Relationship of total height and diameter at breast height (hereafter diameter) of the trees is generally nonlinear, and therefore has complex characteristics, which can be accurately described by the height-diameter model developed using the back propagation (BP) neural network approach. The multiple hidden layered-BP neural network has several hidden layers and neurons, and is therefore considered more appropriate modeling approach compared to the single hidden layered-BP neural network approach. However, the former approach is not widely applied for tree height prediction due to absence of the effective optimization method, but it can be done using the BP neural network modeling approach. The poplar (Populus spp. L.) plantation data acquired from Guangdong province of China were used for evaluating the BP neural network modeling approach and compared its results with those obtained from the traditional regression modeling and mixed-effects modeling approaches. We determined the best BP neural network structure with two hidden layers and five neurons in each layer, and logistic sigmoid transfer functions. Relative to the Mitscherlich height-diameter model that had the highest fitting precision among the six traditional height-diameter models evaluated, coefficient of determination (R²) of the neural network height-diameter model increased by 10.3%, root mean squares error (RMSE) and mean absolute error (MAE) decreased by 12% and 13.5%, respectively. The BP neural network height-diameter model also appeared more accurate than the mixed-effects height-diameter model. Our study proposes the method of determining the optimal numbers of hidden layers, neurons of each layer, and transfer functions in the BP neural network structure. This method can be useful for other modeling studies of similar or different types, such as tree crown modeling, height, and diameter increments modeling, and so on.

This editorial highlights the importance of establishing the journal Digital Intelligence in Agriculture and the typical applications of digital intelligence technology in agriculture such as planting industry, forestry, animal husbandry, and fishery. Digital intelligence technology is driving global agriculture towards a new stage of smart agriculture characterized by "data-driven and intelligent decision-making". Its core is to achieve comprehensive empowerment of agricultural production, operation, management, and services through technologies such as the Internet of Things, big data, artificial intelligence, cloud computing, and blockchain. Smart technology aims to achieve multiple goals in agriculture, including cost reduction and efficiency improvement, quality improvement and income increase, resource conservation, and environmental sustainability. It is a key path to address future food security challenges and achieve agricultural modernization.

Regular control of diseases and pests is crucial for maximizing cauliflower yield and quality. Spray application of chemical pesticides causes environmental pollution, pesticide residue accumulation, and is labor-intensive. To address these challenges, we developed an unmanned plant protection device integrating ozone sterilization, light traps, and Internet of Things (IoT) technologies. Installed in a greenhouse, the device included an ozone generator, high-speed fan, and insect traps. It was remotely controlled via a mobile app for real-time adjustments to ozone release, fan speed, trap lamp operation, environmental data collection, and system monitoring. Greenhouse experiments tested the device against cauliflower aphids, Pieris rapae, and black rot. Infestation/infection rates of aphids, Pieris rapae, and black rot in the greenhouse with the device were 23.18%, 19.72%, and 45.83%, respectively—22.52%, 7.21%, and 6.95% lower than the rates in the conventional greenhouse with pesticide sprays. No adverse effects on cauliflower growth were noted, and pesticide use was significantly reduced, lowering both agrochemical and labor costs. The results demonstrate that the unmanned device effectively controls pests and diseases and is safe for use. This offers a bio-friendly solution for pest control in cauliflower production.

Under the dual pressures of climate change and rapid urbanization, a comprehensive analysis of vegetation’s spatiotemporal patterns and their driving forces plays a pivotal role for addressing global ecological challenges. However, systematic bibliometric analyses in this field remain limited. This study involved a comprehensive bibliometric analysis of 18,270 related publications from 1989 to 2024 retrieved from the Web of Science SCI-Expanded database, elucidating research trends, methodologies, and key thematic areas. Utilizing the bibliometrix and biblioshiny tools, the results reveal an annual average growth rate of 17.62% in the number of published research articles, indicating rapid research expansion. Climate change emerged as the core driving force, with high-frequency keywords such as “vegetation”, “dynamics”, and “variability”. China (18,687 papers), the United States (14,502 papers), and Germany (3394 papers) are the leading contributors in this domain, with China showing the fastest growth in research output, albeit with relatively lower average citation rates. Core journals, including Remote Sensing of Environment and Global Change Biology, have played pivotal roles in advancing vegetation dynamics research, with remote sensing techniques dominating the field. The study highlights a shift in vegetation dynamics research from single-variable analyses (e.g., temperature, precipitation) to multi-scale and multidimensional approaches around 2010. Regional studies, such as those focusing on the Loess Plateau, are gaining importance, while advancements in remote sensing and machine learning technologies have enhanced the precision and scalability of research. This study provides a comprehensive summary of the current state and development trends in vegetation spatiotemporal dynamics and their driving forces, offering valuable insights for future research in the field.

The problem of agricultural surface pollution is becoming increasingly prominent, directly impeding the realization of the goals of “industrial prosperity and ecological livability” in the strategy of rural revitalization. To thoroughly analyze the impact of Digital Rural Construction on agricultural surface pollution and to effectively strengthen the prevention and control measures, the Moran index was used to assess the influence of agricultural surface pollution in 31 provinces and cities across China. The Moran index was employed to conduct global and local spatial autocorrelation analysis of agricultural surface source pollution, and a panel quantile autoregressive model was constructed to explore the effects of Digital Rural Construction on such pollution. The results show the following: (1) agricultural surface pollution in each province and city exhibits spatial spillover effects that are growing stronger; (2) the spatial impact of agricultural surface pollution on neighboring provinces and cities follows an inverted U-shaped pattern at different levels of pollution; (3) the relationship between the degree of agricultural surface pollution and the impact of Digital Rural Construction on it also follows an inverted U-shaped pattern, wherein improvements are observed as the pollution levels deepen. When the level of agricultural surface pollution is located in the quartile point 0.1, the improvement effect of Digital Rural Construction on agricultural surface pollution is small (0.0484), as the quartile point increases, the improvement effect is gradually increased, and it reaches the maximum value at the quartile point 0.5 (0.523), and the coefficient of agricultural surface pollution decreases to the minimum value at the quartile point 0.9 (0.423).

Allometric models of internodes are an important component of Functional-Structural Plant Models (FSPMs), which represent the shape of internodes in tree architecture and help our understanding of resource allocation in organisms. Constant allometry is always assumed in these models. In this paper, multilevel nonlinear mixed-effect models were used to characterize the variability of internode allometry, describing the relationship between the last internode length and biomass of Pinus tabulaeformis Carr. trees within the GreenLab framework. We demonstrated that there is significant variability in allometric relationships at the tree and different-order branch levels, and the variability decreases among levels from trees to first-order branches and, subsequently, to second-order branches. The variability was partially explained by the random effects of site characteristics, stand age, density, and topological position of the internode. Tree- and branch-level-specific allometric models are recommended because they produce unbiased and accurate internode length estimates. The model and method developed in this study are useful for understanding and describing the structure and functioning of trees.