[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-462":3,"related-462":82},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":76,"slug":79,"view_count":80,"doi":8,"paper":8,"created_at":81},462,"Application of Artificial Intelligence and Machine Learning in Vertical Farming: A Comprehensive Review","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261","本文系统综述了2015-2025年期间208篇垂直农业(VF)同行评审研究,探讨AI、ML与IoT如何重塑VF生态系统,聚焦计算机视觉病害识别、作物生长与产量预测、智能气候控制、精准营养与灌溉管理。综述涵盖CNN、随机森林、XGBoost、LSTM等算法在水培、气培、鱼菜共生系统中的性能,以及多传感器网络IoT架构、边-云计算与自动化控制系统。商业农场展示出资源效率提升与供应链缩短,但仍面临能耗(LED占40%-60%成本)、高初始投资、数据集分散、实地验证不足等挑战;许多>95%的高精度来自实验室条件。原文标题:Application of Artificial Intelligence and Machine Learning in Vertical Farming: A Comprehensive Review,作者:Kim M Y、Park G、Seo C H(韩国公州国立大学),发表在MDPI Sustainability 2026年第18卷第16期8261。",null,"## 1. Introduction\n\nGlobal food production systems face a convergence of pressures that seriously threaten the long-term sustainability of agriculture. Soil fertility is declining owing to the combined effects of global warming, indiscriminate use of agrochemicals, recurrent natural disasters, and rapid urbanization [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B1-sustainability-18-08261),[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B2-sustainability-18-08261),[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B3-sustainability-18-08261)]. Together, these factors have led to widespread soil degradation and a steady reduction in per capita arable land. Climate change is accelerating these problems, further undermining the viability of agricultural systems worldwide [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B4-sustainability-18-08261),[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B5-sustainability-18-08261)]. Anthropogenic carbon emissions, primarily from fossil fuel combustion, deforestation, and desertification, remain the main drivers of environmental degradation [[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B6-sustainability-18-08261),[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B7-sustainability-18-08261)]. Simultaneously, water scarcity has become a critical bottleneck. Falling groundwater levels, inefficient irrigation practices, and widespread contamination are increasing pressure on watersheds and agricultural freshwater supplies [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B7-sustainability-18-08261)]. These environmental challenges are unfolding alongside the rapid growth of the population. Projections suggest that the global population will reach 9.7–9.8 billion by 2050, requiring a sustainable increase in agricultural output of 50–60% to meet nutritional needs [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B8-sustainability-18-08261),[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B9-sustainability-18-08261)]. However, this expansion is severely limited by the ongoing loss of arable land. According to the Food and Agriculture Organization (FAO), per capita arable land is expected to fall below 0.20 ha by 2050, which is less than one-third of the 1970 level [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B8-sustainability-18-08261),[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B10-sustainability-18-08261)]. Alarmingly, arable land is disappearing at a rate of approximately 23 hectares per minute, highlighting the urgent need for better land and resource management to secure future food supplies.\n\nMeanwhile, food production has become increasingly detached from urban consumers because of the demands of long-distance transport and distribution networks. This global food transportation system accounts for approximately 29% of total food-related greenhouse gas emissions [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B11-sustainability-18-08261)]. Urban resilience, the ability of infrastructure systems to anticipate, absorb, and recover from disruptions, varies widely across regions. North America and Europe generally show the highest levels, whereas many African nations remain particularly vulnerable [[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B12-sustainability-18-08261),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B13-sustainability-18-08261)]. Emerging environmental stresses continue to weaken this resilience, which involves a system’s capacity to mitigate, adapt to or recover from major disturbances [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B14-sustainability-18-08261),[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B15-sustainability-18-08261),[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B16-sustainability-18-08261),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B17-sustainability-18-08261)].\n\nVertical farming (VF) presents a compelling alternative to address these intertwined environmental and logistical challenges. By substantially reducing land and water use, shortening supply chains, and lowering carbon emissions, VF support the broader goals of ecological preservation [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B18-sustainability-18-08261),[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B19-sustainability-18-08261),[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B20-sustainability-18-08261),[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B21-sustainability-18-08261)]. At this pivotal moment, combining sustainable VF practices with advanced technologies, such as artificial intelligence (AI), offers genuine transformative potential [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B22-sustainability-18-08261)]. Exploring the synergies between AI and VF could not only boost productivity but also help shift agriculture toward more ecologically sound and resource-efficient models [[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B23-sustainability-18-08261),[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B24-sustainability-18-08261)]. This shift also holds promise for improving food safety and public health through better urban agricultural practices.