Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning

Background Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of the experts. Methods To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images captured by a consumer UAV. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of crop candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials captured by the DJI Phantom 4 Pro V2.0. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. Result and conclusion The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33$$\%$$ % ) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based methods significantly outperform the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.

Evaluation of Individual Plant Growth Estimation in an Intercropping Field with UAV Imagery

Agriculture practices in monocropping need to become more sustainable and one of the ways to achieve this is to reintroduce intercropping. However, quantitative data to evaluate plant growth in intercropping systems are still lacking. Unmanned aerial vehicles (UAV) have the potential to become a state-of-the-art technique for the automatic estimation of plant growth. Individual plant height is an important trait attribute for field investigation as it can be used to derive information on crop growth throughout the growing season. This study aimed to investigate the applicability of UAV-based RGB imagery combined with the structure from motion (SfM) method for estimating the individual plants height of cabbage, pumpkin, barley, and wheat in an intercropping field during a complete growing season under varying conditions. Additionally, the effect of different percentiles and buffer sizes on the relationship between UAV-estimated plant height and ground truth plant height was examined. A crop height model (CHM) was calculated as the difference between the digital surface model (DSM) and the digital terrain model (DTM). The results showed that the overall correlation coefficient (R2) values of UAV-estimated and ground truth individual plant heights for cabbage, pumpkin, barley, and wheat were 0.86, 0.94, 0.36, and 0.49, respectively, with overall root mean square error (RMSE) values of 6.75 cm, 6.99 cm, 14.16 cm, and 22.04 cm, respectively. More detailed analysis was performed up to the individual plant level. This study suggests that UAV imagery can provide a reliable and automatic assessment of individual plant heights for cabbage and pumpkin plants in intercropping but cannot be considered yet as an alternative approach for barley and wheat.

Characterization and Evaluation of Metarhizium spp. (Metsch.) Sorokin Isolates for Their Temperature Tolerance

A field survey was done in teak (Tectona grandis F.) forests in South India to explore the entomopathogenic effect of Metarhizium anisopliae (Ascomycota: Sordariomycetes) against teak defoliator, Hyblaea puera (Lepidoptera: Hyblaeidae). About 300 soils and infected insect samples were collected during the survey and thirty-six fungal isolates were isolated from soil and insect samples and characterized. The fungi were cultured on PDAY with dodine and antibiotics. Generally, the EPF culture was incubated at 27 °C in darkness for 15 days. Virulence of the Entomopathogenic Fungi (EPF) ability to germinate under cold and heat temperatures was assessed in a culture impregnated with conidia. In the experiment, it was found that for the first time Metarhizium quizhouense, Metarhizium robertsii, and Metarhizium majus species caused significantly higher mortality to hosts. These isolates of M. anisopliae, M. robertsii, M. majus, and M. quizhouense were all considered to be effective virulent and environmentally adaptive. The Metarhizium isolates were recommended as effective bio-control agents through the field investigation of teak defoliator Hyblaea puera from South India forest. This study paves the way to utilize the indigenous isolates of EPF for the control of teak defoliator and to combat the pests thatare resistant to insecticide.

Social Interaction in Public Spaces and Well-Being among Elderly Women: Towards Age-Friendly Urban Environments

The world’s population is aging and becoming more urbanized. Public space in urban areas is vital for improving the health of the elderly by stimulating social interaction. Many urban design projects are advertised as age-friendly but ignore the real needs of the elderly, especially elderly women, for social interaction in urban public spaces. Insufficient attention is paid to the physical and psychological characteristics of elderly women when shaping public space. This analysis addresses the question: What are the qualities of urban spaces which facilitate health-improving social interaction for elderly women? Methods include a case study in Beijing, field investigation, mapping, and qualitative and quantitative analysis. The survey was carried out in April 2021, and concerned 240 women aged 55–75 years. Results indicate that the social interactions of older women relate to both their physical and psychological situations. Public spaces can positively impact the psychological well-being and social participation of elderly women. Conclusions include insights regarding the relationship between social interaction and well-being among elderly women, as well as proposing a series of principles for shaping public spaces for an age-friendly urban environment.

Problems, assumptions and solutions in locomotive design, traction and operational studies

Locomotive design is a highly complex task that requires the use of systems engineering that depends upon knowledge from a range of disciplines and is strongly oriented on how to design and manage complex systems that operate under a wide range of different train operational conditions on various types of tracks. Considering that field investigation programs for locomotive operational scenarios involve high costs and cause disruption of train operations on real railway networks and given recent developments in the rollingstock compliance standards in Australia and overseas that allow the assessment of some aspects of rail vehicle behaviour through computer simulations, a great number of multidisciplinary research studies have been performed and these can contribute to further improvement of a locomotive design technique by increasing the amount of computer-based studies. This paper was focused on the presentation of the all-important key components required for locomotive studies, starting from developing a realistic locomotive design model, its validation and further applications for train studies. The integration of all engineering disciplines is achieved by means of advanced simulation approaches that can incorporate existing AC and DC locomotive designs, hybrid locomotive designs, full locomotive traction system models, rail friction processes, the application of simplified and exact wheel-rail contact theories, wheel-rail wear and rolling contact fatigue, train dynamic behaviour and in-train forces, comprehensive track infrastructure details, and the use of co-simulation and parallel computing. The co-simulation and parallel computing approaches that have been implemented on Central Queensland University’s High-Performance Computing cluster for locomotive studies will be presented. The confidence in these approaches is based on specific validation procedures that include a locomotive model acceptance procedure and field test data. The problems and limitations presented in locomotive traction studies in the way they are conducted at the present time are summarised and discussed.

