# BOLLWM: A real-world dataset for bollworm pest monitoring from cotton fields in India

arXiv: https://arxiv.org/abs/2304.00763 · HTML: https://mlsanity.com/paper/2304.00763/

## Key facts

- Cited by 1 papers 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 (most cited first, 7 of 7)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [Precise Detection in Densely Packed Scenes](https://mlsanity.com/paper/1904.00853/) (2019)
- [Automatic Moth Detection from Trap Images for Pest Management](https://mlsanity.com/paper/1602.07383/) (2016)
- [Counting and Locating High-Density Objects Using Convolutional Neural Network](https://mlsanity.com/paper/2102.04366/) (2021)
- [Support Vector Machine (SVM) Recognition Approach adapted to Individual and Touching Moths Counting in Trap Images](https://mlsanity.com/paper/1809.06663/) (2018)
- [A Case for Rejection in Low Resource ML Deployment](https://mlsanity.com/paper/2208.06359/) (2022)

## Built on by (most cited first, 1 of 1)

- [Maintaining User Trust Through Multistage Uncertainty Aware Inference](https://mlsanity.com/paper/2402.00015/) (2023)

## 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.