\n\nUrban agriculture encompasses various approaches, including greenhouse, container, rooftop, and warehouse farming [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B25-sustainability-18-08261)]. Among these, VF has attracted considerable attention because of its ability to produce high-value crops with high productivity and a relatively low ecological footprint [[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B26-sustainability-18-08261)]. In VF, crops are grown in multiple vertically stacked layers, often in underutilized urban spaces or on rooftops under tightly controlled environmental conditions [[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B27-sustainability-18-08261)]. The modern concept traces back to Othmar Ruthner’s 1966 proposal for multi-story food production [[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B28-sustainability-18-08261)], but Dickson Despommier provided a comprehensive vision in 2009, proposing climate-controlled, multi-tiered buildings for horizontal or vertical crop growth [[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B27-sustainability-18-08261),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B29-sustainability-18-08261),[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B30-sustainability-18-08261)]. By making efficient use of limited space and resources, VF offers a practical route to greater food security and environmental sustainability [[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B31-sustainability-18-08261)]. Its key benefits include year-round production of fresh produce, shorter transportation distances, reduced spoilage, and a significantly smaller carbon footprint than conventional supply chains [[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B30-sustainability-18-08261)].\n\nThe economic importance of VF is evident in its growing market share. Valued at approximately USD 5.1 billion in 2023, the sector is projected to reach USD 35 billion by 2032 [[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B32-sustainability-18-08261)]. [Figure 1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#fig_body_display_sustainability-18-08261-f001) shows these projections broken down by cultivation technology (hydroponics, aeroponics, and aquaponics), alongside a conceptual overview of the key components of a typical VF system, including soilless cultivation, nutrient delivery, automation, irrigation, lighting, CO 2 control, and environmental regulation. Regionally, East and Southeast Asia, along with North America (particularly China, Japan, Singapore, Republic of Korea, USA, and Canada), currently hold a dominant position, accounting for over 33% of the global market [[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B31-sustainability-18-08261)]. Europe and the Middle East have emerged as important players in recent years [[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B32-sustainability-18-08261)].\n\n**Figure 1.** (**a**) Global VF market size projections from 2022 to 2032 segmented by cultivation technology (hydroponics, aeroponics, and aquaponics), indicating strong growth trends and technology adoption patterns. (**b**) Conceptual framework of a VF system showing key operational components, including structure, soilless cultivation, water–nutrient delivery, automation, irrigation, lighting, CO 2 control, and environmental regulation subsystems.\n\nAt the country level, Japan operates around 200 VF facilities producing crops such as lettuce, broccoli, tomatoes, and bell peppers, led by companies such as Spread Co., Ltd. (Kyoto) and Mirai Co., Ltd. (Gifu) [[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B33-sustainability-18-08261)]. China has more than 250 sites, with SANANBIO cultivating over 300 varieties of leafy greens, fruits, medicinal herbs, and flowers. Republic of Korea has built advanced infrastructure through firms such as N.Thing, NextOn, Farm 8, and ALGA Farmtech [[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B12-sustainability-18-08261),[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B33-sustainability-18-08261)], including a prototype in Suwon that incorporates renewable energy for heating, cooling, and lighting [[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B34-sustainability-18-08261)]. In the United States, innovators such as 80 Acres Farms, AeroFarms, AmHydro, BrightFarms, Hort Americas, Plenty, and The Plant are at the forefront. Europe has witnessed accelerating adoption driven by consumer demand for fresh, local produce. The Netherlands leads with projects such as PlantLab, UrbanFarmers, and B-Four Agro, followed by initiatives such as Agricool in Paris and Infarm in Berlin [[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B35-sustainability-18-08261)]. Germany’s Food4Future (f4f) program stands out for integrating the production of algae and insects with traditional crops [[36](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B36-sustainability-18-08261)].\n\nVF marks a significant shift in agronomic science, enabling crops to be grown in vertical systems within controlled indoor environments, such as warehouses, repurposed containers, or dedicated facilities. This approach helps address many drawbacks of conventional agriculture, including high land and water requirements, heavy reliance on agrochemicals, and substantial CO 2 emissions [[37](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B37-sustainability-18-08261),[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B38-sustainability-18-08261)]. With the intensification of urbanization and the reduction in arable land, VF offers a sustainable method for the continuous production of food in proximity to urban centers. A core feature of VF is its dependence on Controlled Environment Agriculture (CEA), which allows precise management of temperature, humidity, CO 2 levels, and light to optimize plant growth [[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B38-sustainability-18-08261),[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B39-sustainability-18-08261),[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B40-sustainability-18-08261),[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B41-sustainability-18-08261),[42](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B42-sustainability-18-08261),[43](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B43-sustainability-18-08261)]. While this level of oversight assures superior yields and uniformity, it concurrently introduces intricacies in operational oversight and data handling. Although this control yields higher productivity and uniformity, it also creates considerable operational and data management complexity. AI and machine learning (ML) have become essential tools [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B41-sustainability-18-08261)].