# Data-Centric Digital Agriculture: A Perspective

arXiv: https://arxiv.org/abs/2312.03437 · HTML: https://mlsanity.com/paper/2312.03437/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 136 references that are in the map.
- First posted on arXiv 6 Dec 2023.

## Builds on (most cited first, 30 of 32)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Sanity Checks for Saliency Maps](https://mlsanity.com/paper/1810.03292/) (2018)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Bayesian Deep Learning and a Probabilistic Perspective of Generalization](https://mlsanity.com/paper/2002.08791/) (2020)
- [Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples](https://mlsanity.com/paper/1711.09325/) (2017)
- [Understanding the Role of Individual Units in a Deep Neural Network](https://mlsanity.com/paper/2009.05041/) (2020)
- [A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle](https://mlsanity.com/paper/1901.10002/) (2019)
- [Understanding Measures of Uncertainty for Adversarial Example Detection](https://mlsanity.com/paper/1803.08533/) (2018)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Making deep neural networks right for the right scientific reasons by interacting with their explanations](https://mlsanity.com/paper/2001.05371/) (2020)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Automated Data Slicing for Model Validation:A Big data - AI Integration Approach](https://mlsanity.com/paper/1807.06068/) (2018)
- [Predicting into unknown space? Estimating the area of applicability of spatial prediction models](https://mlsanity.com/paper/2005.07939/) (2020)
- [Unsupervised Selective Labeling for More Effective Semi-Supervised Learning](https://mlsanity.com/paper/2110.03006/) (2021)
- [On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration](https://mlsanity.com/paper/1912.07458/) (2019)
- [Efficient Machine Learning for Big Data: A Review](https://mlsanity.com/paper/1503.05296/) (2015)
- [Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming](https://mlsanity.com/paper/1806.03412/) (2018)
- [Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production Systems](https://mlsanity.com/paper/2110.04960/) (2021)
- [GrowliFlower: An image time series dataset for GROWth analysis of cauLIFLOWER](https://mlsanity.com/paper/2204.00294/) (2022)
- [Extended Agriculture-Vision: An Extension of a Large Aerial Image Dataset for Agricultural Pattern Analysis](https://mlsanity.com/paper/2303.02460/) (2023)
- [Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution](https://mlsanity.com/paper/1911.03098/) (2019)
- [Temporal Prediction and Evaluation of Brassica Growth in the Field using Conditional Generative Adversarial Networks](https://mlsanity.com/paper/2105.07789/) (2021)
- [Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation](https://mlsanity.com/paper/2203.01642/) (2022)
- [Towards Autonomous Visual Navigation in Arable Fields](https://mlsanity.com/paper/2109.11936/) (2021)
- [The Power of Transfer Learning in Agricultural Applications: AgriNet](https://mlsanity.com/paper/2207.03881/) (2022)
- [Agricultural Plant Cataloging and Establishment of a Data Framework from UAV-based Crop Images by Computer Vision](https://mlsanity.com/paper/2201.02885/) (2022)

## Built on by (most cited first, 5 of 5)

- [Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery](https://mlsanity.com/paper/2502.18320/) (2025)
- [Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutions](https://mlsanity.com/paper/2510.16207/) (2025)
- [YieldSAT: A Multimodal Benchmark Dataset for High-Resolution Crop Yield Prediction](https://mlsanity.com/paper/2604.00940/) (2026)
- [Predictive Modeling, Pattern Recognition, and Spatiotemporal Representations of Plant Growth in Simulated and Controlled Environments: A Comprehensive Review](https://mlsanity.com/paper/2412.10538/) (2024)
- [AgrI Challenge: A Data-Centric AI Competition for Cross-Team Validation in Agricultural Vision](https://mlsanity.com/paper/2603.07356/) (2026)

## Abstract (arXiv)

In response to the increasing global demand for food, feed, fiber, and fuel, digital agriculture is rapidly evolving to meet these demands while reducing environmental impact. This evolution involves incorporating data science, machine learning, sensor technologies, robotics, and new management strategies to establish a more sustainable agricultural framework. So far, machine learning research in digital agriculture has predominantly focused on model-centric approaches, focusing on model design and evaluation. These efforts aim to optimize model accuracy and efficiency, often treating data as a static benchmark. Despite the availability of agricultural data and methodological advancements, a saturation point has been reached, with many established machine learning methods achieving comparable levels of accuracy and facing similar limitations. To fully realize the potential of digital agriculture, it is crucial to have a comprehensive understanding of the role of data in the field and to adopt data-centric machine learning. This involves developing strategies to acquire and curate valuable data and implementing effective learning and evaluation strategies that utilize the intrinsic value of data. This approach has the potential to create accurate, generalizable, and adaptable machine learning methods that effectively and sustainably address agricultural tasks such as yield prediction, weed detection, and early disease identification