Automatic Moth Detection from Trap Images for Pest Management
Monitoring the number of insect pests is a crucial component in pheromone-based pest management systems. In this paper, we propose an automatic detection pipeline based on deep learning for identifying and counting pests in images taken inside field traps. Applied to a commercial codling moth dataset, our method shows promising performance both qualitatively and quantitatively. Compared to previous attempts at pest detection, our approach uses no pest-specific engineering which enables it to adapt to other species and environments with minimal human effort. It is amenable to implementation on parallel hardware and therefore capable of deployment in settings where real-time performance is required.
Code (0)
등록된 구현이 없습니다.
Tasks
ManagementSimilar Papers 제목 키워드 기반
Accelerating Image-based Pest Detection on a Heterogeneous Multi-core Microcontroller
The codling moth pest poses a significant threat to global crop production, with potential losses of up to 80% in apple orchards. Special camera-based sensor nodes are deployed in the field to record and transmit images …
Support Vector Machine (SVM) Recognition Approach adapted to Individual and Touching Moths Counting in Trap Images
This paper aims at developing an automatic algorithm for moth recognition from trap images in real-world conditions. This method uses our previous work for detection [1] and introduces an adapted classification step. Mor…
ClassificationGeneral ClassificationAutomated Visual Monitoring of Nocturnal Insects with Light-based Camera Traps
Automatic camera-assisted monitoring of insects for abundance estimations is crucial to understand and counteract ongoing insect decline. In this paper, we present two datasets of nocturnal insects, especially moths as a…
Investigating generative neural-network models for building pest insect detectors in sticky trap images for the Peruvian horticulture
Pest insects are a problem in horticulture, so early detection is key for their control. Sticky traps are an inexpensive way to obtain insect samples in crops, but identifying them manually is a time-consuming task. Buil…
A machine learning pipeline for automated insect monitoring
Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosystem services on which human society depends. Data on insect abundance, however, re…
object-detectionObject Detection