\n\nAI and ML are reshaping numerous fields by extracting insights from large datasets, uncovering hidden patterns, and supporting autonomous decision-making with limited human input [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B41-sustainability-18-08261),[44](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B44-sustainability-18-08261)]. In VF, these technologies can enhance environmental control, crop monitoring, maintenance, and farming logistics. AI systems can process data from sensors and imaging devices to support real-time decisions that improve crop health, reduce resource use, and boost overall efficiency [[44](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B44-sustainability-18-08261),[45](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B45-sustainability-18-08261)]. ML algorithms are particularly useful for the early detection of diseases, nutrient deficiencies, and pests, allowing timely interventions that limit yield loss. They also aid in yield prediction, harvest scheduling, and labor automation [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B41-sustainability-18-08261),[43](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B43-sustainability-18-08261),[46](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B46-sustainability-18-08261)]. As big data analytics and edge computing become more accessible, AI and ML solutions are becoming increasingly scalable and cost-effective for farms of different sizes.\n\nNevertheless, the integration of AI and ML into VF remains in its early stages and faces several barriers. These include the need for large, high-quality training datasets, significant upfront investments, and a lack of standardized data interoperability platforms. Successful adoption also requires interdisciplinary expertise in agronomy and data science, which can be challenging for many operators. The present review critically evaluates the current landscape of AI and ML applications in VF, discussing the technologies used, their strengths and limitations, and the likely directions for future intelligent agricultural systems.\n\n### 1.1. Significance of the Present Review\n\nAlthough the existing literature has thoroughly examined the general applications and benefits of VF [[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B6-sustainability-18-08261),[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B18-sustainability-18-08261),[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B19-sustainability-18-08261),[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B32-sustainability-18-08261),[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B40-sustainability-18-08261),[47](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B47-sustainability-18-08261),[48](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B48-sustainability-18-08261),[49](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B49-sustainability-18-08261),[50](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B50-sustainability-18-08261),[51](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B51-sustainability-18-08261),[52](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B52-sustainability-18-08261),[53](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B53-sustainability-18-08261),[54](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B54-sustainability-18-08261),[55](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B55-sustainability-18-08261),[56](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B56-sustainability-18-08261),[57](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B57-sustainability-18-08261),[58](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B58-sustainability-18-08261),[59](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B59-sustainability-18-08261),[60](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B60-sustainability-18-08261)], there remains a notable gap in the comprehensive integration of modern AI and IoT technologies in the specific context of vertical farming. Many previous reviews are somewhat fragmented: some focus on ML and IoT in the broader context of precision agriculture but pay limited attention to the unique characteristics of VF [[55](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B55-sustainability-18-08261)], while others explore smart farming technologies without closely linking them to high-density vertical systems [[53](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B53-sustainability-18-08261),[61](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B61-sustainability-18-08261)]. Isolated case studies, such as the application of Random Forest for energy optimization in a single hydroponic setup [[62](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B62-sustainability-18-08261)], rarely address issues of scalability, system integration, or commercial realities at larger scales. This scattered research landscape calls for a more focused and integrated review that specifically connects AI, ML, and IoT to VF.\n\nFurthermore, questions persist regarding the long-term ecological and economic sustainability of VF. Its dependence on energy-intensive infrastructure, potential land use conflicts, and the need for renewable energy integration raise valid concerns regarding its overall environmental benefits [[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B40-sustainability-18-08261),[59](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B59-sustainability-18-08261),[60](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B60-sustainability-18-08261)]. Without careful planning and efficiency improvements, VF’s sustainability claims could be undermined. Sensor integration in hydroponic systems allows for the real-time monitoring and control of water levels, temperature, and humidity, whereas advanced algorithms enable responsive environmental adjustments [[48](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B48-sustainability-18-08261),[63](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B63-sustainability-18-08261)]. Smart controls can reduce unnecessary interventions, save energy, and improve the responsiveness of the system. Some studies have reported notable savings, such as 20.4% in water and temperature management and 82.1% in lighting energy [[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#B24-sustainability-18-08261)], but these findings are often context-specific and require broader validation across different settings.