BOLLWM: A real-world dataset for bollworm pest monitoring from cotton fields in India
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Abstract (arXiv)
This paper presents a dataset of agricultural pest images captured over five years by thousands of small holder farmers and farming extension workers across India. The dataset has been used to support a mobile application that relies on artificial intelligence to assist farmers with pest management decisions. Creation came from a mix of organized data collection, and from mobile application usage that was less controlled. This makes the dataset unique within the pest detection community, exhibiting a number of characteristics that place it closer to other non-agricultural objected detection datasets. This not only makes the dataset applicable to future pest management applications, it opens the door for a wide variety of other research agendas.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 40 references that are in the map. First posted on arXiv 3 Apr 2023.
Builds on
7 of 40 references.
References · 7
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2017Drone-based Object Counting by Spatially Regularized Regional Proposal Networkcited by 156
- 2019Precise Detection in Densely Packed Scenescited by 63
- 2016Automatic Moth Detection from Trap Images for Pest Managementcited by 10
- 2021Counting and Locating High-Density Objects Using Convolutional Neural Networkcited by 3
- 2018Support Vector Machine (SVM) Recognition Approach adapted to Individual and Touching Moths Counting in Trap Imagescited by 2
- 2022A Case for Rejection in Low Resource ML Deploymentcited by 2
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Citing papers per year: 2023: 1.
1 paper.