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In Brazil, the common earwig Doru luteipes (Scudder) (Dermaptera: Forficulidae) is considered an important biocontrol agent for the maize crop, consuming the fall armyworm Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae) eggs and caterpillars from 1st to the 3rd instar. Despite this, several aspects of the interaction between these species have not yet been studied. We aim to evaluate the non‐consumptive effects of earwigs on the oviposition of moths, the earwig's predatory preference between eggs and neonates and its functional response to S. frugiperda eggs. In no‐choice tests between plants with or without the presence of the predator, S. frugiperda moths deposited a smaller number of eggs on plants with risk of predation. In choice‐based tests, earwigs initially attacked newborn caterpillars, but preferred to feed on eggs. Males and females fed more on eggs with increasing supply density and consumption was adjusted to the type II functional response curve. D. luteipes males were more efficient predators than females when exposed to higher egg densities. These findings clarify aspects of the predatory role of D. luteipes on S. frugiperda that had not yet been addressed and suggest that the earwig has potential for impacting the colonization and population growth of S. frugiperda in maize crops, if conditions are favourable to its early arrival.
In Brazil, the common earwig Doru luteipes (Scudder) (Dermaptera: Forficulidae) is considered an important biocontrol agent for the maize crop, consuming the fall armyworm Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae) eggs and caterpillars from 1st to the 3rd instar. Despite this, several aspects of the interaction between these species have not yet been studied. We aim to evaluate the non‐consumptive effects of earwigs on the oviposition of moths, the earwig's predatory preference between eggs and neonates and its functional response to S. frugiperda eggs. In no‐choice tests between plants with or without the presence of the predator, S. frugiperda moths deposited a smaller number of eggs on plants with risk of predation. In choice‐based tests, earwigs initially attacked newborn caterpillars, but preferred to feed on eggs. Males and females fed more on eggs with increasing supply density and consumption was adjusted to the type II functional response curve. D. luteipes males were more efficient predators than females when exposed to higher egg densities. These findings clarify aspects of the predatory role of D. luteipes on S. frugiperda that had not yet been addressed and suggest that the earwig has potential for impacting the colonization and population growth of S. frugiperda in maize crops, if conditions are favourable to its early arrival.
The agriculture sectors, which account for approximately 50% of the worldwide economic production, are the fundamental cornerstone of each nation. The significance of precision agriculture cannot be understated in assessing crop conditions and identifying suitable treatments in response to diverse pest infestations. The conventional method of pest identification exhibits instability and yields subpar levels of forecast accuracy. Nevertheless, the monitoring techniques frequently exhibit invasiveness, require significant time and resources, and are susceptible to various biases. Numerous insect species can emit distinct sounds, which can be readily identified and recorded with minimal expense or exertion. Applying deep learning techniques enables the automated detection and classification of insect sounds derived from field recordings, hence facilitating the monitoring of biodiversity and the assessment of species distribution ranges. The current research introduces an innovative method for identifying and detecting pests through IoT-based computerized modules that employ an integrated deep-learning methodology using the dataset comprising audio recordings of insect sounds. This included techniques, the DTCDWT method, Blackman-Nuttall window, Savitzky-Golay filter, FFT, DFT, STFT, MFCC, BFCC, LFCC, acoustic detectors, and PID sensors. The proposed research integrated the MF-MDLNet to train, test, and validate data. 9,600 pest auditory sounds were examined to identify their unique characteristics and numerical properties. The recommended system designed and implemented the ultrasound generator, with a programmable frequency and control panel for preventing and controlling pests and a solar-charging system for supplying power to connected devices in the networks spanning large farming areas. The suggested approach attains an accuracy (99.82%), a sensitivity (99.94%), a specificity (99.86%), a recall (99.94%), an F1 score (99.89%), and a precision (99.96%). The findings of this study demonstrate a significant enhancement compared to previous scholarly investigations, including VGG 16, VOLOv5s, TSCNNA, YOLOv3, TrunkNet, DenseNet, and DCNN.
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