\n\nThis review seeks to bridge the knowledge gap by critically examining the integration of AI, ML, and IoT with VF. Unlike previous reviews, which generally addressed smart agriculture, the present study provides a comprehensive, VF-focused review of innovative systems designed to extend automation, enhance energy efficiency, reduce operating costs, and optimize environmentally friendly production practices.\n\nThe significant characteristics of this review paper are as follows:\n\n*   A systematic, PRISMA-guided methodology for literature selection and synthesis promotes transparency and reproducibility.\n\n*   A detailed analysis of IoT infrastructure and ML techniques tailored to VF, with attention to practical implementation, integration challenges, and real-world constraints, is presented.\n\n*   A comparative evaluation of AI\u002FML performance across different vertical farming setups, considering the suitability for various tasks, data conditions, and edge-computing environments.\n\n*   A critical assessment of commercial case studies, distinguishing verified results from company claims while evaluating economic viability, energy use, and operational risks.\n\n*   A structured discussion of key limitations, research gaps, and future directions is organized around four main pillars: data and interoperability, algorithmic advances, energy and life cycle assessment, and economic policy frameworks.\n\n### 1.2. Outline of This Paper\n\nThe manuscript is structured thematically to guide readers from foundational ideas to advanced applications and real-world examples of the technology. [Section 2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#sec2-sustainability-18-08261) describes the review methodology. [Section 3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#sec3-sustainability-18-08261) introduces the key concepts of VF and controlled-environment agriculture. [Section 4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8261#sec4-sustainability-18-08261) provides a technical overview of IoT architectures and multilayer sensing, communication, and processing models in smart VF systems, highlighting the roles of sensor networks,","MDPI Sustainability 2026-08-12","2026-08-12T00:00:00Z","论文",10,false,84,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},18,22,20,14,1,"综述论文系统梳理AI与机器学习在垂直农业中的应用，涵盖全球挑战与解决方案，信息量大且权威，对智慧农业研究有参考价值。",[24],{"name":10,"url":6},[26,27,28,29],"智慧农业","可持续发展","垂直农业","AI综述",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":45},[43,52,58,65,71],{"code":31,"score":44,"supported":45,"title":46,"abstract":49,"tags":51},25,true,[47,48],"machine learning","artificial intelligence",[50,47,48],"计算机视觉",[],{"code":53,"score":54,"supported":45,"title":55,"abstract":56,"tags":57},"B7",4,[],[28],[28],{"code":59,"score":60,"supported":45,"title":61,"abstract":62,"tags":64},"H3",3,[],[63],"灌溉",[],{"code":66,"score":60,"supported":45,"title":67,"abstract":68,"tags":70},"I5",[],[69],"IoT",[],{"code":72,"score":21,"supported":14,"title":73,"abstract":74,"tags":75},"I1",[],[],[26],[77,78],"可持续发展 垂直农业 智慧农业 AI综述","可持续发展 垂直农业","可持续发展垂直农业智慧农业AI综述-462",6,"2026-08-12T23:57:24.177672Z",{"total":80,"page":21,"page_size":80,"items":83},[84,182,261,312,369,426],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":8,"source_name":90,"source_url":87,"published_at":91,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":92,"score_detail":93,"sources":99,"tags":101,"primary_topic":105,"secondary_topics":106,"classification":113,"search_phrases":162,"slug":165,"view_count":21,"doi":166,"paper":167,"created_at":181},5086,"Artificial Intelligence for Environmental Sustainability and Climate Change Mitigation: Recent Advances, Challenges, and Future Perspectives","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23399627","Abstract Climate change and environmental degradation have emerged as two of the most pressing global challenges of the twenty-first century, threatening ecosystems, biodiversity, human health, and sustainable development. Artificial Intelligence (AI) has transformed environmental research and management by enabling advanced data analytics, real-time monitoring, predictive modeling, and intelligent decision-support systems. This review critically examines the applications of AI in addressing environmental and climate-related challenges, including climate forecasting, air and water quality monitoring, biodiversity conservation, precision agriculture, renewable energy optimization, carbon emission assessment, waste management, and disaster risk reduction. Furthermore, The review also identifies current research gaps and highlights future opportunities for developing transparent, energy-efficient, and explainable AI systems that support evidence-based environmental decisionmaking. It concludes that AI has become a transformative tool for accelerating progress toward climate change mitigation, environmental sustainability, and the achievement of the United Nations Sustainable Development Goals (SDGs). Strengthening interdisciplinary collaboration, responsible AI governance, and policy support will be essential to maximize the environmental benefits of AI while minimizing its ecological footprint. Keywords: Artificial Intelligence; Climate Change; Environmental Sustainability; Machine Learning; Deep Learning; Remote Sensing; Internet of Things; Renewable Energy; Biodiversity Conservation; Sustainable Development Goals.","摘要 气候变化和环境退化已成为二十一世纪最紧迫的全球性挑战之一，威胁着生态系统、生物多样性、人类健康和可持续发展。人工智能（AI）通过实现先进的数据分析、实时监测、预测建模和智能决策支持系统，改变了环境研究与管理的方式。本文综述批判性地考察了AI在应对环境和气候相关挑战中的应用，包括气候预测、空气和水质监测、生物多样性保护、精准农业、可再生能源优化、碳排放评估、废物管理和灾害风险降低。此外，本综述还指出了当前的研究空白，并强调了未来开发透明、节能且可解释的AI系统的机遇，以支持基于证据的环境决策。文章得出结论，AI已成为加速气候变化减缓、环境可持续发展以及实现联合国可持续发展目标（SDGs）的变革性工具。加强跨学科合作、负责任的AI治理和政策支持，对于最大化AI的环境效益并同时最小化其生态足迹至关重要。关键词：人工智能；气候变化；环境可持续性；机器学习；深度学习；遥感；物联网；可再生能源；生物多样性保护；可持续发展目标。