# Deep Learning and Machine Vision for Food Processing: A Survey

arXiv: https://arxiv.org/abs/2103.16106 · HTML: https://mlsanity.com/paper/2103.16106/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 142 references that are in the map.
- First posted on arXiv 30 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Automatic estimation of heading date of paddy rice using deep learning](https://mlsanity.com/paper/1906.07917/) (2019)

## Built on by (most cited first, 7 of 7)

- [FoodSAM: Any Food Segmentation](https://mlsanity.com/paper/2308.05938/) (2023)
- [Crop identification using deep learning on LUCAS crop cover photos](https://mlsanity.com/paper/2305.04994/) (2023)
- [A tomographic workflow to enable deep learning for X-ray based foreign object detection](https://mlsanity.com/paper/2201.12184/) (2022)
- [Online Nonparametric Supervised Learning for Massive Data](https://mlsanity.com/paper/2405.19486/) (2024)
- [A SAM based Tool for Semi-Automatic Food Annotation](https://mlsanity.com/paper/2410.19756/) (2024)
- [Hyperspectral Imaging for Identifying Foreign Objects on Pork Belly](https://mlsanity.com/paper/2503.16086/) (2025)
- [Artificial Intelligence in the Food Industry: Food Waste Estimation based on Computer Vision, a Brief Case Study in a University Dining Hall](https://mlsanity.com/paper/2507.14662/) (2025)

## Abstract (arXiv)

The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability. Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption. However, these processes are often labour-intensive. Nowadays, the development of machine vision can greatly assist researchers and industries in improving the efficiency of food processing. As a result, machine vision has been widely used in all aspects of food processing. At the same time, image processing is an important component of machine vision. Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. Subsequently, follow-up design in the machine vision system can address tasks such as food grading, detecting locations of defective spots or foreign objects, and removing impurities. In this paper, we provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing. We present the current approaches and challenges, and the future trends.