Desai Sai Vikas, Balasubramanian Vineeth N, Fukatsu Tokihiro, Ninomiya Seishi, Guo Wei
1Department of Computer Science and Engineering, Indian Institute of Technology - Hyderabad, Kandi, Hyderabad, 502285 India.
3Institute of Agricultural Machinery, National Agriculture and Food Research Organization, 1-31-1 Kannondai, Tsukuba, Ibaraki 3050856 Japan.
Plant Methods. 2019 Jul 13;15:76. doi: 10.1186/s13007-019-0457-1. eCollection 2019.
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. 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.
An efficient heading date estimation method has been described for rice crops using time series RGB images of crop under natural field conditions. This study demonstrated that our method can reliably be used as a replacement of manual observation to detect the heading date of rice crops.
准确估计水稻抽穗期对育种者了解不同作物品种在特定地点的适应性有很大帮助。抽穗期在确定研究实验的谷物产量方面也起着至关重要的作用。通过肉眼观察作物既费力又耗时。因此，快速精确地估计水稻抽穗期非常必要。
在这项工作中，我们提出了一种简单的流程，用于从水稻地面RGB图像中检测包含开花稻穗的区域。对于给定大小固定的图像区域，包含开花稻穗的区域数量与实际存在的开花稻穗数量成正比。因此，我们使用开花稻穗区域计数来估计作物的抽穗期。该方法基于使用卷积神经网络的图像分类。我们在三种不同水稻品种的五个时间序列图像序列上评估了算法的性能。与该数据集上的先前工作相比，该方法的准确性和通用性得到了提高，估计抽穗期的平均绝对误差小于1天。
利用自然田间条件下作物的时间序列RGB图像，描述了一种有效的水稻抽穗期估计方法。本研究表明，我们的方法可以可靠地替代人工观察来检测水稻作物的抽穗期。
