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Accurate prediction of regional water evapotranspiration can greatly contribute to the rational utilization of regional water resources for the water resources saving. Crop evapotranspiration can be one of the most important indicators for the water evapotranspiration status of crops, in order to evaluate the soil water balance of farmland and the water management of farmland. However, the calculation of crop evapotranspiration requires a large amount of meteorological factor data. There is often redundant data with the low correlation with the evapotranspiration in meteorological data, which seriously affects the prediction accuracy and efficiency of the model. It is a high demand to extract the core environmental factors for the less-factor prediction model. The classification and regression tree (CART) can be used to make feasible for the large data sources in a short time. Then, Less factors can be extracted for the predictive analysis in a more scientific way. In this study, a less-factor water evapotranspiration model was constructed to efficiently and accurately predict the evapotranspiration data. The core factors were selected from multiple meteorological factors. 23 typical stations were selected in the nine agricultural regions, and then collected data on eight meteorological factors, such as precipitation, and sunshine hours. The meteorological factors were ranked in the order of importance using CART. The top 3-5 meteorological factors were then selected to predict the evapotranspiration using the extreme learning machine (ELM) model. At the same time, the ELM model was optimized using genetic algorithm (GA), particle swarm optimization (PSO), sparrow search algorithm (SSA). Three optimization algorithms (GA-ELM, PSO-ELM, SSA-ELM) were used to construct a less-factor hybrid optimization water evapotranspiration prediction model. Relative root mean square error (RMSE), coefficient of determination (R 2 ), mean absolute error (MAE), and NashSutcliffe coefficient (NSE) were used to evaluate the performance of the ELM and optimization model. The results showed that: 1) The main influencing factors were ranked in the order of the precipitation, sunshine duration, mean station pressure, daily maximum temperature, and average relative humidity using the importance ranking of the CART algorithm. 2) The PSOELM model presented the highest prediction accuracy among the three optimization algorithms. Specifically, the RMSE of evapotranspiration prediction for the 23 stations was ranged from 6.608 to 22.077 mm/d, the NSE of 0.824-0.998, R 2 of 0.908- 0.995, and MAE of 5.075-16.677 mm/d. In addition, the RF and SVR model with the strong generalization performance were selected to compare with the PSO-ELM model. The prediction performance of three models was slightly improved with the increase of the input factors, indicating the slight overall improvement. The three models shared the strong generalization and robustness. The meteorological factors were input into the prediction model, according to the order of importance. The meteorological factors ranked the 4th and 5th were relatively less important, where the prediction accuracy was slightly improved with the increase of the input parameters. The PSO-ELM model presented the highest prediction accuracy among the three models. 3) The ELM model performed better applicability in the Yunnan-Guizhou Plateau region, the Sichuan Basin, and the surrounding areas. The three optimization algorithms showed the better applicability in the South China and the YunnanGuizhou Plateau region, with the highest applicability of the PSO-ELM model. The findings can provide an important reference for the crop water demand calculation in nine major agricultural regions in China. [ABSTRACT FROM AUTHOR]

An agricultural robot system has been ever increasing to develop rapidly in the world, with the progress of the Internet of Things, big data, and artificial intelligence. This study aims to analyze the development of the global agricultural robot system using the domain knowledge map from the Web of Science core data and visualization software. Research findings were as follows: 1) Four stages were roughly divided in the agricultural robot research. 2) The United States, China, Spain, Germany, Japan, Italy, and the United Kingdom presented the most research outputs, and the closest cooperation was found between China and the United States, the United States and Spain, China and Japan. 3) The agricultural robot research in China was about 10 years later than that of the United States, but there was a rapid increase in the most achievements “quantity and quality”, especially in the fruit picking robot. The key technologies, such as stereo vision technology, have been reported almost simultaneously in China and the United States. 4) The development of the agricultural robot system followed the following path: from simple to complex, and gradually to the multi-functional, intelligent autonomous unmanned system. Correspondingly, the development of the agricultural robot system from the birth, shape and growth to a period of rapid development, was great benefit from the big data, artificial intelligence, internet of things, cloud computing, mobile Internet, as well as the new generation of information and communication technology. Now, the agricultural robot has stepped into a period of accelerated development in the new era. The future agricultural robot can be more smart and intelligent, from one single function to the integration of intelligent perception, intelligent decision-making, and intelligent execution of agricultural robot system. The more extensive development of agricultural robots can be extended from the special robots to multi-functional mechanization, automation, and intellectualization suitable for the agricultural production, particularly from the whole mechanization of the same crop production to the comprehensive large-scale intensive mechanization of different varieties of crops development needs. The application of agricultural robots has been developed from a single robot to a group operation system with the intelligent cooperation of multiple robots, such as swarm UAV, harvesting robot group, robot group, and robot team. The application scenarios have been more efficient and competitive in the large-scale cooperative operation of agricultural robots. The unmanned intelligent agricultural robot can be the development trend to cope with the agricultural labor shortage and machine generation. Man-machine cooperation and man-machine integration can also bring the respective strengths of new farmers and agricultural robots to achieve the common growth of man-machine. Consequently, some recommendations were made to strengthen basic research, and break through the restrictions and blockades of algorithms, models, materials, and processes, particularly for the high-level scientific and technological self-reliance in the field of agricultural robots. It is very necessary to accelerate the implementation of the development strategy of agricultural robots during this time. The level of intelligence of agricultural robots needs to be further improved for the stability, high quality, and high efficiency of operating performance suitable for various operating environments. In addition, the decision making on the supportive and guiding policies and the investment can also be critical to strengthen the research and talent team, thereby promoting sustainable and healthy development in the application of agricultural robot technology and equipment. [ABSTRACT FROM AUTHOR]

