A task level fusion autonomous switching mechanism.

Positioning technology is an important component of environmental perception. It is also the basis for autonomous decision-making and motion control of firefighting robots. However, some issues such as positioning in indoor scenarios still remain inherent challenges. The positioning accuracy of the fire emergency reaction dispatching (FERD) system is far from adequate to support some applications for firefighting and rescue in indoor scenarios with multiple obstacles. To solve this problem, this paper proposes a fusion module based on the Blackboard architecture. This module aims to improve the positioning accuracy of a single sensor of the unmanned vehicles within the FERD system. To reduce the risk of autonomous decision-making of the unmanned vehicles, this module uses a comprehensive manner of multiple channels to complement or correct the positioning of the firefighting robots. Specifically, this module has been developed to fusion a variety of relevant processes for precise positioning. This process mainly includes six strategies. These strategies are the denoising, spatial alignment, confidence degree update, observation filtering, data fusion, and fusion decision. These strategies merge with the current scenarios-related parameter data, empirical data on sensor errors, and information to form a series of norms. This paper then proceeds to gain experience data with the confidence degree, error of different sensors, and timeliness of this module by training in an indoor scenario with multiple obstacles. This process is from data of multiple sensors (bottom-level) to control decisions knowledge-based (up-level). This process can obtain globally optimal positioning results. Finally, this paper evaluates the performance of this fusion module for the FERD system. The experimental results show that this fusion module can effectively improve positioning accuracy in an indoor scenario with multiple obstacles. Code is available at https://github.com/lvbingyu-zeze/gopath/tree/master.

An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image.

Hyperspectral imaging is a key technology for non-destructive detection of seed vigor presently due to its capability to capture variations of optical properties in seeds. As the seed vigor data depends on the actual germination rate, it inevitably results in an imbalance between positive and negative samples. Additionally, hyperspectral image (HSI) suffers from feature redundancy and collinearity due to its inclusion of hundreds of wavelengths. It also creates a challenge to extract effective wavelength information in feature selection, however, which limits the ability of deep learning to extract features from HSI and accurately predict seed vigor. Accordingly, in this paper, we proposed a Focal-WAResNet network to predict seed vigor end-to-end, which improves the network performance and feature representation capability, and improves the accuracy of seed vigor prediction. Firstly, the focal loss function is utilized to adjust the loss weights of different sample categories to solve the problem of sample imbalance. Secondly, a WAResNet network is proposed to select characteristic wavelengths and predict seed vigor end-to-end, focusing on wavelengths with higher network weights, which enhance the ability of seed vigor prediction. To validate the effectiveness of this method, this study collected HSI of maize seeds for experimental verification, providing a reference for plant breeding. The experimental results demonstrate a significant improvement in classification performance compared to other state-of-the-art methods, with an accuracy up to 98.48% and an F1 score of 95.9%.

A comparative study of bacterial diversity based on effects of three different shade shed types in the rhizosphere of Panax quiquefolium L.

BACKGROUND: Shading is an important factor affecting the cultivation of American ginseng, as it influences crop quality and yield. Rhizosphere microorganisms are also crucial for normal plant growth and development. However, whether different shade types significantly change American ginseng rhizosphere microorganisms is unknown. METHODS: This study evaluated the rhizosphere soils of American ginseng under traditional, high flag and high arch shade sheds. High-throughput 16S rRNA gene sequencing determined the change of rhizosphere bacterial communities. RESULTS: The microbial diversity in rhizosphere soils of American ginseng significantly changed under different shading conditions. The bacteria diversity was more abundant in the high arch shade than flat and traditional shades. Different bacterial genera, including Bradyrhizobium, Rhizobium, Sphingomonas, Streptomyces and Nitrospira, showed significantly different abundances. Different shading conditions changed the microbial metabolic function in the American ginseng rhizosphere soils. The three types of shade sheds had specific enriched functional groups. The abundance of ATP-binding cassette (ABC) transporters consistently increased in the bacterial microbiota. These results help understand the influence of shading systems on the rhizosphere microecology of American ginseng, and contribute to the American ginseng cultivation.

[Research progress on saponins, biological activities of saponins, and quality control of Panax quinquefolium].

