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A “smart canopy” ideotype has been proposed with leaves being upright at the top and more horizontal toward the bottom of the plant to maximize light interception and conversion efficiencies, and thus increasing yield. The genetic control of leaf angle has, to date, been studied on one or two leaves, or data have been merged from multiple leaves to generate average values. This approach has limited our understanding of the diversity of leaf angles across layers and their genetic control. Genome-wide association studies and quantitative trait loci mapping studies in sorghum (Sorghum bicolor) were performed using layer-specific angle data collected manually and via high-throughput phenotyping strategies. The observed distribution of angles in indoor and field settings is opposite to the ideotype. Several genomic regions were associated with leaf angle within layers or across the canopy. The expression of the brassinosteroid-related transcription factor BZR1/BES1 and the auxin-transporter Dwarf3 were found to be highly correlated with the distribution of angles at different layers. The application of a brassinosteroid biosynthesis inhibitor could not revert the undesirable overall angle distribution. These discoveries demonstrate that the exploitation of layer-specific quantitative trait loci/genes will be instrumental to reversing the natural angle distribution in sorghum according to the “smart canopy” ideotype.

Contains fulltext : 244074.pdf (Publisher’s version ) (Open Access) PURPOSE: To compare quality of vision and vision-related quality of life (QOL) in patients undergoing Descemet membrane endothelial keratoplasty (DMEK) or ultrathin Descemet stripping automated endothelial keratoplasty (DSAEK). METHODS: Fifty-four eyes of 54 patients with Fuchs' dystrophy from six corneal clinics in the Netherlands were randomized to DMEK or ultrathin DSAEK and examined preoperatively, and 3, 6 and 12 months postoperatively. Main outcome measures were corneal higher-order aberrations (HOAs), contrast sensitivity, straylight and vision-related QOL. RESULTS: Posterior corneal HOAs decreased after DMEK and increased after ultrathin DSAEK (p ≤ 0.001) 3 months after surgery and correlated positively with best spectacle-corrected visual acuity (12 months: r = 0.29, p = 0.04). Anterior and total corneal HOAs did not differ significantly between both techniques at any time point. Contrast sensitivity was better (p = 0.01), and straylight was lower (p = 0.01) 3 months after DMEK compared with ultrathin DSAEK; 95% confidence interval [CI] of log(cs) 1.10-1.35 versus 95% CI: 0.84 to 1.12, and 95% CI: log(s) 1.18 to 1.43 versus 95% CI: 1.41 to 1.66, respectively. Both were comparable at later time points. Vision-related QOL (scale 0-100) did not differ significantly between both groups at any time point and improved significantly at 3 months (β = 12 [95% CI: 7 to 16]; p

The rural US includes 72% of the nation's land and 46 million people, and it serves as major sources of food and energy for the nation. Thus rural prosperity is essential to US wellbeing. As a foundation for next-generation rural economy and communities, broadband connectivity is a key driver of rural prosperity. Yet 39% of the rural US lacks broadband access, and most agriculture (ag) farms are not connected at all. To address the rural broadband challenge, we will develop the ARA rural wireless living lab. ARA will not only serve as a first-of-its-kind, real-world wireless experimental infrastructure for smart and connected rural communities, it will also provide the living lab processes, activities, and organizations to engage the broad wireless and application communities in the research, education, innovation, and pilot of affordable, high-capacity rural broadband solutions. Through this visioning article, we illustrate the community, application, economic, and operational contexts of rural wireless, the design of ARA, ARA-enabled research, and how ARA is expected to make rural broadband as affordable as urban broadband today. This article is also a call-to-action for the broad wireless and application communities to participate in the ARA living lab activities and to join the ARA Consortium of public-private partners in shaping the future of advanced wireless, rural broadband, and rural communities in general.

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed in a low-throughput manner, such as on Petri plates. Additionally, the BR pathway affects drought responses, but drought experiments are time consuming and difficult to control. To mitigate these issues and increase throughput, we developed the Robotic Assay for Drought (RoAD) system to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a robotic arm, a rover, a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction to accurately measure traits including plant area, plant volume, leaf length, and leaf width. We then applied machine learning algorithms that utilize the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought-treated, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants in 3D, providing insights into the BR-mediated control of plant growth and stress responses.

