Chemical and Non-Chemical Options for Managing Twospotted Spider Mite, Western Tarnished Plant Bug and Other Arthropod Pests in Strawberries

Community composition of the entomopathogenic fungal genus Metarhizium in soils of tropical and temperate conventional and organic strawberry fields

Studies on community composition and population structure of entomopathogenic fungi are imperative to link ecosystem functions to conservation biological control. We studied the diversity and abundance of Metarhizium spp. from soil of conventionally and organically farmed strawberry crops and from the adjacent field margins in two different climatic zones: Brazil (tropical) and Denmark (temperate), using the same isolating methods. In Brazilian strawberry soil, Metarhizium robertsii (n = 129 isolates) was the most abundant species, followed by M. humberi (n = 16); M. anisopliae (n = 6); one new taxonomically unassigned lineage Metarhizium sp. indet. 5 (n = 4); M. pingshaense (n = 1) and M. brunneum (n = 1). In Denmark, species composition was very different, with M. brunneum (n = 33) being isolated most commonly, followed by M. flavoviride (n = 6) and M. pemphigi (n = 5), described for the first time in Denmark. In total, 17 haplotypes were determined based on MzFG543igs sequences, four representing Danish isolates and 13 representing Brazilian isolates. No overall difference between the two climatic regimes was detected regarding the abundance of Metarhizium spp. in the soil in strawberry fields and the field margins. However, we found a higher Shannon's diversity index in organically managed soils, confirming a more diverse Metarhizium community than in soils of conventionally managed agroecosystems in both countries. These findings contribute to the knowledge of the indigenous diversity of Metarhizium in agricultural field margins with the potential to contribute to pest regulation in strawberry cropping systems.

Agricultural Robot-Centered Recognition of Early-Developmental Pest Stage Based on Deep Learning: A Case Study on Fall Armyworm (Spodoptera frugiperda)

Accurately detecting early developmental stages of insect pests (larvae) from off-the-shelf stereo camera sensor data using deep learning holds several benefits for farmers, from simple robot configuration to early neutralization of this less agile but more disastrous stage. Machine vision technology has advanced from bulk spraying to precise dosage to directly rubbing on the infected crops. However, these solutions primarily focus on adult pests and post-infestation stages. This study suggested using a front-pointing red-green-blue (RGB) stereo camera mounted on a robot to identify pest larvae using deep learning. The camera feeds data into our deep-learning algorithms experimented on eight ImageNet pre-trained models. The combination of the insect classifier and the detector replicates the peripheral and foveal line-of-sight vision on our custom pest larvae dataset, respectively. This enables a trade-off between the robot's smooth operation and localization precision in the pest captured, as it first appeared in the farsighted section. Consequently, the nearsighted part utilizes our faster region-based convolutional neural network-based pest detector to localize precisely. Simulating the employed robot dynamics using CoppeliaSim and MATLAB/SIMULINK with the deep-learning toolbox demonstrated the excellent feasibility of the proposed system. Our deep-learning classifier and detector exhibited 99% and 0.84 accuracy and a mean average precision, respectively.

Expression Profiles of Digestive Genes in the Gut and Salivary Glands of Tarnished Plant Bug (Hemiptera: Miridae)

Host plant preference of agricultural pests may shift throughout the growing season, allowing the pests to persist on wild hosts when crops are not available. Lygus Hahn (Hemiptera: Miridae) bugs are severe pests of cotton during flowering and fruiting stages, but can persist on alternative crops, or on weed species. Diversity of digestive enzymes produced by salivary glands and gut tissues play a pivotal role in an organism's ability to utilize various food sources. Polyphagous insects produce an array of enzymes that can process carbohydrates, lipids, and proteins. In this study, the digestive enzyme repertoire of the tarnished plant bug, Lygus lineolaris (Palisot de Beauvois), was identified by high-throughput sequencing followed by cDNA cloning and sequencing. This study identified 87 digestive genes, including 30 polygalacturonases (PG), one β-galactosidase, three α-glucosidases, six β-glucosidases, 28 trypsin-like proteases, three serine proteases, one apyrase-like protease, one cysteine protease, 12 lipases, and two transcripts with low similarity to a xylanase A-like genes. RNA-Seq expression profiles of these digestive genes in adult tarnished plant bugs revealed that 57 and 12 genes were differentially expressed in the salivary gland and gut (≥5-fold, P ≤ 0.01), respectively. All polygalacturonase genes, most proteases, and two xylanase-like genes were differentially expressed in salivary glands, while most of the carbohydrate and lipid processing enzymes were differentially expressed in the gut. Seven of the proteases (KF208689, KF208697, KF208698, KF208699, KF208700, KF208701, and KF208702) were not detected in either the gut or salivary glands.