# 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

## TL;DR

A smartphone app was developed to help rice farmers decide when to drain water mid-season by estimating tiller numbers from canopy images.

## Contribution

A novel smartphone-based web application was developed and validated for diagnosing optimal midseason drainage timing in rice cultivation.

## Key findings

- The app showed a strong linear relationship between estimated and observed tiller numbers (R2 = 0.9439).
- The system's errors correspond to approximately 1–3 days of growth progression, acceptable for timing decisions.
- The app supports threshold-based diagnosis of drainage timing, aiding water management and labor efficiency.

## 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 (R2 = 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.