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-15T00:00:00Z",70,{"impact":17,"substance":94,"depth":95,"authority":96,"freshness":97,"relevant":21,"comment":98},16,15,12,9,"综述性论文，覆盖AI在气候预测、精准农业、遥感监测等方向的应用，与农业信息化相关但偏宏观综述，增量有限。",[100],{"name":90,"url":87},[26,102,27,103,104],"农业人工智能","气候变化","遥感监测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[107],{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":38,"locked":14,"version":39,"method":40,"evidence":114},{"candidates":115,"agricultural_evidence":45},[116,123,128,135,142,149,156],{"code":31,"score":17,"supported":45,"title":117,"abstract":118,"tags":122},[48],[119,120,47,121,48],"深度学习","机器学习","deep learning",[102],{"code":108,"score":95,"supported":45,"title":124,"abstract":126,"tags":127},[125],"climate change",[103,125],[103],{"code":72,"score":129,"supported":45,"title":130,"abstract":131,"tags":134},7,[],[132,133],"精准农业","precision agriculture",[26],{"code":136,"score":129,"supported":45,"title":137,"abstract":138,"tags":141},"I3",[],[139,140],"遥感","remote sensing",[104],{"code":143,"score":80,"supported":45,"title":144,"abstract":145,"tags":148},"H4",[],[146,147],"碳排放","carbon emission",[],{"code":150,"score":80,"supported":45,"title":151,"abstract":152,"tags":155},"H6",[],[153,154],"生物多样性","biodiversity",[],{"code":66,"score":80,"supported":45,"title":157,"abstract":158,"tags":161},[],[159,160],"物联网","internet of things",[],[163,164],"AI 气候变化","人工智能 环境可持续","AI气候变化-5086","10.5281\u002Fzenodo.23399627",{"doi":166,"openalex_id":168,"authors":169,"venue":90,"cited_by_count":172,"oa_url":87,"card":173,"direction":179,"ingested_from":180},"W7222092855",[170],{"name":171,"orcid":8},"Deepmala Suryavanshi",0,{"tldr":174,"method":175,"finding":176,"direction":177,"opportunity":178},"综述AI在气候变化缓解与环境可持续领域的应用进展、挑战与未来方向。","文献综述，涵盖机器学习、深度学习、遥感、物联网等技术应用。","AI已成为推动气候减缓与可持续发展目标实现的变革性工具。","农业绿色发展与碳","可聚焦可解释、低能耗AI在农业碳减排与精准环境决策中的落地研究。","智慧农业 \u002F 农业物联网","openalex","2026-10-16T23:30:15.059867Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":187,"content":8,"source_name":188,"source_url":185,"published_at":189,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":194,"tags":196,"primary_topic":198,"secondary_topics":199,"classification":200,"search_phrases":233,"slug":236,"view_count":237,"doi":238,"paper":239,"created_at":260},4211,"An integrated scientometric and BERTopic-based analysis on examining the role of artificial intelligence in achieving Sustainable Development Goals (SDGs)","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04841-y","This study investigates the role of Artificial Intelligence (AI) in achieving the United Nations Sustainable Development Goals (SDGs) through a comprehensive scientometric analysis. It explores the evolution of scholarly research, identifies leading contributors, maps thematic trends, examines how AI technologies contribute to advancing sustainability across multiple domains, and identifies latent semantic themes. A total of 3262 unique scholarly records published between 1989 and 2025, after deduplication and screening, were finalized from the Scopus and Web of Science databases. The dataset was analysed using Biblioshiny (R-based Bibliometrix) and VOSviewer to examine publication trends, citation impact, collaborative networks, thematic evolution, and conceptual structures. Indicators such as Relative Growth Rate (RGR), Doubling Time (DT), h-index, g-index, and Lotka’s Law were applied to evaluate research productivity and scholarly influence. In addition, BERTopic-based abstract-level topic modelling was applied to identify latent semantic themes, SDG-linked thematic domains, temporal evolution and potential research gaps. Results reveal a significant growth in AI-SDG research, particularly after 2015, with an accelerated increase post-2020. India, USA, and China emerged as the most productive contributors, while countries like USA, India, United Kingdom and Australia lead in research impact and citation quality. Thematic evolution analysis reveals a transition from foundational themes such as AI, machine learning, climate change, and decision support systems toward more integrated and sustainability-oriented research themes, including sustainable development and SDGs. Thematic linkages further demonstrate the persistent foundational role of AI and machine learning, alongside the growing centrality of climate change and sustainable development, reflecting the increasing convergence of AI research with global sustainability priorities. BERTopic analysis further revealed eleven interpretable abstract-level thematic domains after excluding one heterogeneous outlier category. The dominant themes included digital transformation and green innovation, AI-enabled education, digital health, smart agriculture, renewable energy systems, clean water and sanitation, remote sensing, NLP-based decision support, sustainable construction, tourism, and sustainable chemistry\u002Fpharmaceutical applications. This study maps the AI–SDG research landscape by integrating bibliometric indicators with thematic evolution, conceptual analysis, and abstract-level semantic topic modelling. The findings provide insights into the development and thematic orientation of AI-related research in the context of sustainable development. The use of BERTopic complements conventional bibliometric approaches by examining semantic patterns within publication abstracts alongside keyword and citation-based analyses.","本研究通过一项全面的科学计量分析，探讨了人工智能（Artificial Intelligence, AI）在实现联合国可持续发展目标（Sustainable Development Goals, SDGs）中的作用。研究考察了学术研究的演进历程，识别了主要贡献者，绘制了主题趋势图谱，分析了AI技术如何在多个领域推动可持续发展，并识别了潜在语义主题。经去重和筛选后，最终从Scopus和Web of Science数据库中确定了1989年至2025年间发表的3262条独特学术记录。使用Biblioshiny（基于R的Bibliometrix）和VOSviewer对数据集进行分析，以考察出版趋势、引用影响、合作网络、主题演进和概念结构。采用相对增长率（Relative Growth Rate, RGR）、倍增时间（Doubling Time, DT）、h指数、g指数和洛特卡定律（Lotka's Law）等指标评估研究生产力和学术影响力。此外，应用基于BERTopic的摘要级主题建模，以识别潜在语义主题、与SDG相关的主题领域、时间演变和潜在研究空白。结果显示，AI-SDG研究显著增长，尤其在2015年之后，2020年后增长加速。印度、美国和中国是最多产的贡献者，而美国、印度、英国和澳大利亚等国家在研究影响力和引用质量方面领先。主题演进分析揭示了从AI、机器学习、气候变化和决策支持系统等基础主题向更综合、面向可持续发展的研究主题（包括可持续发展和SDGs）的转变。主题关联进一步表明AI和机器学习的持续基础性作用，同时气候变化和可持续发展的中心性日益增强，反映了AI研究与全球可持续发展优先事项日益融合。BERTopic分析在排除一个异质性离群类别后，进一步揭示了十一个可解释的摘要级主题领域。主导主题包括数字化转型与绿色创新、AI赋能教育、数字健康、智慧农业、可再生能源系统、清洁水和卫生设施、遥感、基于自然语言处理的决策支持、可持续建筑、旅游业以及可持续化学\u002F制药应用。