[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-380":3,"related-380":78},{"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":31,"secondary_topics":37,"classification":44,"search_phrases":72,"slug":75,"view_count":76,"doi":8,"paper":8,"created_at":77},380,"农户数字能力对低碳农业采纳的影响——基于陕黔川10057户猕猴桃种植户的机器学习分析","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088","基于陕西、贵州、四川三省10057名猕猴桃种植户的微观调查数据,西安建筑科技大学向雯与西北农林大学高建中等于Sustainability发表论文。以农户对低碳农业技术的初始与持续采纳为因变量,构建涵盖数字接入、数字获取、数字处理和数字共享4个维度的数字能力综合评价体系。比较Lasso回归、极端梯度提升、随机森林和反向传播神经网络等多种实证模型,结果显示随机森林模型表现最优。采用SHAP方法定量评估各变量边际贡献,研究发现农户数字能力显著正向促进低碳农业技术的初始与持续采纳,且具有明显的维度异质性:数字接入和获取能力是初始采纳的核心驱动因素,数字处理和共享能力对持续采纳起决定性作用。",null,"## 1. Introduction\n\nIn light of the ongoing decline in atmospheric quality and the momentous deterioration of the ecological environment, the pursuit of low-carbon development has emerged as an imperative response to these challenges. The sustainable development model of low-carbon development, which is characterised by energy conservation, reduced consumption, and pollution reduction, has become a crucial avenue for addressing environmental issues [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B1-sustainability-18-08088),[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B2-sustainability-18-08088),[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B3-sustainability-18-08088),[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B4-sustainability-18-08088)]. In response to the specific conditions of its own development, China has put forth proposals for carbon peaking and carbon neutrality programmes. In 2020, China formally proposed a double carbon goal, committing to the international community to peak its carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060 [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B5-sustainability-18-08088)]. It is widely acknowledged that agriculture represents a significant global source of greenhouse gas emissions. Consequently, it has become evident that farmland has an integral part to play in achieving the double carbon goal [[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B6-sustainability-18-08088),[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B7-sustainability-18-08088)].\n\nNevertheless, for an extended period, the direct cost of agricultural production has been the sole factor considered, with the cost of resource and environmental losses incurred during the production process remaining unaccounted for. The excessive and inefficient use of agricultural resources, such as pesticides and chemical fertilisers, in agricultural production has a direct impact on the quality and safety of agricultural products, the ecological environment, and human health. The negative externalities of this practice are becoming increasingly apparent [[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B8-sustainability-18-08088),[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B9-sustainability-18-08088),[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B10-sustainability-18-08088)].\n\nOver the past few decades, China’s traditional agricultural production and cultivation patterns have undergone significant expansion, resulting in a considerable accumulation of high carbon emissions and a multitude of ecological damage issues [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B11-sustainability-18-08088),[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B12-sustainability-18-08088),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B13-sustainability-18-08088)]. In recent years, the central No. 1 document has consistently emphasised the necessity of promoting environmentally sustainable agricultural practices. This entails accelerating the promotion and application of agricultural input reduction and efficiency technology, integrating water and fertiliser management, and establishing and enhancing the collection and utilisation of straw, agricultural film, pesticide packaging waste, livestock and poultry manure, and other agricultural waste collection and treatment systems. Furthermore, General Secretary Xi Jinping emphasised the necessity of developing ecological and low-carbon agricultural practices, asserting that environmentally sustainable practices are the foundation of modern agriculture.\n\nGreen and low-carbon agriculture has the potential to save energy and reduce emissions, facilitate the sequestration of carbon in an ecological manner, enhance the utilisation of agricultural resources, produce safe and high-quality agricultural products, and provide robust support for the realisation of China’s double carbon target [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B14-sustainability-18-08088),[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B15-sustainability-18-08088)]. The foundation of low-carbon agricultural production is the utilisation of cutting-edge low-carbon agricultural technology [[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B16-sustainability-18-08088),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B17-sustainability-18-08088)]. It is for this reason that research and development, promotion, and application of low-carbon agricultural technology have become pivotal measures to achieve carbon sequestration and emission reduction in agricultural production. The contemporary understanding of low-carbon agricultural technology is primarily concerned with the minimisation of inputs, including energy, fertilisers, pesticides and water, in agricultural production. This approach ultimately aims to curtail greenhouse gas emissions from agricultural sources. In particular, low-carbon agricultural technology encompasses a range of techniques and practices, including formula fertilisation technology, commercial organic fertiliser application, farm fertiliser application, no-till technology, and water-saving irrigation technology.