Development and Field Validation of a Smartphone-Based Web Application for Diagnosing Optimal Timing of Mid-Season Drainage in Rice Cultivation via Canopy Image-Derived Tiller Estimation

Abstract

In recent years, excessive tillering caused by high temperatures during early growth has contributed to rice quality deterioration in warm regions of Japan. Accurate determination of midseason drainage timing is essential but remains difficult due to year- and cultivar-dependent variability. In this study, we developed a smartphone-based web application that estimates rice tiller number from canopy images and diagnoses the optimal timing of midseason drainage by comparing estimated tiller numbers with cultivar-specific target values. The system operates entirely on a smartphone using HTML5 canvas-based pixel extraction, JavaScript computation, and Google Apps Script-based backend processing. Field experiments conducted in Chiba Prefecture using three rice cultivars showed a strong linear relationship between estimated and observed tiller numbers (R 2 = 0.9439). The root mean square error (RMSE) was 42.6 tillers m -2 , with a consistent negative bias (-34.6 tillers m -2 ), indicating systematic underestimation. Considering typical tiller increase rates near midseason drainage (12.0-24.3 tillers m -2 day -1 ), these errors correspond to approximately 1-3 days of growth progression, which is acceptable for timing-based decision-making. Although the system does not aim to provide precise absolute tiller counts, it reliably captures relative growth-stage dynamics and supports threshold-based diagnosis. The proposed approach enables rapid, on-site decision support using only a smartphone, contributing to labor-saving and improved water management in rice production.

Plant phenotyping relevance

スマートフォン画像からイネの分げつ数を推定する手法とWebアプリを開発し、圃場で精度検証しているため、植物表現型取得が研究の中心である。

Code and data availability

The paper's phenotype measurements (estimated/observed tiller numbers, canopy images) and analysis are not publicly available; the Data Availability Statement says data are available only on request from the corresponding author. No public code, image, or dataset deposit is mentioned.

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