**1. Introduction**

The growing population places high demands on crop yields [1]. Crop breeding is a crucial technique to increase yields, disease resistance and other desirable properties by improving the genetic characteristics of crops [2]. Phenotyping is a process central to breeding, which refers to measuring the key parameters related to crop properties, such as plant height, leaf area, leaf angle, number of grains and number of tillers [3,4]. The phenotyping process is currently mainly performed by crop breeding experts, who measure these parameters with manual tools and their sufficient experience.

In order to acquire crop growth status at different growth stages, breeding experts need to perform in-field manual phenotyping for each crop at regular intervals. Undoubtedly, this work is labor-intensive and time-consuming. The traditional manual phenotyping method is highly experience-dependent and its efficiency and reliability are limited. As a result, the rate of plant genome research is restricted by the rate of phenotyping, which is defined as the "Phenotyping Bottleneck" [5].

To speed up the breeding process and relieve the bottleneck, studies on high-throughput phenotyping platforms (HTPPs) have been widely conducted [6]. Many advanced technologies have been applied for automatic phenotyping [7]. The Scanalyzer 3D High Throughput platform [8] developed by German research institute LemnaTec has high impact [9]. Plants are transported by conveyers through a sequence of imaging cabinets equipped with

**Citation:** Huang, Y.; Xia, P.; Gong, L.; Chen, B.; Li, Y.; Liu, C. Designing an Interactively Cognitive Humanoid Field-Phenotyping Robot for In-Field Rice Tiller Counting. *Agriculture* **2022**, *12*, 1966. https://doi.org/10.3390/ agriculture12111966

Academic Editors: Jin Yuan, Wei Ji and Qingchun Feng

Received: 1 October 2022 Accepted: 6 November 2022 Published: 21 November 2022

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various sensors to acquire various phenotype data. This system is widely used in various phenotyping platforms, such as the Plant Accelerator of Australian Centre for Plant Functional Genomics (ACPFG) [10]. The Plant Accelerator, consisting of four greenhouses and two Scanalyzer 3D platforms, can accomplish high-throughput phenotyping, as well as watering and weighing the plants. Hartmann et al. [11] developed an open-source image analysis pipeline called HTPheno. It can acquire crop images using pipelines in greenhouses and measure various phenotypic parameters from the images. Liu et al. [12] presented a Digital Plant Phenotyping Platform for multiple trait measurement, such as leaf and tiller orientation. These HTPPs significantly increased the phenotyping efficiency compared with the traditional manual process. However, plants grown in greenhouses are not affected by soil condition, weather variation or many other natural factors, so phenotypes may differ from those grown naturally in fields. Moreover, to avoid the influence of leaf occlusion and overlap on measurement, plants are planted separately, which cannot simulate the plant interplay when planted closely in fields.

For the purpose of field high-throughput phenotyping, many field high-throughput phenotyping platforms have been developed to date. LemnaTec also developed a field HTPP named the Scanalyzer Field recently [13]. It is a fully automated gantry system with an extensive measurement platform equipped with cameras and sensors. It can measure up to 0.6 hectares of crops to acquire detailed phenotypic data. Researchers at the University of Arizona and the United States Department of Agriculture (USDA) [14] developed a field HTPP that included a sonar proximity sensor, sonar and GPS antenna and infrared radiometer (IRT) sensors. The system can measure canopy height, reflectance, and some other phenotypic parameters, but it can only acquire data overhead. The Robotanist developed by Mueller-Sim et al. [15] is a ground-based platform. It can autonomously navigate fields to measure stalk strength with a manipulator and collect phenotypic parameters with non-contact sensors. The platform developed by researchers at Iowa State University employs a stereo camera rig that consists of six stereo camera heads to accomplish high quality 3D reconstruction of sorghum plant architecture [16]. The system is carried by a self-navigate tractor equipped with RTK-GPS signals. Zhou et al. [17] introduced a rice panicle counting platform using images captured by an unmanned aerial vehicle based on deep learning algorithms.

Field HTPPs automatically conduct phenotyping in natural fields with high efficiency using automatic navigating and measurement systems. However, leaf occlusion and overlap in field environments severely restrict the measurement accuracy of some parameters. This has become a key challenge for automatic in-field phenotyping and restricts practical applications.

Tillers refer to the aboveground branches of gramineous plants, and the number of tillers is one of the most important parameters in ecology and breeding studies. The rice yield is usually dominated by primary tillers and some early secondary tillers [18]. As a result, tiller number is a key phenotypic trait for rice and the measurement and analysis of the tiller number are indispensable in phenotyping [19]. Rice tillers are currently manually counted using the separated shoots from a single plant by experts. The counting process is inefficient and labor-intensive. Automatic tiller counting methods have been studied in the past few years. For instance, Yang et al. [20] used an X-ray computed tomography (CT) system to measure rice tillers on a conveyer. In their work, a mean absolute error (MAE) of approximately 0.3 was reached. Huang et al. [21] proposed to measure rice tillers through magnetic resonance imaging (MRI). However, it is not suitable to perform in-field high-throughput measurements using these cumbersome and expensive systems. Scotford et al. [22] used spectral reflectance and ultrasonic sensing techniques to estimate tiller density and an accuracy of ±125 tillers per m<sup>2</sup> was achieved. Deng et al. [23] presented a rice tiller counting platform based on in-field images captured by smartphones and they were measured after the rice plants were cut and the branches were removed. Yamagishi et al. [24] proposed to count rice tillers using proximal sensing data taken by an unmanned aerial vehicle. These methods provided some attempts for in-field tiller

counting, but the key problem of occlusion and overlap was not addressed, restricting the recognition accuracy in practical applications.

To tackle the occlusion and overlap problem in in-field phenotyping in this paper, a novel phenotyping paradigm of interactive cognition is proposed. A detectable quasistructured environment is actively constructed for in-field phenotyping; therefore, the cognition process can be accomplished smoothly. This method overcomes the problem of occlusion and overlap in traditional passive automatic phenotyping methods. Meanwhile, a field phenotyping robot is developed and a bio-inspired solution is adopted so that it mimics the manual operations of breeding experts in fields. In this way, the phenotyping operational schedules are regularized. Moreover, based on the interactive cognition phenotyping method, a rice tiller counting method based on attentional residual networks (AtResNet) is proposed using the structured light images captured by the robot. The main contributions of this paper are as follows:


The rest of this paper is organized as follows. Section 2 introduces the interactive cognition phenotyping method based on the humanoid robot. Section 3 presents the bio-inspired operational forms. Section 4 describes the rice tiller counting algorithm and Section 5 shows the experimental results. Section 6 concludes the paper.