\n\nThe implementation of the household contract responsibility system in China has resulted in the farming household becoming the primary entity engaged in production and management activities within rural areas, and is thus the most fundamental decision-making unit. The uptake of low-carbon agricultural technology by farmers is contingent upon the comprehensive promotion and implementation of ecological low-carbon agriculture in China [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B18-sustainability-18-08088)]. The decision to adopt technology is a behavioural choice made by an individual, influenced by the opinions of others and based on an evaluation of the advantages offered by the technology and their own suitability for it [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B19-sustainability-18-08088)]. The dissemination and uptake of low-carbon agricultural technology can be conceptualised as a process of technology diffusion [[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B20-sustainability-18-08088)]. The process can be divided into two distinct stages: the initial stage concerns farmers making the decision to adopt low-carbon agricultural technology; the subsequent stage pertains to farmers deciding to continue the behaviour after adopting low-carbon agricultural technology [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B21-sustainability-18-08088)]. In accordance with the conventional perspective on economics, a considerable number of scholars have examined the internal and external elements influencing the initial adoption of low-carbon agricultural technology by farmers at both the theoretical and empirical levels. Internal factors include gender, age, health, education, peer effects, ecological awareness, household income, non-farm labour migration, time preference, and risk perception [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B22-sustainability-18-08088),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B23-sustainability-18-08088),[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B24-sustainability-18-08088),[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B25-sustainability-18-08088),[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B26-sustainability-18-08088),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B27-sustainability-18-08088),[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B28-sustainability-18-08088)]. The adoption of low-carbon agricultural technology by farmers is influenced by several external factors, including training, social capital, market incentives, government regulation, subsidies, agricultural insurance, participation in cooperatives, and machinery outsourcing services [[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B29-sustainability-18-08088),[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B30-sustainability-18-08088),[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B31-sustainability-18-08088),[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B32-sustainability-18-08088),[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B33-sustainability-18-08088),[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B34-sustainability-18-08088)]. A review of the literature reveals a paucity of studies investigating the sustained adoption of low-carbon agricultural technology by farmers. Many scholars have employed sustained use modelling to examine the influence of various factors, including technology cost and efficacy, technology complexity, government technology promotion, and policy subsidies, on the sustained adoption of low-carbon agricultural technology by farmers [[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B35-sustainability-18-08088),[36](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B36-sustainability-18-08088),[37](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B37-sustainability-18-08088)]. A consensus was reached on the fundamental premise that the continued adoption of low-carbon agricultural technology by farmers represents an extension of the technology adoption process. This extension is based on the evaluation of the actual results of technology adoption in relation to individual expectations of the effects of low-carbon agricultural technology. It is frequently observed that the performance of a technology is at its lowest during the initial adoption phase. It is only through continued utilisation of the technology that its intrinsic value can be fully appreciated. Despite the ascendance of green agriculture in China in the context of production and development, a discrepancy persists between the initial and sustained adoption of green and low-carbon agricultural technologies by farmers. Nevertheless, the majority of existing studies focus on the initial decision-making process surrounding the adoption of low-carbon technology, neglecting to consider the dynamic characteristics of technology adoption behaviour across different stages. Furthermore, there is a tendency to conflate initial adoption with sustained adoption.