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[前沿资讯 ] 政府介入订单农业供应链金融的理论支撑和路径优化 进入全文
CNKI:老字号品牌营销
高效合作机制的构建是目前我国农业供应链订单融资急需解决的难题。要使经济社会发展要素的合作成为现实，在农业供应链中必须强调建立一个多主体共同参与、多利益合作驱动的长效机制，发挥比较优势，管控利益分歧，实现利益主张，形成合作共赢的格局。本文基于主体利益分析，通过构建APT-R因子模型，阐述农业供应链金融合作的动力机制，揭示政府介入订单合作机制的功能与作用。
[学术文献 ] 人工智能技术在现代农业机械中的应用研究 进入全文
cnki:南方农机
在农业生产活动中积极应用现代农业机械，是实现农业现代化发展的重要途径。基于此，笔者结合多年的基层工作实践，对人工智能技术在现代农业机械中的运用进行了研究，指出了人工智能技术对现代农业机械发展的意义，并就如何更好地将人工智能技术应用到现代农业机械中进行了阐述。研究结果表明，在人工智能技术快速发展的今天，将人工智能技术应用到现代农业机械中，有利于农业经济结构的调整，提高了农业生产效率和农民收入。
[学术文献 ] 基于STM32的智慧农业大棚系统设计 进入全文
CNKI:现代电子技术
为合理利用有限的土地和农业资源，降低农业生产成本，改善农业生态环境，文中以STM32为主控芯片，结合各类智能传感器、外部控制设备、智慧农业监测APP、大数据分析和智能预警，设计一种基于STM32的智慧农业大棚监测系统。该系统能实时、精准地监测大棚室内各项环境参数，并融合农作物最优生长模型，自动调控卷帘、风机、补光灯、喷洒等农业设施，以低成本、多维度进行环境监测和预测。智慧农业大棚系统设计不仅可为农作物提供最优的生长环境，为现代农业提供针对性、精细化、适用性强的综合农业服务；还能借助大数据、物联网、人工智能等新兴技术，实现对农业大棚精准控制，使农业生产更具“智慧”。实践证明，智慧农业大棚能够科学地提高农业种植生产效率，改善及优化农业产品质量，大幅度提升农业收益率。
[前沿资讯 ] 容祺智能：打造国内无人机行业新势力 进入全文
CNKI:广东科技
广东容祺智能科技有限公司（以下简称“容祺智能”）作为国内工业无人机领域的国家高新技术企业，致力于提供工业无人机系统应用解决方案，推动我国无人机产业更好更快发展。近年来，容祺智能的科技创新和市场业务等均得到快速发展，逐渐成为了国内工业无人机领域的一支重要新生力量。本刊记者就容祺智能发展有关情况采访了其创始人兼董事长陈建伟。
[学术文献 ] Understanding technology acceptance in smart agriculture: A systematic review of empirical research in crop production 进入全文
SD
Smart agriculture offers the potential to analyse agricultural data at a scale not previously possible. Researchers argue that the combination of rich data and intelligent decision support has the potential to improve productivity and profitability in agriculture, whilst also improving sustainability. We argue that achieving this potential requires not just on technological advancement, it also requires a detailed understanding of factors that impact technology acceptance in smart agriculture. Acceptance is necessary if technical advances are to translate into real-world impact. However, technology acceptance is complex and often poorly understood. This systematic review focuses on technology acceptance in prediction and decision support systems in crop production. Major databases were searched to identify papers that formally address technology acceptance and include detailed data. 16 papers met the inclusion criteria and were included in the final analysis. Common facilitators and barriers are identified, and papers are mapping against the Theoretical Framework of Acceptability. This analysis showed that constructs including perceived effectiveness are addressed frequently, but others such as opportunity costs and burden have received less attention. The findings suggest the necessity for greater application of formal methods and the need for standardized, domain-specific methods to support this assessment.
[学术文献 ] AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture 进入全文
SD
Precision Agriculture (PA) promises to boost crop productivity while reducing agricultural costs and environmental footprints, and therefore is attracting ever-increasing interests in both academia and industry. This management strategy is underpinned by various advanced technologies including Unmanned Aerial Vehicle (UAV) sensing systems and Artificial Intelligence (AI) perception algorithms. In particular, due to their unique advantages such as a low cost, high spatio-temporal resolutions, flexibility, automation functions and minimized risk of operation, UAV sensing systems have been extensively applied in many civilian applications including PA since 2010. In parallel, AI algorithms (deep learning since 2012 in particular) are also drawing ever-increasing attention in different fields, since they are able to analyse an unprecedented volume/velocity/variety of data (semi-) automatically, which are also becoming computationally practical with the advancements of cloud computing, Graphics Processing Units and parallel computing. In this survey paper, therefore, a thorough review is performed on recent use of UAV sensing systems (e.g., UAV platforms, external sensing units) and AI algorithms (mainly supervised learning algorithms) in PA applications throughout the crop life-cycle, as well as the challenges and prospects for future development of UAVs and AI in agriculture sector. It is envisioned that this review is able to provide a timely technical reference, demystifying and promoting research, deployment and successful exploitation of AI empowered UAV perception systems for PA, and therefore contributing to addressing future agricultural and human nutrition challenges.
