# Weakly Supervised Learning Guided by Activation Mapping Applied to a Novel Citrus Pest Benchmark

arXiv: https://arxiv.org/abs/2004.11252 · HTML: https://mlsanity.com/paper/2004.11252/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding](https://mlsanity.com/paper/2311.15206/) (2023)
- [Label-Efficient Learning in Agriculture: A Comprehensive Review](https://mlsanity.com/paper/2305.14691/) (2023)
- [AGI for Agriculture](https://mlsanity.com/paper/2304.06136/) (2023)
- [Insect-Foundation: A Foundation Model and Large Multimodal Dataset for Vision-Language Insect Understanding](https://mlsanity.com/paper/2502.09906/) (2025)
- [A Survey of Computer Vision Technologies In Urban and Controlled-environment Agriculture](https://mlsanity.com/paper/2210.11318/) (2022)
- [High performing ensemble of convolutional neural networks for insect pest image detection](https://mlsanity.com/paper/2108.12539/) (2021)
- [Weakly Supervised Attention-based Models Using Activation Maps for Citrus Mite and Insect Pest Classification](https://mlsanity.com/paper/2110.00881/) (2021)
- [Self-supervised transformer-based pre-training method with General Plant Infection dataset](https://mlsanity.com/paper/2407.14911/) (2024)
- [Investigation to answer three key questions concerning plant pest identification and development of a practical identification framework](https://mlsanity.com/paper/2407.18000/) (2024)

## Abstract (arXiv)

Pests and diseases are relevant factors for production losses in agriculture and, therefore, promote a huge investment in the prevention and detection of its causative agents. In many countries, Integrated Pest Management is the most widely used process to prevent and mitigate the damages caused by pests and diseases in citrus crops. However, its results are credited by humans who visually inspect the orchards in order to identify the disease symptoms, insects and mite pests. In this context, we design a weakly supervised learning process guided by saliency maps to automatically select regions of interest in the images, significantly reducing the annotation task. In addition, we create a large citrus pest benchmark composed of positive samples (six classes of mite species) and negative samples. Experiments conducted on two large datasets demonstrate that our results are very promising for the problem of pest and disease classification in the agriculture field.