\n\nThe digital economy is particularly adept at optimising the low-carbon agricultural industry landscape [[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B38-sustainability-18-08088)]. The advent of the digital economy has given rise to a plethora of novel technologies, industries, and business models. Moreover, the digitisation of knowledge and information has become a primary factor in the production process [[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B39-sustainability-18-08088)]. The integration of modern information technology with agriculture, including the internet, the Internet of Things, cloud computing, big data, and artificial intelligence, enables real-time collection of agricultural data, real-time monitoring of the agricultural ecological environment, precise input of agricultural production factors, quantitative decision-making in the agricultural production process, and intelligent regulation and control of agricultural production. These functions collectively constitute a new low-carbon agricultural production model. It has been demonstrated that the digital economy plays a notable role in the implementation of low-carbon agricultural technology by farmers [[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B40-sustainability-18-08088)]. Concurrently, the digital economy has considerable expectations regarding the digital capability of farmers. Those with superior digital abilities are more adept at navigating digital technology and are more likely to receive a premium for their skills in the context of the advancement of the digital economy.\n\nThe disparate digital capability of farmers gives rise to variations in the digital divide and the digital dividend. This raises the question of whether digital capability has a significant positive effect on the initial adoption of low-carbon agricultural technology by farmers. Does digital capability also have a significant positive effect on the sustained adoption of low-carbon agricultural technology? Given the established link between initial and sustained adoption of technology, it is pertinent to inquire whether the factors influencing the initial behaviour of farmers in the adoption of low-carbon agricultural technology also exert an influence on their sustained adoption behaviour. This study proposes an innovative perspective to study the gap between the initial and sustained adoption of existing low-carbon agricultural technology based on a comprehensive analysis of the preceding findings. The objective of this study is to achieve an accurate match between low-carbon agricultural technology and farmers.\n\nFurthermore, it is worthwhile to mention that the conventional methodology utilised in agricultural policy research typically entails the gathering of data, the application of analytical models to elucidate the interrelationships between variables, and the formulation of policy recommendations. A review of the existing literature reveals that the majority of studies have formulated hypotheses and conducted regression analyses, primarily utilising econometric methods such as binary probit, propensity score matching, and double difference methods. Nevertheless, the inherent bias in establishing linear relationships between indicators in explanatory modelling results in insufficient discriminatory precision, which may lead to a wide range of policy recommendations that are disparate. The conventional techniques employed in statistical analysis are gradually being shown to have certain inherent limitations.\n\nIn recent years, machine learning methods have been employed extensively in agri-environmental and economic research due to their ability to effectively address complex non-linear problems. The field of machine learning is predicated on the analysis and learning of the mapping of existing data to expert classification models. Techniques for processing data and information are developed through algorithms that facilitate the stable and reliable classification of unknown categorical data. In the context of artificial intelligence and big data technology, the computational analysis method of machine learning can be applied to discover data laws or implicit models from multi-dimensional data that are difficult to perceive and discover. The computational analysis method offers several advantages, including high accuracy, rapid training speed, and straightforward implementation and interpretation. The method enhances the existing research paradigms and is extensively employed in diverse classification and regression tasks [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B41-sustainability-18-08088)].\n\nIn light of the aforementioned evidence, this study proceeded to devise and administer a questionnaire survey comprising 10,057 kiwifruit farmers situated in the provinces of Shaanxi, Guizhou, and Sichuan, which collectively represent the three leading kiwifruit-producing regions in China. The current status and challenges associated with the initial and sustained adoption of low-carbon agricultural technology by kiwifruit growers were further elucidated. The ordered logit model, which has been demonstrated to offer a robust explanatory framework for behavioural decisions, was selected as a reference for the analysis of the results obtained from other machine learning methods. The dataset was divided into training and testing sets based on the results of the experimental analysis, which employed a range of machine learning techniques. The optimal model is selected on the basis of the expected goodness of fit. Subsequently, the SHAP value interpretive method is implemented to ascertain the contribution rate of each digital capability dimension to the initial and sustained adoption of low-carbon agricultural technology by farmers, thereby facilitating the effective identification of the most significant influencing factors. The influence of digital capability and its constituent elements on the initial and sustained adoption of low-carbon agricultural technology by farmers is subjected to rigorous analysis, and recommendations for pertinent policy measures are put forth.