Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automated Framework for Plant Detection Based on Deep Simulated Learning from Drone Imagery

Traditional mapping and monitoring of agricultural fields are expensive, laborious, and may contain human errors. Technological advances in platforms and sensors, followed by artificial intelligence (AI) and deep learning (DL) breakthroughs in intelligent data processing, led to improving the remote sensing applications for precision agriculture (PA). Therefore, technological advances in platforms and sensors and intelligent data processing methods, such as machine learning and DL, and geospatial and remote sensing technologies, have improved the quality of agricultural land monitoring for PA needs. However, providing ground truth data for model training is a time-consuming and tedious task and may contain multiple human errors. This paper proposes an automated and fully unsupervised framework based on image processing and DL methods for plant detection in agricultural lands from very high-resolution drone remote sensing imagery. The proposed framework’s main idea is to automatically generate an unlimited amount of simulated training data from the input image. This capability is advantageous for DL methods and can solve their biggest drawback, i.e., requiring a considerable amount of training data. This framework’s core is based on the faster regional convolutional neural network (R-CNN) with the backbone of ResNet-101 for object detection. The proposed framework’s efficiency was evaluated by two different image sets from two cornfields, acquired by an RGB camera mounted on a drone. The results show that the proposed method leads to an average counting accuracy of 90.9%. Furthermore, based on the average Hausdorff distance (AHD), an average object detection localization error of 11 pixels was obtained. Additionally, by evaluating the object detection metrics, the resulting mean precision, recall, and F1 for plant detection were 0.868, 0.849, and 0.855, respectively, which seem to be promising for an unsupervised plant detection method.
Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales

Tropical forests are acknowledged for providing important ecosystem services and are renowned as “the lungs of the planet Earth” due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for ‘forest’ classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.
Exploration of Indoor Barrier-Free Plane Intelligent Lofting System Combining BIM and Multi-Sensors

Lofting is an essential part of construction projects and the high quality of lofting is the basis of efficient construction. However, the most common method of lofting currently which uses the total station in a multi-person cooperative way consumes much manpower and time. With the rapid development of remote sensing and robot technology, using robots instead of manpower can effectively solve this problem, but few scholars study this. How to effectively combine remote sensing and robots with lofting is a challenging problem. In this paper, we propose an intelligent lofting system for indoor barrier-free plane environment, and design a high-flexibility, low-cost autonomous mobile robot platform based on single chip microcomputer, Micro Electro Mechanical Systems-Inertial Measurement Unit (MEMS-IMU), wheel encoder, and magnetometer. The robot also combines Building Information Modeling (BIM) laser lofting instrument and WIFI communication technology to get its own position. To ensure the accuracy of localization, the kinematics model of Mecanum wheel robot is built, and Extended Kalman Filter (EKF) is also used to fuse multi-sensor data. It can be seen from the final experimental results that this system can significantly improve lofting efficiency and reduce manpower.
Wall boundary equations with slip and catalysis for multicomponent, nonequilibrium gas flows

Boundary equations obtained for a low Reynolds number, high enthalpy gas flow in regions of velocity slip and temperature jump are presented. The formulation treats, through a first-order distribution function used to include multicomponent diffusion, a multicomponent gas mixture that may be in nonequilibrium with finite-rate catalytic recombination occurring on the wall. In the boundary equations, which are obtained for use in flow-field calculations applicable to low-density flow regimes, a simplified gas/wall interaction is assumed wherein individual atoms or molecules either reflect specularly off the wall or stick and are fully accommodated. Fluxes in terms of evaluated integrals over the distribution function and integrals necessary for determining fluxes are given.
A Gated Recurrent Units (GRU)-Based Model for Early Detection of Soybean Sudden Death Syndrome through Time-Series Satellite Imagery