Burrowing crabs and physical factors hasten marsh recovery at panne edges

Salt marsh loss is projected to increase as sea-level rise accelerates with global climate change. Salt marsh loss occurs along both lateral creek and channel edges and in the marsh interior, when pannes expand and coalesce. Often, edge loss is attributed to erosive processes whereas dieback in the marsh interior is linked to excessive inundation or deposition of wrack, but remains poorly understood. We conducted a two-year field investigation in a central California estuary to identify key factors associated with panne contraction or expansion. Our study explored how an abundant burrowing crab, shown to have strong negative effects on marsh biomass near creek edges, affects panne dynamics. We also explored which physical panne attributes best predicted their dynamics. To our knowledge, ours is the first study of panne dynamics in a California marsh, despite how ubiquitous pannes are as a feature of marshes in the region and how often extensive marsh dieback occurs via panne expansion. Overall, we found that pannes contracted during the study period, but with variable rates of marsh recovery across pannes. Our model incorporating both physical and biological factors explained 86% of the variation in panne contraction. The model revealed a positive effect of crab activity, sediment accretion, and a composite of depth and elevation on panne contraction, and a negative effect of panne size and distance to nearest panne. The positive crab effects detected in pannes contrast with negative effects we detected near creek edges in a previous study, highlighting the context-dependence of top-down and bioturbation effects in marshes. As global change continues and the magnitude and frequency of disturbances increases, understanding the dynamics of marsh loss in the marsh interior as well as creek banks will be critical for the management of these coastal habitats.

Water Turbidity Retrieval Based on UAV Hyperspectral Remote Sensing

The water components affecting turbidity are complex and changeable, and the spectral response mechanism of each water quality parameter is different. Therefore, this study mainly aimed at the turbidity monitoring by unmanned aerial vehicle (UAV) hyperspectral technology, and establishes a set of turbidity retrieval models through the artificial control experiment, and verifies the model’s accuracy through UAV flight and water sample data in the same period. The results of this experiment can also be extended to different inland waters for turbidity retrieval. Retrieval of turbidity values of small inland water bodies can provide support for the study of the degree of water pollution. We collected the images and data of aquaculture ponds and irrigation ditches in Dawa District, Panjin City, Liaoning Province. Twenty-nine standard turbidity solutions with different concentration gradients (concentration from 0 to 360 NTU—the abbreviation of Nephelometric Turbidity Unit, which stands for scattered turbidity.) were established through manual control and we simultaneously collected hyperspectral data from the spectral values of standard solutions. The sensitive band to turbidity was obtained after analyzing the spectral information. We established four kinds of retrieval, including the single band, band ratio, normalized ratio, and the partial least squares (PLS) models. We selected the two models with the highest R2 for accuracy verification. The band ratio model and PLS model had the highest accuracy, and R2 was, respectively, 0.65 and 0.72. The hyperspectral image data obtained by UAV were combined with the PLS model, which had the highest R2 to estimate the spatial distribution of water turbidity. The turbidity of the water areas in the study area was 5–300 NTU, and most of which are 5–80 NTU. It shows that the PLS models can retrieve the turbidity with high accuracy of aquaculture ponds, irrigation canals, and reservoirs in Dawa District of Panjin City, Liaoning Province. The experimental results are consistent with the conclusions of the field investigation.

Analysis of rock mass behavior with Empirical and Numerical method for the construction of diversion tunnel, Laos

Tunneling construction in the mountain area is a challenge for engineers and geotechnicians because of instability due to the presence of discontinuities. The objective of this paper is the modeling of surrounding rock masses for the stability of the diversion tunnel to predict the behavior of rock masses during the excavation process for the Nam Phoun hydropower station project in Laos. Field investigation and laboratories test was realized; Empirical methods as Rock mass designation and Geological Strength Index were performed, rock masses were classified in three categories (RM-1, RM-2, and RM-3); in situ stresses were obtained from existing equations, numerical modeling was performed by the 2D plane strain finite element code Phase2 developed by Rocscience, using Generalized Hoek-Brown criterion for each type of rock masses. The results of numerical modeling show the strength zones of stresses and deformations around the tunnel and predict the instabilities around the tunnel during excavations processes. Thus, for all rock’s masses, it will be necessary to consider an analysis for the supports design before the excavation’s process. The findings of this study allow a clearer understanding of the importance to assess a predictive analysis of slope stability during the feasibility phase of a project by engineers to have an idea of instabilities and its significant in preventing the impact on the cost of the project.