\n\nThe contributions of this study are mainly reflected in the following three aspects: (1) Research Theme: This study investigates the characteristics of a sample of farmers and their initial and sustained adoption of low-carbon agricultural technology through micro-field research. In accordance with the findings of static technology adoption studies, further consideration is given to the continued adoption of technology. Accordingly, the objective of this study is to address the existing gap in the literature on the sustained adoption of low-carbon agricultural technology by farmers. (2) Research Content: This study provides a comprehensive examination of the composition of the digital capability of farmers, with the utilisation of factor analysis to construct and measure the digital capability index from four dimensions: digital access capability, digital acquisition capability, digital processing capability, and digital sharing capability. This offers a reference paradigm for the construction of indicators for subsequent related studies. Concurrently, it furnishes novel evidence to elucidate the influence of the digital capability of farmers and their multifaceted dimensions on the initial and sustained adoption of low-carbon agricultural technology by farmers. (3) Research Method: This study offers a comprehensive integration of the strengths inherent to both traditional econometric research methods and machine learning methods. In order to more accurately capture the impact of digital capability on the initial and sustained adoption of low-carbon agricultural technology by farmers, and to provide more scientific policy recommendations for the decision-making community, an ordered logit model, a Lasso regression method, a random forest method, an XGBoost model, and a BP neural network model were used.\n\n## 2. Theoretical Analysis and Research Hypotheses\n\n### 2.1. Theoretical Analysis Framework\n\nThe behaviours of farmers in the adoption of green and low-carbon agricultural technologies can be divided into two progressive stages: initial trial and long-term sustained application. However, there exist significant discrepancies in the behavioural constraint mechanisms between the two stages [[42](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8088#B42-sustainability-18-08088)]. The former stage is primarily restricted by decision-making uncertainty arising from insufficient information, whereas the latter stage hinges on perceived technical benefits and long-term production preferences. As a core endowment enabling farmers to adapt to the digital transformation of modern agriculture, digital capability can systematically resolve practical dilemmas hindering the adoption of low-carbon technology by farmers through two pathways: alleviating endogenous decision-making constraints and optimising exogenous production mechanisms. Digital capability effectively drives the behavioural transition of farmers from passive wait-and-see to proactive trials, and from one-off usage to regular, in-depth implementation. On the one hand, grounded in the bounded rationality decision-making theory and farmer behaviour theory, comprehensive digital capability integrated by multi-dimensional capability including digital access, information acquisition, data processing, and information sharing can break the ingrained cognitive habits shaped by the traditional farming experiences of farmers, expand supply channels of agricultural technical information, correct farmers’ biased risk perceptions of low-carbon technology, and reshape their long-term income expectations. From the endogenous cognitive dimension, it thereby facilitates farmers’ acceptance of innovative low-carbon production modes. On the other hand, drawing upon the sustained technology application theory, a sound d","Sustainability 2026-08-08","2026-08-08T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,20,12,7,1,"基于大规模农户数据的机器学习研究，探讨数字能力对低碳农业采纳的影响，方法新颖，数据量大，结论可靠，对农业信息化与低碳发展有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字能力","低碳农业","SHAP","随机森林","Sustainability",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,57,62,67],{"code":32,"score":51,"supported":52,"title":53,"abstract":55,"tags":56},11,true,[54],"猕猴桃",[54],[],{"code":39,"score":13,"supported":52,"title":58,"abstract":60,"tags":61},[59],"机器学习",[],[28,29],{"code":63,"score":21,"supported":14,"title":64,"abstract":65,"tags":66},"H4",[],[],[27],{"code":68,"score":21,"supported":14,"title":69,"abstract":70,"tags":71},"I6",[],[],[26],[73,74],"低碳农业 数字能力 随机森林 Sustainability","低碳农业 数字能力","低碳农业数字能力随机森林Sustainability-380",3,"2026-08-10T23:54:46.360428Z",{"total":79,"page":21,"page_size":79,"items":80},6,[81,200,273,338,393,453],{"id":82,"title":83,"url":84,"summary":85,"summary_zh":86,"content":8,"source_name":87,"source_url":84,"published_at":88,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":89,"score_detail":90,"sources":95,"tags":97,"primary_topic":103,"secondary_topics":104,"classification":111,"search_phrases":169,"slug":172,"view_count":21,"doi":173,"paper":174,"created_at":199},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","Frontiers in Plant