In general, early detection and timely management of plant diseases are essential for reducing yield loss. Traditional manual inspection of fields is often time-consuming and laborious. Automated imaging techniques have recently been successfully applied to detect plant diseases. However, these methods mostly focus on the current state of the crop. This paper proposes a gated recurrent unit (GRU)-based model to predict soybean sudden death syndrome (SDS) disease development. To detect SDS at a quadrat level, the proposed method uses satellite images collected from PlanetScope as the training set. The pixel image data include the spectral bands of red, green, blue and near-infrared (NIR). Data collected during the 2016 and 2017 soybean-growing seasons were analyzed. Instead of using individual static imagery, the GRU-based model converts the original imagery into time-series data. SDS predictions were made on different data scenarios and the results were compared with fully connected deep neural network (FCDNN) and XGBoost methods. The overall test accuracy of classifying healthy and diseased quadrates in all methods was above 76%. The test accuracy of the FCDNN and XGBoost were 76.3–85.5% and 80.6–89.2%, respectively, while the test accuracy of the GRU-based model was 82.5–90.4%. The calculation results show that the proposed method can improve the detection accuracy by up to 7% with time-series imagery. Thus, the proposed method has the potential to predict SDS at a future time.
Accuracy Evaluation of Four Greenland Digital Elevation Models (DEMs) and Assessment of River Network Extraction

Digital Elevation Models (DEMs) of Greenland provide the basic data for studying the Greenland ice sheet (GrIS), but little research quantitatively evaluates and compares the accuracy of various Greenland DEMs. This study uses IceBridge elevation data to evaluate the accuracies of the the Greenland Ice Map Project (GIMP)1 DEM, GIMP2 DEM, TanDEM-X, and ArcticDEM in their corresponding time ranges. This study also analyzes the impact of DEM accuracy and resolution on the accuracy of river network extraction. The results show that (1) within the time range covered by each DEM, TanDEM-X with an RMSE of 5.60 m has higher accuracy than the other DEMs in terms of absolute height accuracy, while GIMP1 has the lowest accuracy among the four Greenland DEMs, with an RMSE of 14.34 m. (2) Greenland DEMs are affected by regional errors and interannual changes. The accuracy in areas with elevations above 2000 m is higher than that in areas with elevations below 2000 m, and better accuracy is observed in the north than in the south. The stability of the ArcticDEM product is higher than those of the other three DEM products, and its RMSE standard deviation over multiple years is only 0.14 m. Therefore, the errors caused by the applications of DEMs with longer time spans are smaller. GIMP1 performs in an opposite manner, with a standard deviation of 2.39 m. (3) The river network extracted from TanDEM-X is close to the real river network digitized from remote sensing images, with an accuracy of 50.78%. The river network extracted from GIMP1 exhibits the largest errors, with an accuracy of only 8.83%. This study calculates and compares the accuracy of four Greenland DEMs and indicates that TanDEM-X has the highest accuracy, adding quantitative studies on the accuracy evaluation of various Greenland DEMs. This study also compares the results of different DEM river network extractions, verifies the impact of DEM accuracy on the accuracy of the river network extraction results, and provides an explorable direction for the hydrological analysis of Greenland as a whole.
Detecting Plant Invasion in Urban Parks with Aerial Image Time Series and Residual Neural Network

Invasive plants are a major agent threatening biodiversity conservation and directly affecting our living environment. This study aims to evaluate the potential of deep learning, one of the fastest-growing trends in machine learning, to detect plant invasion in urban parks using high-resolution (0.1 m) aerial image time series. Capitalizing on a state-of-the-art, popular architecture residual neural network (ResNet), we examined key challenges applying deep learning to detect plant invasion: relatively limited training sample size (invasion often confirmed in the field) and high forest contextual variation in space (from one invaded park to another) and over time (caused by varying stages of invasion and the difference in illumination condition). To do so, our evaluations focused on a widespread exotic plant, autumn olive (Elaeagnus umbellate), that has invaded 20 urban parks across Mecklenburg County (1410 km2) in North Carolina, USA. The results demonstrate a promising spatial and temporal generalization capacity of deep learning to detect urban invasive plants. In particular, the performance of ResNet was consistently over 96.2% using training samples from 8 (out of 20) or more parks. The model trained by samples from only four parks still achieved an accuracy of 77.4%. ResNet was further found tolerant of high contextual variation caused by autumn olive’s progressive invasion and the difference in illumination condition over the years. Our findings shed light on prioritized mitigation actions for effectively managing urban invasive plants.
Evolution of AITR family genes in cotton and their functions in abiotic stress tolerance.

