laboratory assays and efficient capture in traps in the field. We conducted experiments to examine disruption of sexual communication and attraction of males to these high-rate dispensers with either one or both compounds. Trap suppression experiments compared males captured in pheromone traps in orchard blocks treated at a low, medium, or high number of dispensers per ha, and a trapping study used the dispensers as attractants in sticky traps to compare their relative attractiveness. The 2-compound dispensers suppressed male capture in pheromone traps more effectively than the 1-compound dispensers at the intermediate density. The trapping study found that both dispensers captured fewer males than a monitoring lure in the absence of mating disruption but more males in the presence of mating disruption. These data provide additional support for a hybrid mechanism of mating disruption for this species and indicate that the use of the more attractive 2-compound dispensers could make mating disruption more effective at the lowest dispenser density currently used. Alternatively, a lure base on the 2-compound dispenser could provide equally effective monitoring in the presence of mating disruption.

Automatic Detection of Moths (Lepidoptera) with a Funnel Trap Prototype

Monitoring insect populations is essential to optimise pest control with the correct protection timing and the avoidance of unnecessary insecticide use. Modern real-time monitoring practices use automatic insect traps, which are expected to be able to estimate the population sizes of pest animals with high species specificity. There are many solutions to overcome this challenge; however, there are only a few data that consider their accuracy under field conditions. This study presents an opto-electronic device prototype (ZooLog VARL) developed by us. A pilot field study evaluated the precision and accuracy of the data filtering using an artificial neural network(ANN) and the detection accuracy of the new probes. The prototype comprises a funnel trap, sensor-ring, and data communication system. The main modification of the trap was a blow-off device that prevented the escape of flying insects from the funnel. These new prototypes were tested in the field during the summer and autumn of 2018, detecting the daily and monthly flight of six moth species (Agrotis segetum, Autographa gamma, Helicoverpa armigera, Cameraria ohridella, Grapholita funebrana, Grapholita molesta). The accuracy of ANN was always higher than 60%. In the case of species with larger body sizes, it reached 90%. The detection accuracy ranged from 84% to 92% on average. These probes detected the real-time catches of the moth species. Therefore, weekly and daily patterns of moth flight activity periods could be compared and displayed for the different species. This device solved the problem of multiple counting and gained a high detection accuracy in target species cases. ZooLog VARL probes provide the real-time, time-series data sets of each monitored pest species. Further evaluation of the catching efficiency of the probes is needed. However, the prototype allows us to follow and model pest dynamics and may make more precise forecasts of population outbreaks.

Infestation of the gall midge Dasineura oleae provides first evidence of induced plant volatiles in olive leaves

In this study, we present the first characterization of herbivore-induced plant volatiles (HIPVs) released from infested olive leaves. The gall midge Dasineura oleae is a specific pest of Olea europaea and endemic of the Mediterranean Basin, an area in which severe outbreaks currently occurred. Little is known about the damage caused by the pest and the relationship with its host. Since gall formation and larval feeding activity may lead to the release of specific plant volatile compounds, we investigated the volatile profiles emitted from infested plants compared with healthy plants under both laboratory and field conditions. Additionally, the volatiles emitted from mechanically damaged plants were considered. A blend of 12 volatiles was emitted from olive trees infested by D. oleae. Of these, β-copaene, β-ocimene, cosmene, unknown 1 and unknown 3 were found to be exclusively emitted in infested plants. The emission of germacrene-D, (E,E)-α-farnesene, and (Z,E)-α-farnesene, α-copaene, (E)-4,8-dimethylnona-1,3,7-triene, (E)-β-guaiene and heptadecane significantly increased in infested trees. Linalool, β-copaen-4-α-ol, β-bourbonene, β-cubebene, β-elemene, β-copaene and δ-amorphene were found only in the field trial and showed differences depending on the level of infestation and the plant stage. (Z)-3-Hexenol, (E)-4-oxohen-2-enal, and 2-(2-butoxyethoxy)-ethanol, were exclusively emitted from the leaves after mechanical damage. In a field trial in Italy, we also demonstrated spring synchronization between adults of D. oleae and O. europaea trees. Analyses of morphoanatomical malformations of gall leaves showed that tissue alterations occur at the spongy parenchyma causing an increase of the leaf blade thickness. We speculate that tissue alterations may lead to HIPV release, in turn potentially attracting D. oleae natural enemies.

Diseases Detection of Occlusion and Overlapping Tomato Leaves Based on Deep Learning

Background: In view of the existence of light shadow, branches occlusion, and leaves overlapping conditions in the real natural environment, problems such as slow detection speed, low detection accuracy, high missed detection rate, and poor robustness in plant diseases and pests detection technology arise. Results: Based on YOLOv3-tiny network architecture, to reduce layer-by-layer loss of information during network transmission, and to learn from the idea of inverse-residual block, this study proposes a YOLOv3-tiny-IRB algorithm to optimize its feature extraction network, improve the gradient disappearance phenomenon during network deepening, avoid feature information loss, and realize network multilayer feature multiplexing and fusion. The network is trained by the methods of expanding datasets and multiscale strategies to obtain the optimal weight model. Conclusion: The experimental results show that when the method is tested on the self-built tomato diseases and pests dataset, and while ensuring the detection speed (206 frame rate per second), the mean Average precision (mAP) under three conditions: (a) deep separation, (b) debris occlusion, and (c) leaves overlapping are 98.3, 92.1, and 90.2%, respectively. Compared with the current mainstream object detection methods, the proposed method improves the detection accuracy of tomato diseases and pests under conditions of occlusion and overlapping in real natural environment.