With the continuous development of the Internet of Things, artificial intelligence, big data technology, and intelligent agriculture have become hot topics in agricultural science and technology research. Machine learning is one of the core topics in artificial intelligence, and its application has penetrated every aspect of human social life. In modern agricultural intelligent management and decision making, machine learning plays an important role in crop classification, crop disease and insect pest prediction, agricultural product price prediction, and other aspects of management and decision-making processes in agriculture. To detect and recognize the latest research developing features in a quantitative and visual way, and based on machine learning methods in agricultural management, the authors of this paper used CiteSpace bibliometric methods to analyze relevant studies on the development process and hot spots. High-value references, productive authors, country and institution distributions, journal visualizations, research topics, and emerging trends were reviewed and analyzed. According to the keyword visualization and high-value references, machine learning approaches focus on sustainable agriculture, water resources, remote sensing, and machine learning methods. The research mainly focuses on six topics: learning technology, land environment, reference evapotranspiration, decision support systems for river geography, soil management, and winter wheat, while learning technology has been the most popular in recent years.

The flourish of the Internet of Things (IoT) and data-driven techniques provide new ideas for enhancing agricultural production, where evapotranspiration estimation is a crucial issue in crop irrigation systems. However, tremendous and unsynchronized data from agricultural cyber–physical systems bring large computational costs as well as complicate performing conventional machine learning methods. To precisely estimate evapotranspiration with acceptable computational costs under the background of IoT, we combine time granulation computing techniques and gradient boosting decision tree (GBDT) with Bayesian optimization (BO) to propose a hybrid machine learning approach. In the combination, a fuzzy granulation method and a time calibration technique are introduced to break voluminous and unsynchronized data into small-scale and synchronized granules with high representativeness. Subsequently, GBDT is implemented to predict evapotranspiration, and BO is utilized to find the optimal hyperparameter values from the reduced granules. IoT data from Xi’an Fruit Technology Promotion Center in Shaanxi Province, China, verify that the proposed granular-GBDT-BO is effective for cherry tree evapotranspiration estimation with reduced computational time, and acceptable and robust predictive accuracy. Consequently, the precise estimation of crop evapotranspiration could provide operational guidance for plant irrigation, plant conservations, and pest control in the agricultural greenhouse.

Internet finance is a financial mode combining traditional financial industry with Internet technologies, which has become a crucial part of the financial field. Due to the rapid change of information technologies and public financial needs, Internet finance has produced quite a few specific operation modes, which have interested many scholars. To better appreciate its development process and innovation modes, we used bibliometrics to analyze 2,877 articles on Internet finance in Web of Science. Through the co-word network, co-citation network and various results generated by CiteSpace, we recognized six main modes of Internet finance, that is, Internet bank, peer to peer lending (P2P lending), crowdfunding, big data finance, digital currency and fintech. Emerging research topics and the development history of each mode are also detected. We find that the mainstream modes in current research are P2P lending and crowdfunding and the research on fintech and digital currency has just begun. Through the review, we also suggest some research directions for the research direction of each mode. These results will help to deepen relevant scholars’ understanding of Internet finance and provide guidance for them to choose research directions.

In the wake of the COVID-19 pandemic, selling by short video has become a new online selling model that enhances the communication between buyers and sellers. Therefore, it is necessary to identify the key factors influencing consumers’ purchase of agricultural products on short-video platforms. Additionally, it is also important to figure out the influencing mechanism and action path. Specifically, based on the ‘Stimulus-Organism-Response (SOR)’ framework and structural equation model, we delineate and empirically test hypotheses regarding the effects of key components on consumers’ purchase intentions and behaviors. The key components refer to three external stimuli of consumers’ social network, sellers’ resource endowment, and both sides’ infrastructure development levels. Simultaneously, we analyze the mediating role of consumers’ perceived value and perceived risk between external stimuli and consumers’ purchase intentions. This paper argues that short-video merchants improving the influence of their stores and platforms strengthening supervision and management are the keys to ensuring stable growth in consumers’ willingness to purchase agricultural products sold on short videos and promoting the development of the short-video live industry.

The decision-making process of choosing an ideal tourism destination is influenced by a number of psychological and nonpsychological variables. Tourists need a method to quickly and easily select a suitable destination. Driven by this practical decision issue, a novel approach of tourist destination evaluation, grey relation analysis (GRA), is developed and applied to the ranking evaluation of Taiwan tourism destinations in China. In the evaluating process, we apply entropy to calculate the weight of each index, which is a more objective method of calculating weights. The results of the study indicate that although the same size is small and the distribution of data is unknown, GRA can still be successfully used in evaluating tourist destinations. In addition, we compare the GRA results with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and show that more accurate ranking results can be obtained.

Fertigation technology is key to solve water pollution and inefficient fertilizer use. However, some early techniques cannot adapt to the current situation of labor shortages and large-scale planting. Therefore, it is necessary to consider farmers’ willingness to adopt more adaptive techniques. Specifically, we focus on whether early technology adoption will hinder technology renewal and whether the factors affecting the adoption of early and latest techniques are consistent. Through theoretical analysis and a survey, we find that farmers’ endowments such as income and labor force only affect the adoption intentions to the high-cost technique (Intelligent Irrigation Control System), but not early techniques (Venturi injector and Differential pressure tank), while farmers’ information processing ability and information acquisition channels affect both. Finally, the results of Propensity Score Matching show that early technology adoption will not become an obstacle to technology renewal.