本研究通过整合文献计量","Discover Sustainability","2026-10-04T00:00:00Z",80,{"impact":17,"substance":18,"depth":17,"authority":192,"freshness":97,"relevant":21,"comment":193},13,"该研究以3262篇文献的计量与BERTopic主题建模系统梳理AI支撑SDGs的研究格局，其中智慧农业、遥感、数字健康等主题与农业信息化高度相关，方法新颖、数据规模大，值得进入每日精选。",[195],{"name":188,"url":185},[197,26,102,27,139],"数字乡村",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":201},{"candidates":202,"agricultural_evidence":45},[203,208,213,217,221,228],{"code":31,"score":17,"supported":45,"title":204,"abstract":205,"tags":207},[48],[120,47,48,206],"decision support",[102],{"code":72,"score":129,"supported":45,"title":209,"abstract":210,"tags":212},[],[26,211],"smart agriculture",[26],{"code":136,"score":129,"supported":45,"title":214,"abstract":215,"tags":216},[],[139,140],[139],{"code":108,"score":80,"supported":45,"title":218,"abstract":219,"tags":220},[],[103,125],[],{"code":222,"score":80,"supported":45,"title":223,"abstract":224,"tags":227},"I7",[],[225,226],"文献计量","bibliometric",[],{"code":229,"score":21,"supported":14,"title":230,"abstract":231,"tags":232},"I6",[],[],[197],[234,235],"AI SDG 文献计量","BERTopic 可持续农业","AISDG文献计量-4211",2,"10.1007\u002Fs43621-026-04841-y",{"doi":238,"openalex_id":240,"authors":241,"venue":188,"cited_by_count":172,"oa_url":185,"card":254,"direction":179,"ingested_from":180},"W7219683711",[242,245,248,251],{"name":243,"orcid":244},"Reetu Verma","https:\u002F\u002Forcid.org\u002F0000-0002-0567-7219",{"name":246,"orcid":247},"Ashok Kumar","https:\u002F\u002Forcid.org\u002F0009-0005-3279-2895",{"name":249,"orcid":250},"Kavita Pabreja","https:\u002F\u002Forcid.org\u002F0000-0001-9856-0900",{"name":252,"orcid":253},"Saurabh Kumar","https:\u002F\u002Forcid.org\u002F0009-0000-5924-6425",{"tldr":255,"method":256,"finding":257,"direction":258,"opportunity":259},"用科学计量与BERTopic分析3262篇文献，揭示AI助力可持续发展目标的研究格局与主题演化。","Scopus\u002FWoS文献，Biblioshiny、VOSviewer计量分析加B","AI-SDG研究2015年后激增，主题从基础AI转向可持续发展，识别出智慧农业等11个主题域。","农业人工智能与决策模型","可聚焦智慧农业主题，探究AI在农业SDG中的具体贡献路径与区域差异，填补细粒度实证空白。","2026-10-05T23:30:09.006483Z",{"id":262,"title":263,"url":264,"summary":265,"summary_zh":266,"content":8,"source_name":90,"source_url":264,"published_at":267,"category":12,"cover_url":8,"hotness":44,"is_selected":14,"score":268,"score_detail":269,"sources":271,"tags":275,"primary_topic":277,"secondary_topics":278,"classification":279,"search_phrases":296,"slug":299,"view_count":60,"doi":300,"paper":301,"created_at":311},3914,"Artificial Intelligence for Sustainable Development: Opportunities, Innovations, and Systemic Risks","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23052318","Abstract The paper considers the SDGs as a broad organizing framework, with particular attention to SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 7 (Affordable and Clean Energy), SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 15 (Life on Land). The outcomes of AI applications may vary with local infrastructure, data quality, energy sources, water availability, and institutional capacity. Any numerical claims drawn from prior studies should therefore be interpreted in the context and scope of the original source. This paper is a qualitative review based on secondary information and published literature concerning AI and sustainable development. It uses a descriptive and thematic approach to organize the discussion into application areas, enabling technologies, potential benefits, and implementation risks. The principal themes are precision agriculture, renewable energy integration, circular logistics, data infrastructure, algorithmic fairness, and environmental governance. No new field survey, experiment, or original statistical dataset was collected for this paper. Accordingly, the discussion is interpretive and should not be treated as a causal or quantitative evaluation of a particular AI system.","本文将可持续发展目标视为一个广泛的组织框架，特别关注可持续发展目标2（零饥饿）、可持续发展目标6（清洁饮水和卫生设施）、可持续发展目标7（经济适用的清洁能源）、可持续发展目标12（负责任消费和生产）、可持续发展目标13（气候行动）以及可持续发展目标15（陆地生物）。人工智能应用的成效可能因当地基础设施、数据质量、能源来源、水资源可得性和机构能力而异。因此，凡引自既有研究的数值性论断，均应结合原始来源的语境和范围加以解读。本文是一项基于二手信息和已发表文献的定性综述，涉及人工智能与可持续发展。文章采用描述性和主题式方法，将讨论组织为应用领域、使能技术、潜在效益和实施风险。主要主题包括精准农业、可再生能源整合、循环物流、数据基础设施、算法公平性和环境治理。本文未开展新的实地调查、实验，也未收集原始统计数据集。因此，相关讨论属于阐释性分析，不应被视为对某一特定人工智能系统的因果性或定量评估。","2026-09-30T00:00:00Z",69,{"impact":17,"substance":20,"depth":95,"authority":192,"freshness":97,"relevant":21,"comment":270},"Zenodo 发布的 AI 与可持续发展定性综述，覆盖精准农业、算法公平等议题，但无新数据与实证，属框架性梳理，参考价值中等。",[272,273],{"name":90,"url":264},{"name":90,"url":274},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23052317",[26,102,27,132,276],"算法公平",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":280},{"candidates":281,"agricultural_evidence":45},[282,286,291],{"code":31,"score":97,"supported":45,"title":283,"abstract":284,"tags":285},[48],[],[102],{"code":72,"score":287,"supported":45,"title":288,"abstract":289,"tags":290},8,[],[132,133],[26,132],{"code":59,"score":60,"supported":45,"title":292,"abstract":293,"tags":295},[],[294],"水资源",[],[297,298],"AI 可持续农业 SDGs","农业人工智能 可持续发展 智慧农业 算法公平","AI可持续农业SDGs-3914","10.5281\u002Fzenodo.23052318",{"doi":300,"openalex_id":302,"authors":303,"venue":90,"cited_by_count":172,"oa_url":264,"card":306,"direction":258,"ingested_from":180},"W7214923133",[304],{"name":305,"orcid":8},"Snehal Ramesh Patil",{"tldr":307,"method":308,"finding":309,"direction":177,"opportunity":310},"综述AI在可持续发展目标中的应用机遇、创新与系统性风险。","基于二手文献的定性主题综述，无新数据或实验。","AI效益受本地基础设施、数据质量与制度能力制约，需情境化解读。","可针对特定区域开展AI农业应用的实证量化评估，弥补综述缺乏因果验证的空白。","2026-10-01T23:30:49.083263Z",{"id":313,"title":314,"url":315,"summary":316,"summary_zh":317,"content":8,"source_name":90,"source_url":315,"published_at":267,"category":12,"cover_url":8,"hotness":44,"is_selected":14,"score":318,"score_detail":319,"sources":321,"tags":325,"primary_topic":326,"secondary_topics":327,"classification":328,"search_phrases":353,"slug":356,"view_count":54,"doi":357,"paper":358,"created_at":368},3462,"AI For Sustainable Development Opportunities & Innovation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933761","Abstract Artificial Intelligence is increasingly being explored as a tool for accelerating progress toward sustainable development. AI can analyse large datasets, identify patterns, forecast events, optimise systems and support decision-making across agriculture, energy, water, healthcare, education, cities, industry and environmental management. Recent research shows that AI-for-SDG research is expanding rapidly, while also revealing gaps in social inclusion, governance and assessment of AI’s own environmental footprint. This project examines the major opportunities for AI-enabled sustainable development, including smart agriculture, renewable-energy optimisation, climate and disaster forecasting, intelligent waste management, sustainable cities, healthcare and education. It also discusses innovation pathways such as machine learning, computer vision, remote sensing, generative AI, digital twins and edge AI. The study emphasises that technological capability alone is insufficient: responsible AI requires reliable data, transparency, privacy, human oversight, equitable access, energy-efficient computing and lifecycle environmental assessment.","摘要 人工智能正日益被视为加速可持续发展进程的工具。