# A Robust Illumination-Invariant Camera System for Agricultural Applications

arXiv: https://arxiv.org/abs/2101.02190 · HTML: https://mlsanity.com/paper/2101.02190/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 6 Jan 2021.

## Builds on (most cited first, 10 of 10)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Using Deep Learning for Image-Based Plant Disease Detection](https://mlsanity.com/paper/1604.03169/) (2016)
- [Deep learning in agriculture: A survey](https://mlsanity.com/paper/1807.11809/) (2018)
- [Deep Fruit Detection in Orchards](https://mlsanity.com/paper/1610.03677/) (2016)
- [DeepApple: Deep Learning-based Apple Detection using a Suppression Mask R-CNN](https://mlsanity.com/paper/2010.09870/) (2020)

## Built on by (most cited first, 11 of 11)

- [Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection](https://mlsanity.com/paper/2108.13344/) (2021)
- [Assessing The Performance of YOLOv5 Algorithm for Detecting Volunteer Cotton Plants in Corn Fields at Three Different Growth Stages](https://mlsanity.com/paper/2208.00519/) (2022)
- [3D Reconstruction-Based Seed Counting of Sorghum Panicles for Agricultural Inspection](https://mlsanity.com/paper/2211.07748/) (2022)
- [Push Past Green: Learning to Look Behind Plant Foliage by Moving It](https://mlsanity.com/paper/2307.03175/) (2023)
- [3D Skeletonization of Complex Grapevines for Robotic Pruning](https://mlsanity.com/paper/2307.11706/) (2023)
- [Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision](https://mlsanity.com/paper/2212.01506/) (2022)
- [Occlusion Reasoning for Skeleton Extraction of Self-Occluded Tree Canopies](https://mlsanity.com/paper/2301.08387/) (2023)
- [AgRegNet: A Deep Regression Network for Flower and Fruit Density Estimation, Localization, and Counting in Orchards](https://mlsanity.com/paper/2409.17400/) (2024)
- [Transformer-Based Spatio-Temporal Association of Apple Fruitlets](https://mlsanity.com/paper/2503.03200/) (2025)
- [A workflow for segmenting soil and plant X-ray CT images with deep learning in Googles Colaboratory](https://mlsanity.com/paper/2203.09674/) (2022)
- [T-REX: Vision-Based System for Autonomous Leaf Detection and Grasp Estimation](https://mlsanity.com/paper/2505.01654/) (2025)

## Abstract (arXiv)

Object detection and semantic segmentation are two of the most widely adopted deep learning algorithms in agricultural applications. One of the major sources of variability in image quality acquired in the outdoors for such tasks is changing lighting condition that can alter the appearance of the objects or the contents of the entire image. While transfer learning and data augmentation to some extent reduce the need for large amount of data to train deep neural networks, the large variety of cultivars and the lack of shared datasets in agriculture makes wide-scale field deployments difficult. In this paper, we present a high throughput robust active lighting-based camera system that generates consistent images in all lighting conditions. We detail experiments that show the consistency in images quality leading to relatively fewer images to train deep neural networks for the task of object detection. We further present results from field experiment under extreme lighting conditions where images without active lighting significantly lack to provide consistent results. The experimental results show that on average, deep nets for object detection trained on consistent data required nearly four times less data to achieve similar level of accuracy. This proposed work could potentially provide pragmatic solutions to computer vision needs in agriculture.