Maize (Zea mays L.) is one of the most economically important cereal crops. Though time-consuming and labour-intensive, manually measuring phenotypic traits in the field has been the common practice for maize breeding programs. This study presents a system for automated characterisation of several important plant architectural traits of maize plants under field conditions. An algorithm was developed to extract 3D plant skeletons from point cloud data acquired by side-viewing Time-of-Flight cameras. Plants were detected as 3D lines by Hough transform of the skeleton nodes. By analysing the graph structure of the skeletons with respect to the 3D lines, the point cloud was partitioned into plant instances with the stems and the leaves separated. Furthermore, plant height, plant orientation, leaf angle, and stem diameter were extracted for each plant. The image-derived estimates of traits were compared to manual measurements at multiple growth stages. Satisfactory accuracies in terms of mean absolute error (MAE) and coefficient of determination (R2) were achieved for plant height (before flowering: MAE 0.15 m, R2 0.96; after flowering: MAE 0.054 m, R2 0.83), leaf angle (MAE 2.8°, R2 0.83), and plant orientation (MAE 13°), except for stem diameter due to the limitations of the depth sensor. The results showed that the system was robust and accurate when the plants were imaged from only one side despite occlusions caused by leaves, and the method was applicable to maize plants from an early growth stage to full maturity.

To analyze the focal characteristics of cylindrically polarized beams, a visual analysis method is proposed. As known, the focal field can be described by three mutually perpendicular components, each one is the total contribution of all parts of the incident beams. For each component of all contributing parts weapply path integral method, then from the path integral curves extract focal field properties immediately, such as polarization state or intensity distribution. The analysis process of PI is visual and more understandable, and has more powerful information extraction function, which is also helpful for the design of special filtering pupil.

Mechanical weeding augmented with automation technology should result in highly effective weeding systems. However, the interaction between weeding mechanisms and soil is not well understood. Moreover, soil is highly variable, which makes studying this interaction challenging. The main objective of this research was to develop a method to investigate the effects of mechanical tool-soil interaction on weeding performance for different operating conditions in a controlled environment. Experiments were conducted in an indoor soil bin with loam soil, and the weeding performance was studied using small wooden cylinders as simulated weed plants. The investigations featured a single cylindrical tine and a rotating tine mechanism, vertically oriented and inserted into the soil. The total width of soil disturbance and potential weeding rate were evaluated for the single cylindrical tine at different levels of three operating parameters: tine diameter (6.35, 7.94, and 9.53 mm), working soil depth (25.4, 50.8, and 76.2 mm), and tine speed (0.23 and 0.45 m s-1). Potential weeding rate was examined for the rotating tine mechanism with two operating parameters: working soil depth (25.4 and 76.2 mm) and rotational speed (25, 50, and 100 rpm). Statistical analysis was performed using ANOVA at p < 0.05. A simulation of the rotating tine mechanism was developed that estimated the disturbed area. For the single tine, soil disturbance width was independent of tine speed; however, tine diameter and depth had significant effects, as the width increased with increased levels of these two parameters. All three parameters had significant effects on the potential weeding rate of the single tine, and the rates were observed to increase with higher levels of the parameters. For the rotating tine mechanism, both depth and rotational speed were significant. The potential weeding rate for the rotating tine mechanism was found to increase with higher levels of these parameters. The results showed that although the width of soil disturbance due to a cylindrical tine was affected by the tine diameter and working soil depth, operating parameters such as increased longitudinal and rotational speeds also affected plant disturbance. The percentage of disturbed soil area in the simulation followed similar patterns as the percentage of disturbed plants observed in the experiments. Keywords: Inter-row weeding, Intra-row weeding, Mechanical weeding, Rotating tine mechanism, Soil disturbance, Tine.

Agricultural and field robots operating autonomously require several layers of technologies. To achieve the functionality and complexity needed, robot development takes place with simulation and visualization tools that can reduce physical prototyping and compliment field testing. Models are needed to implement simulation process and validate various modeling scopes. The degree of fidelity in reproducing the physical systems must be considered and decided as a part of the development process. Simulations of agricultural and field robots need dynamic system models of the robot, virtual world models, and sensor models, as well as three-dimensional solid models to visualize simulations. Agricultural and field robots often interact with the crop fields or plant environments, and so any simulation development process must also include interaction models between machine systems, soil, biological entities (e.g., plants, pests), and operators. This chapter provides an overview of modeling, simulation, and visualization aspects needed for the development of agricultural and field robotic systems. Different tool set scenarios are also described. In addition, a case study showing the use of Gazebo to model a phenotyping robot is presented.