Science","2026-10-14T00:00:00Z",81,{"impact":17,"substance":91,"depth":17,"authority":92,"freshness":93,"relevant":21,"comment":94},22,14,9,"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[96],{"name":87,"url":84},[98,99,100,101,102],"智慧农业","农业人工智能","产量预测","多模态融合","遥感",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},[105,106],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":107,"slug":108,"name":109,"path":110,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":45,"locked":14,"version":46,"method":47,"evidence":112},{"candidates":113,"agricultural_evidence":52},[114,121,129,136,143,150,157,163],{"code":39,"score":51,"supported":52,"title":115,"abstract":116,"tags":120},[],[117,59,118,119],"深度学习","machine learning","deep learning",[99,101],{"code":32,"score":93,"supported":52,"title":122,"abstract":123,"tags":128},[],[124,125,126,127],"苹果","芒果","orchard","apple",[],{"code":107,"score":130,"supported":52,"title":131,"abstract":132,"tags":135},8,[],[133,134],"无人机多光谱","multispectral",[100,102],{"code":137,"score":20,"supported":52,"title":138,"abstract":139,"tags":142},"I1",[],[140,141],"精准农业","precision agriculture",[98],{"code":144,"score":79,"supported":52,"title":145,"abstract":146,"tags":149},"H2",[],[147,148],"土壤","soil",[],{"code":151,"score":79,"supported":52,"title":152,"abstract":153,"tags":156},"I5",[],[154,155],"物联网","IoT",[],{"code":158,"score":76,"supported":52,"title":159,"abstract":160,"tags":162},"H6",[],[161],"农业生态",[],{"code":164,"score":76,"supported":52,"title":165,"abstract":166,"tags":168},"H7",[],[167],"气候适应",[],[170,171],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":173,"openalex_id":175,"authors":176,"venue":87,"cited_by_count":190,"oa_url":84,"card":191,"direction":197,"ingested_from":198},"W7221795850",[177,180,182,184,186,188],{"name":178,"orcid":179},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":181,"orcid":8},"Umarfarooque Momin",{"name":183,"orcid":8},"Sudeep Kumar E",{"name":185,"orcid":8},"Itfaq Ahmad Mir",{"name":187,"orcid":8},"Ab Rouf Khan",{"name":189,"orcid":8},"Ghulam Mohammad Mir",0,{"tldr":192,"method":193,"finding":194,"direction":195,"opportunity":196},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","农业人工智能与决策模型","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","智慧农业 \u002F 农业物联网","openalex","2026-10-15T23:30:08.750140Z",{"id":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":8,"source_name":206,"source_url":203,"published_at":207,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":208,"score_detail":209,"sources":214,"tags":216,"primary_topic":220,"secondary_topics":221,"classification":224,"search_phrases":246,"slug":249,"view_count":21,"doi":250,"paper":251,"created_at":272},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",77,{"impact":210,"substance":211,"depth":17,"authority":212,"freshness":93,"relevant":21,"comment":213},16,21,13,"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[215],{"name":206,"url":203},[98,217,99,218,219],"无人机","遥感监测","病害预警",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[222,223],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":107,"slug":108,"name":109,"path":110,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":225},{"candidates":226,"agricultural_evidence":52},[227,233,238,242],{"code":32,"score":228,"supported":52,"title":229,"abstract":230,"tags":232},17,[127],[124,231,127],"葡萄",[],{"code":39,"score":234,"supported":52,"title":235,"abstract":236,"tags":237},15,[119],[117,119],[99],{"code":107,"score":19,"supported":52,"title":239,"abstract":240,"tags":241},[134],[134],[218],{"code":137,"score":20,"supported":52,"title":243,"abstract":244,"tags":245},[],[140,141],[98],[247,248],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":250,"openalex_id":252,"authors":253,"venue":206,"cited_by_count":190,"oa_url":265,"card":266,"direction":197,"ingested_from":198},"W7221558726",[254,257,260,262],{"name":255,"orcid":256},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":258,"orcid":259},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":261,"orcid":8},"Fatih Sinan Bir",{"name":263,"orcid":264},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":267,"method":268,"finding":269,"direction":270,"opportunity":271},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","农业遥感与作物表型","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":278,"content":8,"source_name":279,"source_url":276,"published_at":280,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":281,"score_detail":282,"sources":284,"tags":286,"primary_topic":289,"secondary_topics":290,"classification":292,"search_phrases":308,"slug":311,"view_count":190,"doi":312,"paper":313,"created_at":337},4744,"An empirical performance comparison of deep learning models for apple leaf disease recognition","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10791-026-10573-4","Currently, Biotic and abiotic factors, in agriculture, are estimated to be responsible for around 22% of the total losses in crop production. It is therefore very important, but at the same time very difficult for farmers to identify these constraints at the earliest possible stage. The most recent developments in computer vision have made it possible to identify as well as classify plant lesions at a primary level. Various deep learning techniques have made it possible to detect biotic stress in apple plant leaves with a very high degree of accuracy. Hence, automated segmentation of images of diseased leaves has become a leading research theme all over the world. In this paper, a dataset from FGVC7, which is an open, source dataset and contains four different classes of leaf diseases of the apple crop is used to measure the performance of the proposed model. A three, step image preprocessing procedure is conducted, which involves