Image-based leaf SPAD value and chlorophyll measurement using a mobile phone: enabling accessible and sustainable crop management

Abstract

Abstract Purpose Leaf chlorophyll content is a critical indicator of plant health. On-farm monitoring is limited by the high cost and accessibility of specialised meters and laboratory assays. This study evaluates a smartphone-based imaging method (PhotoFolia) as a practical, low-cost alternative for accurately estimating leaf SPAD (Soil and Plant Analysis Development) values and chlorophyll content. Methods Image-based estimates from the PhotoFolia mobile application were validated against standard laboratory assays and SPAD-502+ meter readings. The validation was conducted across four commercial rice varieties cultivated in Thailand. Results The smartphone imaging method predicted SPAD values with a mean absolute error (MAE) of 1.2 units and chlorophyll concentrations with a mean absolute percentage error (MAPE) of 7.2% relative to laboratory benchmarks. Conclusion With SPAD error approaching the industry ±1 unit standard and chlorophyll estimation remaining below a 10% relative error threshold, this approach demonstrates that ordinary mobile phones can serve as highly accessible, cost-effective tools for routine on-farm crop monitoring, eliminating the need for dedicated hardware. Highlights Novel low-cost approach for chlorophyll assay and SPAD-value measurement using standard mobile phone. Achieves accuracy comparable to commercial tools. Eliminates need for specialised sensors or laboratory equipment. Impact This study demonstrates that mobile phone-based image analysis can accurately estimate leaf SPAD and chlorophyll levels in rice under ambient lighting conditions, offering a low-cost, accessible tool for monitoring plant health.

Plant phenotyping relevance

スマートフォン画像から葉のSPAD値とクロロフィル量を推定する手法を開発・標準測定と検証しており、植物表現型取得が中心である。

Code and data availability

The supplied blocks describe a rice leaf chlorophyll study comparing spectrophotometry, SPAD-502+, and the PhotoFolia mobile app, but contain no public phenotype dataset, leaf image repository, author analysis code, or trained model with an authors' public URL. The only availability statement is a generic supplementary

No evidence-backed public reproduction asset is currently recorded.