Participatory AI for inclusive crop improvement

Lasdun, Violet, Güereña, Davíd, Ortiz-Crespo, Berta, Mutuvi, Stephen and Selvaraj, Michael (2024) Participatory AI for inclusive crop improvement. Agricultural Systems, 220. ISSN 0308-521X

Text (FINAL Ag Systems Revisions) - Accepted Version Repository staff only until 24 July 2026. Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (2MB) | Request a copy

• Scopus publication
• Author

Abstract

CONTEXT: Crop breeding in the Global South faces a ‘phenotyping bottleneck’ due to reliance on manual visual phenotyping, which is both error-prone and challenging to scale across multiple environments, inhibiting selection of germplasm adapted to farmer production environments. This limitation impedes rapid varietal turnover, crucial for maintaining high yields and food security under climate change. Low adoption of improved varieties results from a top-down system in which farmers have been more passive recipients than active participants in varietal development. OBJECTIVE: A new suite of research at the Alliance of Bioversity and CIAT seeks to democratize crop breeding by leveraging mobile phenotyping technologies for high-quality, decentralized data collection. This approach aims to resolve the inherent limitations and inconsistencies in traditional visual phenotyping methods, allowing for more accurate and efficient crop assessment. In parallel, the research seeks to harness multimodal data on farmer preferences to better tailor variety development xzto meet specific production and consumption goals. METHODS: Novel mobile phenotyping tools were developed and field-tested on breeder stations in Colombia and Tanzania, and data from these trials were analyzed for quality and accuracy, and compared with traditional manual estimates and absolute ground truth data. Concurrently, Human-Centered Design (HCD) methods were applied to ensure the technology suits its context of use, and serves the nuanced requirements of breeders. RESULTS AND CONCLUSIONS: Computer vison (CV)-enabled mobile phenotyping achieved a significant reduction in scoring variation, attaining imagery-modeled trait accuracies with Pearson Correlation values between 0.88 and 0.95 with ground truth data, and reduced labor requirements with the ability to fully phenotype a breeder's plot (4 m × 3 m) in under a minute. With this technology, high-quality quantitative phenotyping data can be collected by anyone with a smartphone, expanding the potential to measure crop performance in decentralized on-farm environments and improving accuracy and speed of crop improvement on breeder stations. SIGNIFICANCE: Inclusive innovations in mobile phenotyping technologies and AI-supported data collection enable rapid, accurate trait assessment and actively involve farmers in variety selection, aligning breeding programs with local needs and preferences. These advancements offer a timely solution for accelerating varietal turnover to mitigate climate change impacts, while ensuring developed varieties are both high-performing and culturally relevant.

Item Type:	Article
Official URL:	https://www.sciencedirect.com/journal/agricultural...
Additional Information:	© 2024 Elsevier
Divisions:	LSE
Subjects:	G Geography. Anthropology. Recreation > G Geography (General) S Agriculture Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	02 Aug 2024 11:00
Last Modified:	16 Aug 2024 18:09
URI:	http://eprints.lse.ac.uk/id/eprint/124444

Actions (login required)

View Item