image enhancement, removal of background noise, and object identification through a variety of approaches. Using UNet, PSPNet, and UNet++, disease areas are identified. UNet + + delivering the most precise object segmentation. The segmented patch data are then fed into a fusion, based classification model as input. Patch segmentation makes it possible to separate the various disease areas, thus increasing the classification accuracy. The proposed framework combines several CNN architectures, namely, VGG16, InceptionV3, AlexNet, and GoogleNet, in order to take advantage of their complementary strengths, then a Vision Transformer (ViT) is used to identify distinct local features via early feature fusion. After this, the efficiency of the proposed model is check in comparison with the pre-trained CNN models individually. Comparing with all other models the proposed model has shown the accuracy of highest order i.e. 98.85%, clearly out performing individual CNN models.","目前，农业中的生物和非生物因素估计约占作物生产总损失的22%。因此，农民尽早识别这些限制因素非常重要，但同时也非常困难。计算机视觉的最新发展使得在初级水平上识别和分类植物病变成为可能。各种深度学习技术使得以极高准确度检测苹果植株叶片的生物胁迫成为可能。因此，患病叶片图像的自动分割已成为世界范围内的前沿研究主题。本文使用来自FGVC7的数据集来衡量所提出模型的性能，该数据集是一个开放源数据集，包含苹果作物的四类不同叶片病害。进行了三步图像预处理，包括图像增强、背景噪声去除以及通过多种方法进行目标识别。使用UNet、PSPNet和UNet++识别病害区域，其中UNet++提供了最精确的目标分割。然后将分割后的图像块数据作为输入馈入基于融合的分类模型。图像块分割使得能够分离不同的病害区域，从而提高分类准确率。所提出的框架结合了多种CNN架构，即VGG16、InceptionV3、AlexNet和GoogleNet，以利用它们互补的优势，然后使用视觉Transformer（ViT）通过早期特征融合来识别不同的局部特征。此后，将所提出模型的效率与各个预训练CNN模型进行比较。与所有其他模型相比，所提出的模型显示出最高阶的准确率，即98.85%，明显优于各个单独的CNN模型。","Discover Computing","2026-10-10T00:00:00Z",69,{"impact":19,"substance":18,"depth":210,"authority":212,"freshness":130,"relevant":21,"comment":283},"方法组合（UNet++分割+CNN与ViT融合）与98.85%准确率有参考价值，但属实验室数据集验证，产业落地影响有限，可作为技术前沿资讯收录。",[285],{"name":279,"url":276},[98,99,287,117,288],"计算机视觉","苹果病害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[291],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":293},{"candidates":294,"agricultural_evidence":52},[295,300,304],{"code":39,"score":18,"supported":52,"title":296,"abstract":297,"tags":299},[119],[117,287,119,298],"computer vision",[99,117,287],{"code":32,"score":92,"supported":52,"title":301,"abstract":302,"tags":303},[127],[124,127],[],{"code":137,"score":21,"supported":14,"title":305,"abstract":306,"tags":307},[],[],[98],[309,310],"苹果叶片病害 深度学习","FGVC7 苹果病害 数据集","苹果叶片病害深度学习-4744","10.1007\u002Fs10791-026-10573-4",{"doi":312,"openalex_id":314,"authors":315,"venue":279,"cited_by_count":190,"oa_url":331,"card":332,"direction":197,"ingested_from":198},"W7221353656",[316,318,321,323,325,328],{"name":317,"orcid":8},"Apoorva Arora",{"name":319,"orcid":320},"Vinay Gautam","https:\u002F\u002Forcid.org\u002F0000-0002-0258-5132",{"name":322,"orcid":8},"Anand Muni Mishra",{"name":324,"orcid":8},"Santar Pal Singh",{"name":326,"orcid":327},"Yadvendra Pratap Singh","https:\u002F\u002Forcid.org\u002F0000-0001-6396-3355",{"name":329,"orcid":330},"Mukund Pratap Singh","https:\u002F\u002Forcid.org\u002F0000-0001-5877-9510","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10791-026-10573-4.pdf",{"tldr":333,"method":334,"finding":335,"direction":195,"opportunity":336},"用UNet++分割苹果病叶并融合多种CNN与ViT分类，实现98.85%识别准确率。","FGVC7数据集，UNet\u002FPSPNet\u002FUNet++分割，VGG16等CNN与","UNet++分割最准，融合模型准确率达98.85%，优于单个CNN模型。","可探索轻量化分割-分类联合模型在田间移动端的实时部署与多作物泛化。","2026-10-12T23:30:09.091475Z",{"id":339,"title":340,"url":341,"summary":342,"summary_zh":343,"content":8,"source_name":344,"source_url":341,"published_at":345,"category":12,"cover_url":8,"hotness":346,"is_selected":14,"score":347,"score_detail":348,"sources":350,"tags":354,"primary_topic":356,"secondary_topics":357,"classification":359,"search_phrases":374,"slug":377,"view_count":21,"doi":378,"paper":379,"created_at":392},4241,"Detection of Apple Plant Diseases Using Leaf Images through Convolutional Neural Network","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23142591","Plant diseases have major social, ecological and economic impact on agriculture and frequently results in large financial losses for farmers. Therefore early and precise disease detection is crucial for efficient crop management. The severity of leaf diseases and their direct impact on yield quality have been highlighted in recent studies. Both digital cameras and flatbed scanners can be used to take leaf images but they have certain drawbacks. Although scanners offer a more secure and regulated method of acquiring images they are comparatively slow and may cause edge shadows. This study uses a hybrid deep learning approach to identify diseases in apple plant leaves. To improve classification performance; the proposed approach combines the advantages of Residual Networks (ResNet), Genetic Algorithms (GA) and Convolutional Neural Networks (CNN). CNNs are used to extract features and classify diseases whereas ResNet enhances the learning efficiency of deeper networks. The feature set is optimized using a genetic algorithm which improves accuracy and lowers computational complexity. The model also calculates the percentage of the leaf area that is impacted. According to experimental results the proposed method detects leaf diseases with high accuracy of percentage and low computational cost making it a dependable and effective precision agriculture solution. The proposed model exhibited strong classification efficiency across various leaf conditions that pertaining an accuracy of 0.97 for healthy leaves, 0.994 for apple scab, 0.89 for rust, and 0.91 for powdery mildew.","植物病害对农业产生重大的社会、生态和经济影响，并经常给农民带来巨大的经济损失。因此，早期且精确的病害检测对于高效的作物管理至关重要。近期研究强调了叶部病害的严重程度及其对产量品质的直接影响。数码相机和平板扫描仪均可用于拍摄叶片图像，但它们各有一定缺陷。