Abstract
The number of panicles per unit area is a common indicator of rice yield and is of great significance to yield estimation, breeding, and phenotype analysis. Traditional counting methods have various drawbacks, such as long delay times and high subjectivity, and they are easily perturbed by noise. To improve the accuracy of rice detection and counting in the field, we developed and implemented a panicle detection and counting system that is based on improved region-based fully convolutional networks, and we use the system to automate rice-phenotype measurements. The field experiments were conducted in target areas to train and test the system and used a rotor light unmanned aerial vehicle equipped with a high-definition RGB camera to collect images. The trained model achieved a precision of 0.868 on a held-out test set, which demonstrates the feasibility of this approach. The algorithm can deal with the irregular edge of the rice panicle, the significantly different appearance between the different varieties and growing periods, the interference due to color overlapping between panicle and leaves, and the variations in illumination intensity and shading effects in the field. The result is more accurate and efficient recognition of rice-panicles, which facilitates rice breeding. Overall, the approach of training deep learning models on increasingly large and publicly available image datasets presents a clear path toward smartphone-assisted crop disease diagnosis on a global scale.
Highlights
Rice is recognized as the most significant crop species worldwide, and the current annual production of rice grain is 590 million tons [1]
This paper proposes a method to detect rice panicles based on statistical treatment of digital images acquired from a UAV platform
Across all our experimental configurations, the overall precision obtained on the dataset varied from 0.592 to 0.897, which shows the strong potential of deep learning (DL) technology for such recognition problems
Summary
Rice is recognized as the most significant crop species worldwide, and the current annual production of rice grain is 590 million tons [1]. High yield has always been one of the most important objectives of rice breeding and cultivation. Rice breeding requires measuring the yield of a large number of candidate samples in different environments, so as to provide a basis for breeding high-yield, high-quality, stress-resistant rice varieties. The rice panicle is the organ for the growth of rice grains and is directly related to final yield. It plays an important role in pest detection, nutritional diagnosis, and growth-period detection [2]. The appearance of panicles—such as shape, color, size, texture, and posture—vary strongly among the different rice varieties and growth stages. The edge of the rice panicle is very irregular, and the panicle color blends
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