Panax quinquefolium, as a common precious medicinal plant, has complex chemical components and unique pharmacological activities, which can play a healthcare role in the human body. With the deepening of research, the application of P. quinquefolium has become increasingly extensive. This paper summarized the research progress of the saponins isolated and identified from diffe-rent parts of P. quinquefolium, the structural classification and pharmacological activities of the saponins, and the quality control of Panacis Quinquefolii Radix. Further, this paper put forward the urgent problems to be solved in the development of P. quinquefolium. It is hoped to lay a foundation for the further study and provide reference for the research direction of P. quinquefolium.

Multi-Threshold Image Segmentation of Maize Diseases Based on Elite Comprehensive Particle Swarm Optimization and Otsu.

Maize is a major global food crop and as one of the most productive grain crops, it can be eaten; it is also a good feed for the development of animal husbandry and essential raw material for light industry, chemical industry, medicine, and health. Diseases are the main factor limiting the high and stable yield of maize. Scientific and practical identification is a vital link to reduce the damage of diseases and accurate segmentation of disease spots is one of the fundamental techniques for disease identification. However, one single method cannot achieve a good segmentation effect to meet the diversity and complexity of disease spots. In order to solve the shortcomings of noise interference and oversegmentation in the Otsu segmentation method, a non-local mean filtered two-dimensional histogram was used to remove the noise in disease images and a new elite strategy improved comprehensive particle swarm optimization (PSO) method was used to find the optimal segmentation threshold of the objective function in this study. The experimental results of segmenting three kinds of maize foliar disease images show that the segmentation effect of this method is better than other similar algorithms and it has better convergence and stability.

Improved Q-Learning Algorithm Based on Approximate State Matching in Agricultural Plant Protection Environment.

An Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in the agricultural plant protection such as watering, sowing, and pesticide spraying. It is essential to develop a Decision-making Support System (DSS) for UAVs to help them choose the correct action in states according to the policy. In an unknown environment, the method of formulating rules for UAVs to help them choose actions is not applicable, and it is a feasible solution to obtain the optimal policy through reinforcement learning. However, experiments show that the existing reinforcement learning algorithms cannot get the optimal policy for a UAV in the agricultural plant protection environment. In this work we propose an improved Q-learning algorithm based on similar state matching, and we prove theoretically that there has a greater probability for UAV choosing the optimal action according to the policy learned by the algorithm we proposed than the classic Q-learning algorithm in the agricultural plant protection environment. This proposed algorithm is implemented and tested on datasets that are evenly distributed based on real UAV parameters and real farm information. The performance evaluation of the algorithm is discussed in detail. Experimental results show that the algorithm we proposed can efficiently learn the optimal policy for UAVs in the agricultural plant protection environment.

The forward and backward transport processes in the AOT/hexane reversed micellar extraction of soybean protein.

Soybean protein was taken as a model protein to investigate two aspects of the protein extraction by sodium bis(2-ethylhexyl) sulfosuccinate (AOT) reverse micelles: (1) the forward protein extraction from the solid state, and the effect of pH, AOT concentration, alcohol and water content (W0) on the transfer efficiency; (2) the back-transfer, the capability of the protein to be recovered from the micellar solution. The experimental results led to the conclusion that the highest forward extraction efficiency of soybean protein was reached at AOT concentration 180 mmol l(-1), aqueous pH 7.0, KCl concentration 0.05 mol l(-1), 0.5 % (v/v) alcohol, W0 18. Under these conditions, the forward extraction efficiency of soybean protein achieved 70.1 %. It was noted that the percentage of protein back extraction depended on the salt concentration and pH value. Around 92 % of protein recovery was obtained after back extraction.

Surface characterization of corn stalk superfine powder studied by FTIR and XRD.

Corn stalk superfine powder was ground by a special designed machine. The physical-chemical properties of corn stalk powders with particle sizes of >300, 300-150, 150-74, 74-37 and <15 µm were investigated. The particle size distributions of the powders were: d(90)=362, 147, 74, 40 and 12 µm. The size of corn stalk powders was smaller, the surface area (from 1.188 to 2.432 m(2)/g) and bulk density (from 0.103 to 0.1145 g/ml) were greater. Light microscopy (LM) and scanning electron microscopy (SEM) observations revealed the shape and surface morphology of five types of corn stalk powders. FTIR analysis showed that some position of absorbing peaks was shifted as the powder particle size decreased. X-ray diffraction analyses for corn stalk coarse and superfine powders revealed no evident changes in X-ray pattern. However, the crystallinity, intensity of crystal peaks and crystal size of corn stalk powders with particle sizes from >300 to 300-150 µm dropped, then, as the size of the powders decreased, the crystallinity, intensity of crystal peaks and crystal size increased in some degree.