尽管扫描仪提供了更安全、更可控的图像获取方式，但其速度相对较慢，且可能产生边缘阴影。本研究采用混合深度学习方法识别苹果植株叶部病害。为提高分类性能，所提方法结合了残差网络（ResNet）、遗传算法（GA）和卷积神经网络（CNN）的优势。CNN用于提取特征并分类病害，而ResNet增强了更深网络的学习效率。利用遗传算法对特征集进行优化，从而提高准确率并降低计算复杂度。该模型还计算了受影响的叶面积百分比。实验结果表明，所提方法以高准确率和低计算成本检测叶部病害，使其成为可靠且有效的精准农业解决方案。所提模型在多种叶片条件下均表现出较强的分类效率，健康叶片准确率为0.97，苹果黑星病为0.994，锈病为0.89，白粉病为0.91。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-04T00:00:00Z",25,67,{"impact":19,"substance":17,"depth":210,"authority":19,"freshness":93,"relevant":21,"comment":349},"基于ResNet+GA+CNN的苹果叶病识别研究，准确率数据具体，方法有增量，但属实验室论文，产业影响有限。",[351,352],{"name":344,"url":341},{"name":344,"url":353},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23142592",[98,99,117,140,355],"苹果病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[358],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":360},{"candidates":361,"agricultural_evidence":52},[362,366,370],{"code":32,"score":234,"supported":52,"title":363,"abstract":364,"tags":365},[127],[124,127],[355],{"code":137,"score":130,"supported":52,"title":367,"abstract":368,"tags":369},[],[140,141],[98,140],{"code":39,"score":130,"supported":52,"title":371,"abstract":372,"tags":373},[],[117,119],[99,117],[375,376],"苹果叶片病害 卷积神经网络","苹果锈病 白粉病 识别","苹果叶片病害卷积神经网络-4241","10.5281\u002Fzenodo.23142591",{"doi":378,"openalex_id":380,"authors":381,"venue":344,"cited_by_count":190,"oa_url":341,"card":386,"direction":391,"ingested_from":198},"W7219637192",[382,384],{"name":383,"orcid":8},"Mohd Arshad Raza",{"name":385,"orcid":8},"Kiran Pandey",{"tldr":387,"method":388,"finding":389,"direction":195,"opportunity":390},"提出ResNet+GA+CNN混合模型，用苹果叶片图像检测病害并计算病斑面积占比。","ResNet提取深层特征，遗传算法优化特征集，CNN分类，苹果叶片图像数据。","健康叶、黑星病、锈病、白粉病准确率分别达0.97、0.994、0.89、0.91，计算成本低。","可探索轻量化模型在田间移动端实时检测，并融合多病害严重度分级与产量损失预测。","数字乡村与农业信息化","2026-10-05T23:30:30.830366Z",{"id":394,"title":395,"url":396,"summary":397,"summary_zh":398,"content":8,"source_name":399,"source_url":396,"published_at":400,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":208,"score_detail":401,"sources":403,"tags":405,"primary_topic":409,"secondary_topics":410,"classification":412,"search_phrases":427,"slug":430,"view_count":21,"doi":431,"paper":432,"created_at":452},3811,"Automated extraction of architectural traits from apple tree branches via 3D instance segmentation and skeletonisation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104607","Accurate and efficient extraction of 3D phenotypic traits of apple tree branches is crucial for apple tree breeding and growth monitoring. Current methods, however, rely heavily on manual measurement, which are time-consuming, costly, and difficult to scale. Moreover, existing approaches based on deep learning and skeleton extraction still face critical challenges when processing high-density, large-scale point cloud data, including insufficient branch segmentation accuracy, suboptimal skeleton extraction quality, and a lack of category-specific fine-grained phenotypic quantification capabilities. To overcome these limitations, a dedicated framework for automated phenotypic trait extraction was presented. This approach consists of three key steps: (1) instance segmentation of branches from 3D data using the Relation3D model to distinguish individual branches; (2) skeletonisation of each segmented branch based on geometric characteristics and the L1-median algorithm to obtain fine-grained skeletal structures; and (3) quantitative trait extraction, where trunk height, branch count by category, and branch length are automatically computed. Experimental results demonstrate that Relation3D achieves high-precision instance segmentation, and our skeletonisation method reliably recovers branch geometry. Consequently, the framework accurately extracts key phenotypic traits, including branch number and length. This study provides an efficient and accurate solution for branch-level phenotyping, with significant potential to enhance apple tree breeding and precision horticulture.","准确高效地提取苹果树枝条的三维表型性状对于苹果树育种和生长监测至关重要。然而，现有方法严重依赖人工测量，耗时、成本高且难以规模化。此外，基于深度学习和骨架提取的现有方法在处理高密度、大规模点云数据时仍面临关键挑战，包括枝条分割精度不足、骨架提取质量欠佳以及缺乏类别特定的细粒度表型量化能力。为克服这些局限，本文提出了一种用于自动化表型性状提取的专用框架。该方法包括三个关键步骤：（1）利用Relation3D模型从三维数据中对枝条进行实例分割，以区分单个枝条；（2）基于几何特征和L1中位数算法对每个分割后的枝条进行骨架化，以获得细粒度骨架结构；（3）定量性状提取，自动计算主干高度、按类别统计的枝条数量和枝条长度。实验结果表明，Relation3D实现了高精度的实例分割，且本文的骨架化方法能够可靠地恢复枝条几何形态。因此，该框架能够准确提取关键表型性状，包括枝条数量和长度。本研究为枝条级表型分析提供了一种高效且准确的解决方案，在提升苹果树育种和精准园艺方面具有显著潜力。","Biosystems Engineering","2026-09-29T00:00:00Z",{"impact":234,"substance":211,"depth":17,"authority":92,"freshness":93,"relevant":21,"comment":402},"提出基于Relation3D实例分割与L1中值骨架化的苹果树枝条三维表型自动提取框架，方法新颖、结论可靠，对果树育种与精准园艺有实用价值。",[404],{"name":399,"url":396},[98,99,406,407,408],"三维点云","果树表型","苹果育种",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[411],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":413},{"candidates":414,"agricultural_evidence":52},[415,419,423],{"code":32,"score":228,"supported":52,"title":416,"abstract":417,"tags":418},[127],[34,124,127],[],{"code":39,"score":130,"supported":52,"title":420,"abstract":421,"tags":422},[],[117,119],[406,99],{"code":137,"score":21,"supported":14,"title":424,"abstract":425,"tags":426},[],[],[98],[428,429],"苹果树 三维点云 表型","苹果树 分枝 骨架提取","苹果树三维点云表型-3811","10.1016\u002Fj.biosystemseng.2026.104607",{"doi":431,"openalex_id":433,"authors":434,"venue":399,"cited_by_count":190,"oa_url":396,"card":447,"direction":270,"ingested_from":198},"W7214750336",[435,438,441,443,445],{"name":436,"orcid":437},"Anjian Fei","https:\u002F\u002Forcid.org\u002F0009-0000-9349-9223",{"name":439,"orcid":440},"Jintao Wu","https:\u002F\u002Forcid.org\u002F0000-0003-4257-2288",{"name":442,"orcid":8},"Jian