人工智能可以分析大型数据集、识别模式、预测事件、优化系统，并在农业、能源、水资源、医疗、教育、城市、工业与环境管理等领域支持决策。近期研究表明，人工智能促进可持续发展目标（SDG）的研究正在迅速扩展，同时也揭示了在社会包容、治理以及人工智能自身环境足迹评估方面的不足。本项目考察了人工智能赋能可持续发展的主要机遇，包括智慧农业、可再生能源优化、气候与灾害预测、智能废物管理、可持续城市、医疗和教育。项目还讨论了机器学习、计算机视觉、遥感、生成式人工智能、数字孪生和边缘人工智能等创新路径。研究强调，仅靠技术能力是不够的：负责任的人工智能需要可靠的数据、透明度、隐私保护、人类监督、公平获取、节能计算以及生命周期环境评估。",68,{"impact":17,"substance":94,"depth":95,"authority":192,"freshness":80,"relevant":21,"comment":320},"系统梳理AI赋能农业等可持续发展领域的机遇与治理挑战，属综合性研究综述，对智慧农业方向有参考价值但非突破性成果。",[322,323],{"name":90,"url":315},{"name":90,"url":324},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22933762",[197,26,102,27,139],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":329},{"candidates":330,"agricultural_evidence":45},[331,336,341,345,349],{"code":72,"score":13,"supported":45,"title":332,"abstract":333,"tags":335},[],[26,334,211],"数字孪生",[26],{"code":31,"score":13,"supported":45,"title":337,"abstract":338,"tags":340},[],[120,50,47,48,339],"computer vision",[102],{"code":136,"score":129,"supported":45,"title":342,"abstract":343,"tags":344},[],[139,140],[139],{"code":59,"score":60,"supported":45,"title":346,"abstract":347,"tags":348},[],[294],[],{"code":229,"score":21,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[197],[354,355],"AI 可持续发展 智慧农业","农业人工智能 可持续发展 数字乡村 智慧农业","AI可持续发展智慧农业-3462","10.5281\u002Fzenodo.22933761",{"doi":357,"openalex_id":359,"authors":360,"venue":90,"cited_by_count":172,"oa_url":315,"card":363,"direction":179,"ingested_from":180},"W7214172367",[361],{"name":362,"orcid":8},"Saloni Ananda Patil",{"tldr":364,"method":365,"finding":366,"direction":177,"opportunity":367},"综述AI在可持续发展各领域的机会与创新路径，并强调负责任AI的治理要求。","文献综述，覆盖机器学习、计算机视觉、遥感、数字孪生与边缘AI等。","AI-for-SDG研究快速扩张，但社会包容、治理与AI自身环境足迹评估仍存缺口。","可量化AI自身能耗与碳足迹，并评估其在农业减排中的净环境效益。","2026-09-25T23:30:08.894307Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":8,"source_name":375,"source_url":372,"published_at":376,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":190,"score_detail":377,"sources":379,"tags":381,"primary_topic":382,"secondary_topics":383,"classification":385,"search_phrases":404,"slug":407,"view_count":408,"doi":409,"paper":410,"created_at":425},3353,"Integrating artificial intelligence in climate change and sustainable development: A comprehensive bibliometric review","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sftr.2026.102145","The integration of Artificial Intelligence (AI) with climate change and sustainable development (SD) studies has significant potential to enable actionable, data-driven, innovative, and long-term mitigation and adaptation strategies. However, there is a lack of a well-organized, comprehensive review of mapping that integrates AI, climate change, and the SD nexus. This study addresses this notable gap through a bibliometric review that dissects descriptive bibliometric, performance, and conceptual analyses. Utilizing the Web of Science Core Collection, 3291 publications from 2007 to 2025 were analyzed using Bibliometrix and VOSviewer software. The findings indicate a 38.29% annual publication growth rate, and original articles (80%) were the dominant publication type. The Chinese Academy of Sciences was the top contributor, and China and the USA were leaders in research output and international AI-integrated climate and SD research collaborations. Keyword analysis shows “artificial intelligence”, “climate change”, “deep learning”, “machine learning”, and “sustainability” as dominant keywords. Conceptual mapping identified four approaches: macro-level AI-driven digitalization; methodologically driven geospatial and machine-learning-based environmental monitoring; predictive modeling; and AI-driven smart agriculture applications. Thematic mapping reveals a shift from fundamental AI for climate change-focused research (2007–2013) to integrating AI with climate change and SD (2014–2019), and modeling and predicting approaches (2020–2025). Findings highlight substantial geographic asymmetries; data and research gaps persist in climate-vulnerable low-income areas. The extractivism rate for low-income countries was 41%, indicating that nearly half of their publications are led by high-income countries. Recommendations are grounded in theoretical, methodological, practical, and policy considerations, with an emphasis on SDGs 13 and 17. This study provides insights for researchers, practitioners, and policymakers to emphasize policies and technologies and implement a nexus-based framework.","人工智能（Artificial Intelligence, AI）与气候变化及可持续发展（Sustainable Development, SD）研究的融合，在推动可操作、数据驱动、创新性和长期性的减缓与适应策略方面具有巨大潜力。然而，目前缺乏对AI、气候变化与可持续发展三者交叉领域的系统化、综合性文献计量综述。本研究通过文献计量学综述填补了这一显著空白，从描述性文献计量分析、绩效分析和概念分析三个维度进行剖析。利用Web of Science核心合集，采用Bibliometrix和VOSviewer软件对2007年至2025年间的3291篇文献进行了分析。研究结果表明，年度发文增长率为38.29%，原创论文（80%）为主要文献类型。中国科学院是最大的贡献机构，中国和美国在研究产出及AI融合气候与可持续发展研究的国际合作方面处于领先地位。关键词分析显示，“人工智能”“气候变化”“深度学习”“机器学习”和“可持续性”是主导性关键词。概念图谱识别出四种研究路径：宏观层面的AI驱动数字化；方法论驱动的基于地理空间和机器学习的环境监测；预测建模；以及AI驱动的智慧农业应用。主题图谱揭示了研究重心的演变：从气候变化聚焦的基础AI研究（2007—2013年），到AI与气候变化及可持续发展的融合（2014—2019年），再到建模与预测方法（2020—2025年）。研究发现存在显著的地理不对称性；气候脆弱型低收入地区仍存在数据和研究缺口。低收入国家的提取主义率为41%，表明其近半数出版物由高收入国家主导。建议基于理论、方法论、实践和政策层面的考量，并着重关注可持续发展目标13和目标17。