# Localized Data Work as a Precondition for Data-Centric ML: A Case Study of Full Lifecycle Crop Disease Identification in Ghana

arXiv: https://arxiv.org/abs/2307.01767 · HTML: https://mlsanity.com/paper/2307.01767/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 4 Jul 2023.

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

- [Data Models for Dataset Drift Controls in Machine Learning With Optical Images](https://mlsanity.com/paper/2211.02578/) (2022)
- [MakeSense: An IoT Testbed for Social Research of Indoor Activities](https://mlsanity.com/paper/1908.03380/) (2019)
- [Cashew dataset generation using augmentation and RaLSGAN and a transfer learning based tinyML approach towards disease detection](https://mlsanity.com/paper/2304.08766/) (2023)

## Built on by (most cited first, 1 of 1)

- [DMLR: Data-centric Machine Learning Research -- Past, Present and Future](https://mlsanity.com/paper/2311.13028/) (2023)

## Abstract (arXiv)

The Ghana Cashew Disease Identification with Artificial Intelligence (CADI AI) project demonstrates the importance of sound data work as a precondition for the delivery of useful, localized datacentric solutions for public good tasks such as agricultural productivity and food security. Drone collected data and machine learning are utilized to determine crop stressors. Data, model and the final app are developed jointly and made available to local farmers via a desktop application.