# Automatic estimation of heading date of paddy rice using deep learning

## TL;DR

This paper presents a deep learning-based method to accurately estimate the heading date of paddy rice from ground-level RGB images, significantly improving accuracy and versatility over previous approaches.

## Contribution

It introduces a CNN-based pipeline for detecting flowering panicles and estimating heading date from images, enhancing accuracy and generalization.

## Key findings

- Mean absolute error in heading date estimation is less than 1 day.
- Method outperforms previous work on the same dataset.
- Applicable to multiple rice varieties and time series data.

## Abstract

Accurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also plays a vital role in determining grain yield for research experiments. Visual examination of the crop is laborious and time consuming. Therefore, quick and precise estimation of heading date of paddy rice is highly essential. In this work, we propose a simple pipeline to detect regions containing flowering panicles from ground level RGB images of paddy rice. Given a fixed region size for an image, the number of regions containing flowering panicles is directly proportional to the number of flowering panicles present. Consequently, we use the flowering panicle region counts to estimate the heading date of the crop. The method is based on image classification using Convolutional Neural Networks (CNNs). We evaluated the performance of our algorithm on five time series image sequences of three different varieties of rice crops. When compared to the previous work on this dataset, the accuracy and general versatility of the method has been improved and heading date has been estimated with a mean absolute error of less than 1 day.