Abstract
The number of grains within a panicle is an important index for rice breeding. Counting manually is laborious and time-consuming and hardly meets the requirement of rapid breeding. It is necessary to develop an image-based method for automatic counting. However, general image processing methods cannot effectively extract the features of grains within a panicle, resulting in a large deviation. The convolutional neural network (CNN) is a powerful tool to analyze complex images and has been applied to many image-related problems in recent years. In order to count the number of grains in images both efficiently and accurately, this paper applied a CNN-based method to detecting grains. Then, the grains can be easily counted by locating the connected domains. The final error is within 5%, which confirms the feasibility of CNN-based method for counting grains within a panicle.
Highlights
convolutional neural network (CNN) is widely used for tasks such as image recognition [15], target detection [16], image segmentation [17], and image reconstruction [18], and performs better than traditional methods or even humans in many fields [19]
The panicle samples are taken from the rice RIL population in this study, which was derived by single-seed descents from a cross between Oryza sativa ssp
As the number numberofofgrains grainswithin within a panicle is large, the accuracy will if the number is directly selected as the output of the network
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. CNN is widely used for tasks such as image recognition [15], target detection [16], image segmentation [17], and image reconstruction [18], and performs better than traditional methods or even humans in many fields [19]. CNN is such a kind of deep neural network designed to reduce the number of parameters, but it maintains the ability to extract complex features of images. J. Luo proposed an Expectation Maximization (EM)-like self-training method that makes it possible to train U-Net cell counting networks with a small number of samples [32]. Based on numerous panicle image samples, a method of labeling and a fully convolutional network [35] based on U-Net were designed to achieve the goal of detecting grains within a panicle. The results show that the error of this algorithm was within 5%
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