Zhuang",{"name":444,"orcid":8},"Leyou Yang",{"name":446,"orcid":8},"Hao Yang",{"tldr":448,"method":449,"finding":450,"direction":270,"opportunity":451},"提出3D实例分割与骨架化框架，自动提取苹果树枝条表型性状。","Relation3D实例分割、L1中值骨架化、高密度点云处理。","框架能高精度分割枝条并可靠提取枝长、枝数等表型性状。","可扩展至多树种、多尺度动态监测，并与基因组关联推动智能育种。","2026-09-30T23:30:05.626390Z",{"id":454,"title":455,"url":456,"summary":457,"summary_zh":458,"content":8,"source_name":459,"source_url":456,"published_at":460,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":461,"score_detail":462,"sources":464,"tags":466,"primary_topic":468,"secondary_topics":469,"classification":472,"search_phrases":499,"slug":502,"view_count":76,"doi":503,"paper":504,"created_at":528},3167,"Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-65828-3","Accurate estimation of horticultural crop acreage is essential for agricultural planning, market forecasting and evidence-based policy formulation. The present study investigated long-term trends in mango cultivation and developed a multi-temporal remote sensing framework for mango orchard acreage estimation in Navsari District using integrated optical, SAR and machine learning approaches. Time-series (temporal) data spanning 23 years (2001–02 to 2023–24) were analyzed using polynomial regression models to evaluate trends in mango area and production. Linear regression best represented area expansion trends (Adj. R² = 0.969), whereas cubic regression better captured production variability (Adj. R² = 0.634), indicating climatic and seasonal influences on productivity. For orchard classification and acreage estimation, multi-temporal Sentinel-2 imagery acquired from November 2023 to March 2024 was processed within a phenology-guided framework. Monthly composites were generated and integrated with Sentinel-1 SAR backscatter data, vegetation indices (NDVI, GNDVI, NDRE, SAVI, EVI and NDMI) and texture metrics derived from Gray Level Co-occurrence Matrix (GLCM) analysis. A comprehensive 72-band feature stack was developed for classification. Four machine learning algorithms, namely Random Forest (RF), XGBoost (XGB), Support Vector Machine (SVM) and Multinomial Logistic Regression (MNLR) were evaluated for orchard discrimination. Among the tested models, RF achieved the highest classification performance with an Overall Accuracy of 99.80% and a Kappa coefficient of 0.997, followed by SVM (99.30%), MNLR (98.21%) and XGB (9.20%). The RF model estimated mango orchard area at 36,099.94 ha, showing the closest agreement with official horticultural statistics (34,363 ha) with only 5.05% estimation error. In contrast, SVM and MNLR overestimated orchard extent by 16.03% and 43.37%, respectively. The proposed framework provides a reliable and scalable methodology for operational horticultural monitoring, crop inventory generation and precision agricultural planning in tropical orchard ecosystems.","准确估算园艺作物种植面积对于农业规划、市场预测和循证政策制定至关重要。本研究探讨了芒果种植的长期趋势，并开发了一个多时相遥感框架，结合光学、合成孔径雷达（SAR）和机器学习方法，用于纳夫萨里县芒果园种植面积估算。利用多项式回归模型分析了跨越23年（2001—02年至2023—24年）的时间序列数据，以评估芒果面积和产量的变化趋势。线性回归最能表征面积扩张趋势（调整R² = 0.969），而三次回归更能捕捉产量变异性（调整R² = 0.634），表明气候和季节性因素对生产力具有影响。在果园分类和面积估算方面，基于物候指导框架处理了2023年11月至2024年3月获取的多时相Sentinel-2影像。生成了月度合成影像，并将其与Sentinel-1 SAR后向散射数据、植被指数（NDVI、GNDVI、NDRE、SAVI、EVI和NDMI）以及基于灰度共生矩阵（GLCM）分析提取的纹理指标进行整合。构建了一个包含72个波段的综合特征集用于分类。评估了四种机器学习算法，即随机森林（RF）、XGBoost（XGB）、支持向量机（SVM）和多项逻辑回归（MNLR），用于果园判别。在测试的模型中，RF取得了最高的分类性能，总体精度为99.80%，Kappa系数为0.997，其次是SVM（99.30%）、MNLR（98.21%）和XGB（9.20%）。RF模型估算的芒果园面积为36,099.94公顷，与官方园艺统计数据（34,363公顷）最为接近，估算误差仅为5.05%。相比之下，SVM和MNLR分别高估了果园面积16.03%和43.37%。所提出的框架为热带果园生态系统中的业务化园艺监测、作物清单生成和精准农业规划提供了一种可靠且可扩展的方法。","Scientific Reports","2026-09-22T00:00:00Z",78,{"impact":92,"substance":91,"depth":17,"authority":92,"freshness":13,"relevant":21,"comment":463},"方法扎实、数据规模大且精度高，但属区域性作物遥感估产研究，产业影响有限，可作为技术方法类精选。",[465],{"name":459,"url":456},[98,59,125,218,467],"作物估产",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[470,471],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"code":107,"slug":108,"name":109,"path":110,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":473},{"candidates":474,"agricultural_evidence":52},[475,479,483,488,493],{"code":32,"score":234,"supported":52,"title":476,"abstract":477,"tags":478},[126],[125,126],[125],{"code":39,"score":234,"supported":52,"title":480,"abstract":481,"tags":482},[118],[59,118],[59],{"code":107,"score":130,"supported":52,"title":484,"abstract":485,"tags":487},[],[102,486],"remote sensing",[467,218],{"code":137,"score":489,"supported":52,"title":490,"abstract":491,"tags":492},4,[],[140],[98],{"code":494,"score":76,"supported":52,"title":495,"abstract":496,"tags":498},"J1",[],[497],"农业规划",[],[500,501],"Navsari 芒果 遥感估产","Sentinel-2 芒果 果园面积","Navsari芒果遥感估产-3167","10.1038\u002Fs41598-026-65828-3",{"doi":503,"openalex_id":505,"authors":506,"venue":459,"cited_by_count":190,"oa_url":456,"card":523,"direction":270,"ingested_from":198},"W7213920056",[507,509,512,514,516,519,521],{"name":508,"orcid":8},"V. Raju",{"name":510,"orcid":511},"Yogesh A. Garde","https:\u002F\u002Forcid.org\u002F0000-0002-0297-316X",{"name":513,"orcid":8},"Dr. V. S. Thorat",{"name":515,"orcid":8},"V. T. Shinde",{"name":517,"orcid":518},"Nitin Varshney","https:\u002F\u002Forcid.org\u002F0000-0001-9144-5475",{"name":520,"orcid":8},"Alok Shrivastava",{"name":522,"orcid":8},"A. P. Chaudhary",{"tldr":524,"method":525,"finding":526,"direction":270,"opportunity":527},"融合多时相Sentinel-1\u002F2与机器学习，估算印度芒果园面积并分析23年种植趋势。","23年时序回归分析；Sentinel-2月合成+SAR+植被指数+GLCM纹理共","RF精度最高（总体精度99.80%，Kappa 0.997），面积估算误差仅5.05%，优于SVM和","可迁移该多时相SAR-光学特征框架至其他热带果园，并探索深度学习与物候自适应特征优化。","2026-09-22T23:30:22.619289Z"]