Continuous crop improvement is essential to meet the growing demands for food, feed, fuel, and fiber around the globe. High-throughput plant phenotyping (HTPP) aims to break the bottleneck in plant breeding programs where phenotypic data are mostly collected with inefficient manual methods. With the recent rapid advancements and applications of robotics in many industries, field robots are also expected to bring transformational changes to HTPP applications. This chapter presents an updated review of the infield ground-based robotic HTPP systems developed so far. Moreover, we report a case study of an autonomous mobile phenotyping robot PhenoBot 3.0 for row crop phenotyping, focusing on the development and evaluation of the navigation system for the articulated steering, a four-wheel-drive robot with an extremely tall sensor mast. Several navigation techniques were integrated to achieve robustness at different corn plant growth stages. Additionally, we briefly review the major sensing technologies for field-based HTPP and present a vision sensor PhenoStereo to show the promising potential of integrating conventional stereo imaging with the state-of-the-art visual perception techniques for plant organ phenotyping applications. As an example, we show that a highly accurate estimation of sorghum stem diameter can be achieved with PhenoStereo. With this chapter, our goal is to provide valuable insights and guidance on the development of infield ground robotic HTPP systems to researchers and practitioners.

Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments are typically performed on petri plates and/or in a low-throughput manner. Additionally, the BR pathway has extensive crosstalk with drought responses, but drought experiments are time-consuming and difficult to control. Thus, we developed Robotic Assay for Drought (RoAD) to perform BR and drought response experiments in soil-grown Arabidopsis plants. RoAD is equipped with a bench scale, a precisely controlled watering system, an RGB camera, and a laser profilometer. It performs daily weighing, watering, and imaging tasks and is capable of administering BR response assays by watering plants with Propiconazole (PCZ), a BR biosynthesis inhibitor. We developed image processing algorithms for both plant segmentation and phenotypic trait extraction in order to accurately measure traits in 2-dimensional (2D) and 3-dimensional (3D) spaces including plant surface area, leaf length, and leaf width. We then applied machine learning algorithms that utilized the extracted phenotypic parameters to identify image-derived traits that can distinguish control, drought, and PCZ-treated plants. We carried out PCZ and drought experiments on a set of BR mutants and Arabidopsis accessions with altered BR responses. Finally, we extended the RoAD assays to perform BR response assays using PCZ in Zea mays (maize) plants. This study establishes an automated and non-invasive robotic imaging system as a tool to accurately measure morphological and growth-related traits of Arabidopsis and maize plants, providing insights into the BR-mediated control of plant growth and stress responses.

In recent years, three-dimensional (3D) sensing has gained a great interest in plant phenotyping because it can represent the 3D nature of plant architecture. Among all available 3D imaging technologies, stereo vision offers a viable solution due to its high spatial resolution and wide selection of camera modules. However, the performance of in-field stereo imaging for plant phenotyping has been adversely affected by textureless regions and occlusions of plants, and variable outdoor lighting and wind conditions. In this research, a portable stereo imaging module namely PhenoStereo was developed for high-throughput field-based plant phenotyping. PhenoStereo featured a self-contained embedded design, which made it capable of capturing images at 14 stereoscopic frames per second. In addition, a set of customized strobe lights was integrated to overcome lighting variations and enable the use of high shutter speed to overcome motion blurs. The stem diameter of sorghum plants is an important trait for stalk strength and biomass potential evaluation but has been identified as a challenging sensing task to automated in the field due to the complexity of the imaging object and the environment. To that connection, PhenoStereo was used to acquire a set of sorghum plant images and an automated point cloud data processing pipeline was also developed to automatically extract the stems and then quantify their diameters via an optimized 3D modeling process. The pipeline employed a Mask R-CNN deep learning network for detecting stalk contours and a Semi-Global Block Matching stereo matching algorithm for generating disparity maps. The correlation coefficient (r) between the image-derived stem diameters and the ground truth was 0.97 with a mean absolute error (MAE) of 1.44 mm, which outperformed any previously reported sensing approaches. These results demonstrated that with proper customization stereo vision can be a highly desirable sensing method for field-based plant phenotyping using high-fidelity 3D models reconstructed from stereoscopic images. With the proving results from sorghum plant stem diameter sensing, this proposed stereo sensing approach can likely be extended to characterize a broad spectrum of plant phenotypes such as leaf angle and tassel shape of maize plants and seed pods and stem nodes of soybean plants.

