Blockchain Adoption in Agricultural Supply Chain for Better Sustainability: A Game Theory Perspective

Within the context of the rise of the Internet of Things, blockchain, and other new technologies, telecommunications operators are committed to applying technologies to promote business transformation and upgrading. The government also actively applies technologies to traditional fields to promote social progress. In agriculture, the agricultural supply chain has a low information level and low degree of digitization. The application of blockchain technology in agriculture offers exceptional advantages because of its decentralization, openness, and transparency. Based on the application of blockchain in an agricultural scenario, an evolutionary game model made up of governments, telecom operators, and agricultural enterprises was established to analyze the model’s equilibrium stability and evolutionary stable strategy. Then, numerical simulation was carried out to study the influence of the initial green level, equipment deployment cost, technology operation cost, and other core factors on the tripartite evolution behaviour. The results show that each factor influences the behaviour of a third party in different ways. Finally, according to the simulation results, this paper puts forward practical suggestions, explores the long-term impact of the application cost and sustainable income of blockchain technology on cooperation, and provides new ideas for the governance of China’s traditional fields from the perspective of new technology application

Integration of Multiple Spectral Indices and a Neural Network for Burned Area Mapping Based on MODIS Data

Since wildfires have occurred frequently in recent years, accurate burned area mapping is required for wildfire severity assessment and burned land reconstruction. Satellite remote sensing is an effective technology that can provide valuable information for wildfire assessment. However, the common approaches based on using a single satellite image to promptly detect the burned areas have low accuracy and limited applicability. This paper develops a new burned area mapping method that surpasses the detection accuracy of previous methods, while still using a single Moderate Resolution Imaging Spectroradiometer (MODIS) sensor image. The key innovation is integrating optimal spectral indices and a neural network algorithm. We used the traditional empirical formula method, multi-threshold method and visual interpretation method to extract the sample sets of five typical types (burned area, vegetation, cloud, bare soil, and cloud shadow) from the MODIS data of several wildfires in the American states of Nevada, Washington and California in 2016. Afterward, the separability index M was adopted to assess the capacity of seven spectral bands and 13 spectral indices to distinguish the burned area from four unburned land cover types. Based on the separability analysis between the burned area and unburned areas, the spectral indices with an M value higher than 1.0 were employed to generate the training sample sets that were assessed to have an overall accuracy of 98.68% and Kappa coefficient of 97.46%. Finally, we utilized a back-propagation neural network (BPNN) to learn the spectral differences of different types from the training sample sets and obtain the output burned area map. The proposed method was applied to three wildfire cases in the American states of Idaho, Nevada and Oregon in 2017. A comparison of detection results between the new MODIS-based burned area map and the reference burned area map compiled from Landsat-8 Operational Land Imager (OLI) data indicates that the proposed method can effectively exploit the spectral characteristics of various land cover types. Also, this new method can achieve higher accuracy with the reduction of commission error (CE, >10%) and omission error (OE, >6%) compared to the traditional empirical formula method. The new burned area mapping method could help managers and the public perform more effective wildfire assessments and emergency management

Optimization of Solid-State Fermentation Conditions of <i>Quercus liaotungensis</i> by <i>Bacillus subtilis</i>

The current study aimed to investigate the solid-state fermentation process of Quercus liaotungensis (QL) by Bacillus subtilis (BS). The parameters included the inoculation amount, the soybean meal addition amount, the fermentation temperature and the ratio of material to water. The optimal process was determined based on the nutritional value, tannin content and DPPH clearance of QL after fermentation. The results showed that: (1) The parameters of the optimal process included inoculating 106 BS per gram of QL, then adding 10% soybean meal, the ratio of material to the water of 100:80, and temperature at 33 °C for 72 h. (2) In the optimum fermentation conditions, the crude fiber content, and the ether extract content of QL decreased by 66.94% and 66.96%, respectively (p p p p < 0.05) after fermentation, respectively. In summary, the QL significantly improved the nutritional value after the solid-state fermentation with BS

Gold Nanoparticle-Based Enzyme-Linked Antibody-Aptamer Sandwich Assay for Detection of <i>Salmonella</i> Typhimurium

Enzyme-linked immunosorbent assay (ELISA) provides a convenient means for the detection of <i>Salmonella enterica</i> serovar Typhimurium (STM), which is important for rapid diagnosis of foodborne pathogens. However, conventional ELISA is limited by antibody–antigen immunoreactions and suffers from poor sensitivity and tedious sample pretreatment. Therefore, development of novel ELISA remains challenging. Herein, we designed a comprehensive strategy for rapid, sensitive, and quantitative detection of STM with high specificity by gold nanoparticle-based enzyme-linked antibody-aptamer sandwich (nano-ELAAS) method. STM was captured and preconcentrated from samples with aptamer-modified magnetic particles, followed by binding with detector antibodies. Then nanoprobes carrying a large amount of reporter antibodies and horseradish peroxidase molecules were used for colorimetric signal amplification. Under the optimized reaction conditions, the nano-ELAAS assay had a quantitative detection range from 1 × 10³ to 1 × 10⁸ CFU mL^–1, a limit of detection of 1 × 10³ CFU mL^–1, and a selectivity of >10-fold for STM in samples containing other bacteria at higher concentration with an assay time less than 3 h. In addition, the developed nanoprobes were improved in terms of detection range and/or sensitivity when compared with two commercial enzyme-labeled antibody signal reporters. Finally, the nano-ELAAS method was demonstrated to work well in milk samples, a common source of STM contamination

Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments

This data repository hosts relevant data to our paper with the same name. Paper Abstract: This paper proposes a UAV platform that autonomously detects, hunts, and takes down other small UAVs in GPS-denied environments. The platform detects, tracks, and follows another drone within its sensor range using a pre-trained machine learning model. We collect and generate a 58,647-image dataset and use it to train a Tiny YOLO detection algorithm. This algorithm combined with a simple visual-servoing approach was validated on a physical platform. Our platform was able to successfully track and follow a target drone at an estimated speed of 1.5 m/s. Performance was limited by the detection algorithm’s 77% accuracy in cluttered environments and the frame rate of eight frames per second along with the field of view of the camera

Autonomous drone hunter operating by deep learning and all-onboard computations in GPS-denied environments.

This paper proposes a UAV platform that autonomously detects, hunts, and takes down other small UAVs in GPS-denied environments. The platform detects, tracks, and follows another drone within its sensor range using a pre-trained machine learning model. We collect and generate a 58,647-image dataset and use it to train a Tiny YOLO detection algorithm. This algorithm combined with a simple visual-servoing approach was validated on a physical platform. Our platform was able to successfully track and follow a target drone at an estimated speed of 1.5 m/s. Performance was limited by the detection algorithm's 77% accuracy in cluttered environments and the frame rate of eight frames per second along with the field of view of the camera