本研究为研究人员、实践者和政策制定者提供了洞见，以强调政策与技术的重要性，并实施基于交叉领域的框架。","Sustainable Futures","2026-09-23T00:00:00Z",{"impact":17,"substance":18,"depth":17,"authority":192,"freshness":97,"relevant":21,"comment":378},"基于3291篇文献的AI与气候变化及可持续发展文献计量综述，方法规范、数据规模大，对智慧农业与农业AI研究具有参考价值。",[380],{"name":375,"url":372},[26,102,27,103,225],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[384],{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},{"status":38,"locked":14,"version":39,"method":40,"evidence":386},{"candidates":387,"agricultural_evidence":45},[388,392,396,400],{"code":31,"score":17,"supported":45,"title":389,"abstract":390,"tags":391},[48],[119,120,47,121,48],[102],{"code":108,"score":95,"supported":45,"title":393,"abstract":394,"tags":395},[125],[103,125],[103],{"code":222,"score":95,"supported":45,"title":397,"abstract":398,"tags":399},[226],[225,226],[225],{"code":72,"score":129,"supported":45,"title":401,"abstract":402,"tags":403},[],[26,211],[26],[405,406],"农业人工智能 可持续发展 文献计量 智慧农业","农业人工智能 可持续发展","农业人工智能可持续发展文献计量智慧农业-3353",5,"10.1016\u002Fj.sftr.2026.102145",{"doi":409,"openalex_id":411,"authors":412,"venue":375,"cited_by_count":172,"oa_url":372,"card":420,"direction":179,"ingested_from":180},"W7214099405",[413,416,418],{"name":414,"orcid":415},"H. B. T. P. Jayathilaka","https:\u002F\u002Forcid.org\u002F0009-0001-0589-6999",{"name":417,"orcid":8},"Shiyan Zhai",{"name":419,"orcid":8},"Yuke Feng",{"tldr":421,"method":422,"finding":423,"direction":258,"opportunity":424},"用文献计量法梳理2007-2025年AI与气候变化及可持续发展交叉研究，揭示主题演化与地域失衡。","Web of Science 3291篇文献，Bibliometrix与VOSv","年增38.29%，主题从基础AI转向建模预测，低收入国家41%论文由高收入国家主导。","可针对气候脆弱低收入地区，构建AI+智慧农业的本地化数据与决策模型，填补研究空白。","2026-09-24T23:30:10.248845Z",{"id":427,"title":428,"url":429,"summary":430,"summary_zh":431,"content":8,"source_name":432,"source_url":429,"published_at":433,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":434,"score_detail":435,"sources":437,"tags":439,"primary_topic":441,"secondary_topics":442,"classification":444,"search_phrases":473,"slug":475,"view_count":21,"doi":476,"paper":477,"created_at":495},2642,"Artificial Intelligence for Environmental Resilience and Sustainable Innovation","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fair.v1i2.239","Environmental challenges such as climate change, resource depletion, biodiversity loss, and increasing pollution require innovative and adaptive solutions to strengthen environmental resilience. Artificial intelligence (AI) has emerged as a transformative technology capable of improving environmental monitoring, prediction, and decision-making processes. This study examines the role of artificial intelligence in enhancing environmental resilience and promoting sustainable innovation across various sectors, including natural resource management, climate adaptation, waste management, environmental conservation, and smart environmental systems. This study adopts a Systematic Literature Review (SLR) methodology integrated with bibliometric analysis and thematic content analysis to identify research trends, conceptual developments, and emerging themes related to AI applications in environmental sustainability. The selected studies were analyzed using VOSviewer, Tableau, and Microsoft Excel to visualize knowledge structures, thematic relationships, and research patterns. The findings reveal that AI significantly contributes to environmental resilience by enabling advanced data analysis, early detection of environmental risks, predictive assessment, resource optimization, and evidence-based decision-making. Furthermore, AI-driven innovations support sustainable practices through renewable energy optimization, precision agriculture, waste management improvement, water resource monitoring, and smart city development. However, challenges remain regarding data quality, technological accessibility, computational requirements, ethical considerations, and responsible governance. The study concludes that AI has substantial potential to accelerate environmental resilience and sustainable innovation when supported by appropriate policies, stakeholder collaboration, and long-term sustainability strategies. By integrating intelligent technologies with responsible environmental management approaches, AI can serve as a strategic enabler in addressing complex ecological challenges and supporting a more adaptive, resilient, and sustainable future.","气候变化、资源枯竭、生物多样性丧失和污染加剧等环境挑战，需要创新性和适应性的解决方案来增强环境韧性。人工智能（AI）已成为一种变革性技术，能够改善环境监测、预测和决策过程。本研究探讨了人工智能在增强环境韧性和促进各领域可持续创新中的作用，涵盖自然资源管理、气候适应、废物管理、环境保护和智能环境系统等。本研究采用系统性文献综述（SLR）方法，并结合文献计量分析和主题内容分析，以识别与人工智能在环境可持续性中应用相关的研究趋势、概念发展和新兴主题。所选研究使用VOSviewer、Tableau和Microsoft Excel进行分析，以可视化知识结构、主题关系和研究模式。研究结果表明，人工智能通过实现先进数据分析、环境风险早期检测、预测性评估、资源优化和循证决策，显著促进了环境韧性。此外，人工智能驱动的创新通过可再生能源优化、精准农业、废物管理改进、水资源监测和智慧城市发展，支持可持续实践。然而，在数据质量、技术可及性、计算需求、伦理考量和负责任治理方面仍存在挑战。研究结论认为，在适当的政策、利益相关者协作和长期可持续战略支持下，人工智能具有加速环境韧性和可持续创新的巨大潜力。通过将智能技术与负责任的环境管理方法相结合，人工智能可以作为应对复杂生态挑战、支持更具适应性、韧性和可持续未来的战略赋能者。","AI Innovation and Resilience for the Environment (AIR)","2026-09-15T00:00:00Z",72,{"impact":17,"substance":17,"depth":94,"authority":96,"freshness":287,"relevant":21,"comment":436},"系统综述类论文，梳理AI在环境韧性与可持续创新中的应用，涵盖精准农业、水资源监测等方向，方法规范但结论偏综述性，对农业信息化有参考价值。",[438],{"name":432,"url":429},[26,102,27,132,440],"环境韧性",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[443],{"code":108,"slug":109,"name":110,"path":111,"parent_code":112},{"status":38,"locked":14,"version":39,"method":40,"evidence":445},{"candidates":446,"agricultural_evidence":45},[447,451,456,460,465,469],{"code":31,"score":96,"supported":45,"title":448,"abstract":449,"tags":450},[48],[48],[102],{"code":108,"score":97,"supported":45,"title":452,"abstract":453,"tags":455},[],[103,454,125],"气候适应",[],{"code":72,"score":287,"supported":45,"title":457,"abstract":458,"tags":459},[],[132,133],[26,132],{"code":59,"score":80,"supported":45,"title":461,"abstract":462,"tags":464},[],[294,463],"water resource",[],{"code":150,"score":80,"supported":45,"title":466,"abstract":467,"tags":468},[],[153,154],[],{"code":222,"score":80,"supported":45,"title":470,"abstract":471,"tags":472},[],[225,226],[],[474,406],"农业人工智能 可持续发展 智慧农业 环境韧性","农业人工智能可持续发展智慧农业环境韧性-2642","10.68012\u002Fair.v1i2.239",{"doi":476,"openalex_id":478,"authors":479,"venue":432,"cited_by_count":172,"oa_url":429,"card":490,"direction":179,"ingested_from":180},"W7213274173",[480,482,485,488],{"name":481,"orcid":8},"Sri Poedji Lestari",{"name":483,"orcid":484},"Rosa Lesmana","https:\u002F\u002Forcid.org\u002F0009-0002-5308-6023",{"name":486,"orcid":487},"Sabil Maulana Fauzi","https:\u002F\u002Forcid.org\u002F0009-0003-1833-1027",{"name":489,"orcid":8},"Alexander Johnson Johnson",{"tldr":491,"method":492,"finding":493,"direction":177,"opportunity":494},"系统综述AI在环境韧性与可持续创新中的应用、趋势与挑战。","系统文献综述结合文献计量与主题分析，用VOSviewer等可视化。","AI通过数据分析、风险预警和资源优化显著增强环境韧性，但存在数据与治理挑战。","可聚焦AI在精准农业与碳减排协同中的实证研究，弥补数据质量与治理机制空白。","2026-09-16T23:30:11.835233Z"]