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Rural revitalization strategy is a hot topic in China. How to ensure the steady advancement of rural revitalization strategy and its effect of rural revitalization needs to evaluate the level of rural revitalization. Therefore, Construction of a scientific and reasonable evaluation index system for rural rejuvenation level is of great significance for the monitoring and evaluation of rural rejuvenation level. This study uses five aspects indexes of prosperity of industry, ecological livability, rural civilization, effective governance, and wealthy life to construct a evaluation index system of rural revitalization level based on statistical analysis of the evaluation indexes in the literature and frequency analysis, combined with expert consultation and field surveys, and the indexes are comprehensive grain production capacity; agricultural science and technology progress contribution rate; agricultural labor productivity; proportion of agricultural mechanization; level of agricultural informatization; village green coverage; waste treatment rate; rural sanitary toilet penetration rate; days of air quality compliance;percentage of expenditure on education, culture and entertainment of rural residents; average years of education of rural population; proportion of full-time teachers with bachelor degree or above in primary and secondary schools; village affairs open rate; rural gini coefficient; rural governance villager participation rate; income ratio of urban and rural residents; engel coefficient of rural residents; and per capita disposable income of rural residents. An empirical study is carried out with 5 towns (Anzhuang, Wangzhuang, Taoyuan, Wenyang and Sunbo) in Feicheng City, Shandong Province. The weight of each evaluation index for the evaluation of the rural revitalization level of each township is determined based on the analytic hierarchy process and Delphi method. A comprehensive evaluation method is used to establish an evaluation model of rural revitalization level and calculate the rural revival level index of each township. The rural revitalization index of Anzhuang Town, Wangzhuang Town, Taoyuan Town, Wenyang Town and Sunbo Town are 0.850 7, 0.848 8, 0.844 2, 0.811 2, 0.781 0 respectively. Anzhuang Town, Wangzhuang Town, Taoyuan Town, and Wenyang Town have a relatively high level of rural revitalization. The rural revitalization level of Sunbo Town is lower than the above 4 towns and needs to be improved. In the context of the implementation of the rural revitalization strategy, the research on the evaluation indexes and models of rural revitalization level is not only important for scientifically measuring the level of rural revitalization, but also for monitoring and early warning and correcting the level of rural revitalization, and it would play a positive role to promote the implementation of strategy. [ABSTRACT FROM AUTHOR]

Under the background of implementing the rural revitalization strategy to promote the development of digital agriculture and digital village, the evaluation of agricultural informatization level is a hot issue in domestic agriculture. At present, the evaluation index system of agricultural informatization level determined by different scholars in China is quite different. This study analyzes and discusses the evaluation indexes of China's agricultural informatization level, and discusses the characteristics and regulation of China's agricultural informatization level evaluation indexes. The research finds that the indexes of the agricultural informatization level have obvious characteristics of the times. The indexes of the agricultural informatization level are too many, too extensive, and have the synonymous features when names are different. Some evaluation indexes have poor measurability, and the connotation is too general and broad. Some indexes are outdated, and there are also new indexes reflecting the application of new technologies not considered. Therefore, in the process of implementing the rural revitalization strategy, it is of great significance to formulate agricultural informatization level evaluation indexes that are unified, standardized, scientific and reasonable, and advancing with the times for the evaluation, monitoring and early warning of agricultural informatization level. On the basis of comprehensive predecessors' research, considering agricultural informatization resources and informatization technology applications, agricultural informatization infrastructure, agricultural informatization industry, agricultural informatization human resources, and agricultural informatization technology policy and environment，a set of relatively scientific and reasonable evaluation index system of agricultural informatization level is constructed, and the analytic hierarchy process is used to determine the weight of each index, which provide reference for the evaluation of agricultural informatization level. [ABSTRACT FROM AUTHOR]