# RAFA-Net: Region Attention Network For Food Items And Agricultural Stress Recognition

arXiv: https://arxiv.org/abs/2410.12718 · HTML: https://mlsanity.com/paper/2410.12718/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 64 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Scaling Local Self-Attention for Parameter Efficient Visual Backbones](https://mlsanity.com/paper/2103.12731/) (2021)
- [P2T: Pyramid Pooling Transformer for Scene Understanding](https://mlsanity.com/paper/2106.12011/) (2021)
- [PlantDoc: A Dataset for Visual Plant Disease Detection](https://mlsanity.com/paper/1911.10317/) (2019)
- [Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding](https://mlsanity.com/paper/2311.15206/) (2023)
- [Wide-Slice Residual Networks for Food Recognition](https://mlsanity.com/paper/1612.06543/) (2016)
- [LeafGAN: An Effective Data Augmentation Method for Practical Plant Disease Diagnosis](https://mlsanity.com/paper/2002.10100/) (2020)
- [Pay attention to the activations: a modular attention mechanism for fine-grained image recognition](https://mlsanity.com/paper/1907.13075/) (2019)
- [Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition](https://mlsanity.com/paper/2110.12183/) (2021)
- [An Efficient Insect Pest Classification Using Multiple Convolutional Neural Network Based Models](https://mlsanity.com/paper/2107.12189/) (2021)
- [Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis](https://mlsanity.com/paper/2308.00323/) (2023)
- [Learn More for Food Recognition via Progressive Self-Distillation](https://mlsanity.com/paper/2303.05073/) (2023)

## Built on by (most cited first, 3 of 3)

- [Soybean Disease Detection via Interpretable Hybrid CNN-GNN: Integrating MobileNetV2 and GraphSAGE with Cross-Modal Attention](https://mlsanity.com/paper/2503.01284/) (2025)
- [Women Sport Actions Dataset for Visual Classification Using Small Scale Training Data](https://mlsanity.com/paper/2507.10969/) (2025)
- [CLAP Convolutional Lightweight Autoencoder for Plant Disease Classification](https://mlsanity.com/paper/2602.18833/) (2026)

## Abstract (arXiv)

Deep Convolutional Neural Networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing of region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes using multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modelling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pair aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channel-wise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed Region Attention network for Food items and Agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracies of RAFA-Net are 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively; implying RAFA-Net's generalization capability.