The frequency of a Gunn diode is usually determined by the length of its oscillation channel, hard to be change after the device has been fabricated. We show that in a top-gated planar nanodevice the frequency of Gunn oscillations can be conveniently increased, i.e. tuned by the top-gate bias. Ensemble Monte Carlo (EMC) simulations indicate that the achieved maximum frequency reaches 1 THz, about three times of that of an un-gated device. Moreover, the tunable frequency-range also increases with the length of the top-gate, resulting in a widest range of ∼0.7 THz. Detailed analysis of the Gunn-domain dynamics shows that this tunable effect is related to the simultaneous formation of multi-domains, which are excited by the top-gate induced non-uniformed electricfield along the oscillation channel.

Various instrumentation devices for plant physiology studies, such as spectrometers, chlorophyll fluorometers, and Raman spectroscopy sensors, require accurate placement of their sensor probes toward the leaf surface to meet specific requirements of probe-to-target distance and orientation. In this work, a Kinect V2 sensor, a high-precision 2D laser profilometer, and a six-axis robotic manipulator were used to automate the leaf probing task. The relatively wide field of view and high resolution of the Kinect V2 allowed rapid capture of the full 3D environment in front of the robot. The location and size of each plant were estimated by k-means clustering where k was the user-defined number of plants. A real-time collision-free motion planning framework based on probabilistic roadmaps was adapted to maneuver the robotic manipulator without colliding with the plants. Each plant was scanned from the top with the short-range profilometer to obtain high-precision 3D point cloud data. Potential leaf clusters were extracted by a 3D region growing segmentation scheme. Each leaf segment was further partitioned into small patches by a voxel cloud connectivity segmentation method. Only the patches with low root mean square errors of plane fitting were used to compute leaf probing poses of the robot. Experiments conducted inside a growth chamber mockup showed that the developed robotic leaf probing system achieved an average motion planning time of 0.4 s with an average end-effector travel distance of 1.0 m. To examine the probing accuracy, a square surface was scanned at different angles, and its centroid was probed perpendicularly. The average absolute probing errors of distance and angle were 1.5 mm and 0.84°, respectively. These results demonstrate the utility of the proposed robotic leaf probing system for automated non-contact deployment of spectroscopic sensor probes for indoor plant phenotyping under controlled environmental conditions. Keywords: 3D perception, Agricultural robotics, Leaf probing, Motion planning, Plant phenotyping.

A low-cost three-dimensional (3D) plant reconstruction and morphological traits characterization system was developed. Corn plant seedlings were used as research objects for development and validation of the 3D reconstruction and point cloud data analysis algorithms. In this application, precise alignment of multiple 3D views generated by a 3D time-of-flight (ToF) sensor is critical to the 3D reconstruction of a plant. Previous research indicated that there is strong need for high-throughput, high-accuracy, and low-cost 3D plant reconstruction and trait characterization phenotyping systems. This research produced a 3D reconstruction system for indoor plant phenotyping by innovatively integrating a low-cost 2D camera, a low-cost 3D ToF camera, and a chessboard pattern beacon array to track the position and attitude of the 3D ToF sensor and, thus, accomplished precise 3D point cloud registration over multiple views. Specifically, algorithms for beacon target detection, camera pose tracking, and spatial relationship calibration between 2D and 3D cameras were developed for such a low-cost but high-performance 3D reconstruction solution. A plant analysis algorithm in a 3D space was developed to extract the morphological trait parameters of the plants by analyzing their 3D point cloud data. The phenotypical data obtained by this novel and low-cost 3D reconstruction based phenotyping system were validated by the experimental data generated by instrument and manual measurement. The results demonstrated that the developed phenotyping system has achieved promising measurement accuracy, fast processing speed while offering a low hardware cost, lending itself to a practical means of acquiring detailed 3D morphological traits for automated indoor plant phenotyping.

The order Enterobacterales was divided into seven families including the family Enterobacteriaceae in 2016. The genus Klebsiella within the family Enterobacteriaceae was divided into two genera Klebsiella and Raoultella in 2001. Here, our phylogenomic analysis shows that the genus Raoultella is nested within the genus Klebsiella. Klebsiella and Raoultella together are monophyletic and share average amino acid identities (AAIs) of 86.9–89.6% above the AAI threshold (86%) for genus delimitation within the family Enterobacteriaceae. Klebsiella and Raoultella share AAIs of 79.9%–85.0% with the other genera within the subfamily “Klebsiella clade”, which are in the range of inter-genus AAIs (74–85%) within the family Enterobacteriaceae. Klebsiella and Raoultella also share six known conserved signature indels . Therefore, we propose to reunify Klebsiella and Raoultella to the single genus Klebsiella and reclassify Raoultella electrica as Klebsiella electrica comb. nov. Our genome-based taxonomic analyses also identified seven potential novel species within the unified genus Klebsiella.