Abiotic stresses are major environmental factors inhibiting plant growth and development. AITRs (ABA-induced transcription repressors) are a novel family of transcription factors regulating ABA (abscisic acid) signaling and plant responses to abiotic stresses in Arabidopsis. However, the composition and evolution history of AITRs and their roles in cotton genus are largely unknown. Here, a total of 12 putative AITR genes were identified in cultivated tetraploid cotton Gossypium hirsutum. Phylogenetic analysis of GhAITRs in these cottons and their closely related species implicate the ancient genome-wide duplication occurring after speciation of Gossypium and Theobroma could generate the duplicates of GhAITRs. Duplicated GhAITRs were stably inherited in following diploid speciation and further allotetraploidy in Gossypium. Homologous GhAITRs shared common expression patterns in response to ABA, drought and salinity treatments, and drought tolerance could be affected in transgenic Arabidopsis plants expressing GhAITR-A1. Together, our findings reveal that duplicates in GhAITR gene family were achieved by whole genome duplication rather than three individual duplication events, and GhAITRs function as transcription repressors and are involved in the regulation of plant responses to ABA and drought stress. These results may provide insights towards the improvement of abiotic stress tolerance in cotton by using GhAITRs.
Manufacturing Considerations for the Development of Lipid Nanoparticles Using Microfluidics

In the recent of years, the use of lipid nanoparticles (LNPs) for RNA delivery has gained considerable attention, with a large number in the clinical pipeline as vaccine candidates or to treat a wide range of diseases. Microfluidics offers considerable advantages for their manufacture due to its scalability, reproducibility and fast preparation. Thus, in this study, we have evaluated operating and formulation parameters to be considered when developing LNPs. Among them, the flow rate ratio (FRR) and the total flow rate (TFR) have been shown to significantly influence the physicochemical characteristics of the produced particles. In particular, increasing the TFR or increasing the FRR decreased the particle size. The amino lipid choice (cationic—DOTAP and DDAB; ionisable—MC3), buffer choice (citrate buffer pH 6 or TRIS pH 7.4) and type of nucleic acid payload (PolyA, ssDNA or mRNA) have also been shown to have an impact on the characteristics of these LNPs. LNPs were shown to have a high (>90%) loading in all cases and were below 100 nm with a low polydispersity index (≤0.25). The results within this paper could be used as a guide for the development and scalable manufacture of LNP systems using microfluidics.
Impact of Pediatric Mobile Game Play on Healthy Eating Behavior: Randomized Controlled Trial

Background Video and mobile games have been shown to have a positive impact on behavior change in children. However, the potential impact of game play patterns on outcomes of interest are yet to be understood, especially for games with implicit learning components. Objective This study investigates the immediate impact of fooya! , a pediatric dietary mobile game with implicit learning components, on food choices. It also quantifies children’s heterogeneous game play patterns using game telemetry and determines the effects of these patterns on players’ food choices. Methods We analyzed data from a randomized controlled trial (RCT) involving 104 children, aged 10 to 11 years, randomly assigned to the treatment group (played fooya! , a dietary mobile game developed by one of the authors) or the control group (played Uno, a board game without dietary education). Children played the game for 20 minutes each in two sessions. After playing the game in each session, the children were asked to choose 2 out of 6 food items (3 healthy and 3 unhealthy choices). The number of healthy choices in both sessions was used as the major outcome. We first compared the choice and identification of healthy foods between treatment and control groups using statistical tests. Next, using game telemetry, we determined the variability in game play patterns by quantifying game play measures and modeled the process of game playing at any level across all students as a Markov chain. Finally, correlation tests and regression models were used to establish the relationship between game play measures and actual food choices. Results We saw a significant main effect of the mobile game on number of healthy foods actually chosen (treatment 2.48, control 1.10; P<.001; Cohen d=1.25) and identified (treatment 7.3, control 6.94; P=.048; Cohen d=.25). A large variation was observed in children’s game play patterns. Children played an average of 15 game levels in 2 sessions, with a range of 2 to 23 levels. The greatest variation was noted in the proportion of scoring activities that were highly rewarded, with an average of 0.17, ranging from 0.003 to 0.98. Healthy food choice was negatively associated with the number of unhealthy food facts that children read in the game (Kendall τ=–.32, P=.04), even after controlling for baseline food preference. Conclusions A mobile video game embedded with implicit learning components showed a strong positive impact on children’s food choices immediately following the game. Game telemetry captured children’s different play patterns and was associated with behavioral outcomes. These results have implications for the design and use of mobile games as an intervention to improve health behaviors, such as the display of unhealthy food facts during game play. Longitudinal RCTs are needed to assess long-term impact. Trial Registration ClinicalTrials.gov NCT04082195; https://clinicaltrials.gov/ct2/show/NCT04082195, registered retrospectively.
Effect of Achilles Tendon Lengthening and Gastrocnemius Recession on Radiographic Tibiotalar Motion Following Total Ankle Replacement

