IntroductionEffective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes. MethodsTo tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference (SAHI), along with applying a detection model. HTL pioneers a multi-step knowledge transfer paradigm, harnessing intermediary in-domain datasets to facilitate model adaptation. Moreover, slicing-aided hyper inference subdivides images into overlapping patches, conducting independent object detection on each patch before merging outcomes for precise, comprehensive results. ResultsThe outcomes underscore the substantial improvement achievable in detection results by integrating a diverse and expansive in-domain dataset within the HTL method, complemented by the utilization of SAHI. DiscussionWe also present a hardware and software infrastructure for deploying such models for real-life applications. Our results can assist researchers and practitioners looking for solutions for insect-pest detection and quantification on yellow sticky traps.
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Chloroplast Genome Engineering: A Plausible Approach to Combat Chili Thrips and Other Agronomic Insect Pests of Crops
The world population’s growing demand for food is expected to increase dramatically by 2050. The agronomic productivity for food is severely affected due to biotic and abiotic constraints. At a global level, insect pests alone account for ~20% loss in crop yield every year. Deployment of noxious chemical pesticides to control insect pests always has a threatening effect on human health and environmental sustainability. Consequently, this necessitates for the establishment of innovative, environmentally friendly, cost-effective, and alternative means to mitigate insect pest management strategies. According to a recent study, using chloroplasts engineered with double-strand RNA (dsRNA) is novel successful combinatorial strategy deployed to effectively control the most vexing pest, the western flower thrips (WFT: Frankliniella occidentalis). Such biotechnological avenues allowed us to recapitulate the recent progress of research methods, such as RNAi, CRISPR/Cas, mini chromosomes, and RNA-binding proteins with plastid engineering for a plausible approach to effectively mitigate agronomic insect pests. We further discussed the significance of the maternal inheritance of the chloroplast, which is the major advantage of chloroplast genome engineering.
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- Award ID(s):
- 1658709
- PAR ID:
- 10567096
- Publisher / Repository:
- MDPI
- Date Published:
- Journal Name:
- Plants
- Volume:
- 12
- Issue:
- 19
- ISSN:
- 2223-7747
- Page Range / eLocation ID:
- 3448
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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