A 3D time-of-flight camera was applied to develop a crop plant recognition system for broccoli and green bean plants under weedy conditions. The developed system overcame the previously unsolved problems caused by occluded canopy and illumination variation. An efficient noise filter was developed to remove the sparse noise points in 3D point cloud space. Both 2D and 3D features including the gradient of amplitude and depth image, surface curvature, amplitude percentile index, normal direction, and neighbor point count in 3D space were extracted and found effective for recognizing these two types of plants. Separate segmentation algorithms were developed for each of the broccoli and green bean plant in accordance with their 3D geometry and 2D amplitude characteristics. Under the experimental condition where the crops were heavily infested by various types of weed plants, detection rates over 88.3% and 91.2% were achieved for broccoli and green bean plant leaves, respectively. Additionally, the crop plants were segmented out with nearly complete shape. Moreover, the algorithms were computationally optimized, resulting in an image processing speed of over 30 frames per second.

In this study, visible and near-infrared hyperspectral imaging (HSI) technique combined with deep learning algorithm was investigated for discriminating the freshness of shrimp during cold storage. Shrimps were labeled into two freshness grades (fresh and stale) according to their total volatile basic nitrogen contents. Spectral features were extracted from the HSI data by stacked auto-encoders (SAEs)-based deep learning algorithm and then used to classify the freshness grade of shrimp by a logistic regression (LR)-based deep learning algorithm. The results demonstrated that the SAEs–LR achieved satisfactory total classification accuracy of 96.55 and 93.97% for freshness grade of shrimp in calibration (116 samples) and prediction (116 samples) sets, respectively. An image processing algorithm was also developed for visualizing the classification map of freshness grade. Results confirmed the possibility of rapid and nondestructive detecting freshness grade of shrimp by the combination of hyperspectral imaging technique and deep learning algorithm. The SAEs–LR method adds a new tool for the multivariate analysis of hyperspectral image for shrimp quality inspections.

1 Standing corn residue has been proven to reduce wind erosion in Northeast China, but how standing corn residue affects soils during winter remained unclear. Our objective was to compare soil frost depth associated with two zero-tillage methods [i.e., chopping corn stalks into small sections then spread them on soil (hereafter referred to as ‘CCR’) and standing corn residue (hereafter referred to as ‘SCR’)]. Frost tubes were used to determine soil frost depth, and soil heat flux plates were used to measure soil heat flux 8-cm beneath the soil surface. Compared with CCR treatment, SCR reduced maximum soil frost depth significantly ( p

Background Assessing the impact of the environment on plant performance requires growing plants under controlled environmental conditions. Plant phenotypes are a product of genotype × environment (G × E), and the Enviratron at Iowa State University is a facility for testing under controlled conditions the effects of the environment on plant growth and development. Crop plants (including maize) can be grown to maturity in the Enviratron, and the performance of plants under different environmental conditions can be monitored 24 h per day, 7 days per week throughout the growth cycle. Results The Enviratron is an array of custom-designed plant growth chambers that simulate different environmental conditions coupled with precise sensor-based phenotypic measurements carried out by a robotic rover. The rover has workflow instructions to periodically visit plants growing in the different chambers where it measures various growth and physiological parameters. The rover consists of an unmanned ground vehicle, an industrial robotic arm and an array of sensors including RGB, visible and near infrared (VNIR) hyperspectral, thermal, and time-of-flight (ToF) cameras, laser profilometer and pulse-amplitude modulated (PAM) fluorometer. The sensors are autonomously positioned for detecting leaves in the plant canopy, collecting various physiological measurements based on computer vision algorithms and planning motion via “eye-in-hand” movement control of the robotic arm. In particular, the automated leaf probing function that allows the precise placement of sensor probes on leaf surfaces presents a unique advantage of the Enviratron system over other types of plant phenotyping systems. Conclusions The Enviratron offers a new level of control over plant growth parameters and optimizes positioning and timing of sensor-based phenotypic measurements. Plant phenotypes in the Enviratron are measured in situ—in that the rover takes sensors to the plants rather than moving plants to the sensors.