  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.