Background: Surgeons frequently add an Achilles tendon lengthening or gastrocnemius recession to increase dorsiflexion following total ankle replacement. Previous studies have looked at the effects of these procedures on total tibiopedal motion. However, tibiopedal motion includes motion of the midfoot and hindfoot as well as the ankle replacement. The current study examined the effects of Achilles tendon lengthening and gastrocnemius recession on radiographic tibiotalar motion at the level of the prosthesis only. Methods: Fifty-four patients with an average of 25 months follow-up after total ankle replacement were divided into 3 groups: (1) patients who underwent Achilles tendon lengthening, (2) patients who had a gastrocnemius recession, (3) patients with no lengthening procedure. Tibiotalar range of motion was measured on lateral dorsiflexion-plantarflexion radiographs using reference lines on the surface of the implants. Results: Both Achilles tendon lengthening and gastrocnemius recession significantly increased tibiotalar dorsiflexion when compared to the group without lengthening. However, the total tibiotalar range of motion among the 3 groups was the same. Interestingly, the Achilles tendon lengthening group lost 11.7 degrees of plantarflexion compared to the group without lengthening, which was significant. Conclusion: Both Achilles tendon lengthening and gastrocnemius recession increased radiographic tibiotalar dorsiflexion following arthroplasty. Achilles tendon lengthening had the unexpected effect of significantly decreasing plantarflexion. Gastrocnemius recession may be a better choice when faced with a tight ankle replacement because it increases dorsiflexion without a compensatory loss of plantarflexion. Level of Evidence: Level III, retrospective comparative study.
Real-time PCR and droplet digital PCR are accurate and reliable methods to quantify Pseudomonas syringae pv. actinidiae biovar 3 in kiwifruit infected plantlets.

Pseudomonas syringae pv. actinidiae (Psa) is the etiological agent of kiwifruit canker disease, causing severe economic losses in kiwifruit production areas around the world. Rapid diagnosis, understanding of bacterial virulence and rate of infection in kiwifruit cultivars is important in applying effective measures of disease control. Psa load in kiwifruit is currently determined by a labor-intense colony counting method with no high-throughput and specific quantification method being validated. In this work we used three alternative Psa quantification methods in two infected kiwifruit cultivars: start of growth time, quantitative qPCR (qPCR), and droplet digital PCR (ddPCR). Method performance in each case was compared to the colony counting method. Methods were validated using calibration curves obtained with serial dilutions of Psa3 inoculum and standard growth curves obtained from kiwifruit samples infected with Psa3 inoculum. All three alternative methods showed high correlation (r > 0.85) with the colony counting method. qPCR and ddPCR were very specific, sensitive (5 × 102 CFU/cm2), highly correlated to each other (r = 0.955) and flexible allowing for sample storage. The inclusion of a kiwifruit biomass marker increased the methods' accuracy. The qPCR method was efficient and allowed for high-throughput processing and the ddPCR method showed highly accurate results but was more expensive and time consuming. While not ideal for high-throughput processing, ddPCR was useful in developing accurate standard curves for the qPCR method. The combination of the two methods is high-throughput, specific for Psa3 quantification and useful for research studies e.g. disease phenotyping and host-pathogen interactions.