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[学术文献 ] 深度学习在林果品质无损检测中的研究进展 进入全文
农业工程学报；
林果品质与消费者和果农密切相关，在保障消费者安全、优化运输贮藏、改善后续分级、实现优质优价、提高果农收益等方面均有重要作用，然而传统的林果品质检测方法存在效率低、检测范围有限和鲁棒性差等问题。近年来，随着深度学习的迅猛发展，林果品质无损检测技术也取得了突破性的进步。该研究梳理了目前主流的深度学习模型及其作用，然后从林果安全品质、外部品质和内部品质3个方面阐述了深度学习的研究进展，发现卷积神经网络是林果品质无损检测领域应用最广泛的深度学习模型。同时结合深度学习应用现状与模型不足分析了该领域仍存在数据质量低、模型泛化能力差、实际部署困难等问题，提出未来应围绕数据增强与评价指标、建立公共数据集、多模态融合、模型融合、元学习、拓展应用、模型压缩等方面展开研究。该文旨在为深度学习在林果品质无损检测中的进一步发展提供参考，以加快林果产业的数字化、信息化进程。
[学术文献 ] 花生机械化收获装备与技术研究进展 进入全文
农业机械学报
花生收获季节性强，人工作业劳动强度高，效率低，收获损失大，花生生产需要依靠成熟的机械化收获技术。中国的花生收获机械化水平在花生机械化生产环节中处于较低水平，严重制约了中国花生机械化水平的整体提高。本文在阐述花生收获作业模式为联合收获作业模式、两段式收获作业模式和三段式收获作业模式的基础上，对中国花生机械化收获的挖掘装置和摘果装置进行了系统归纳总结，并阐述了输送链式花生收获机、条铺式花生收获机、挖掘翻秧花生收获机、半喂入和全喂入式花生联合收获机的性能和特点。同时，分析了美国花生机械化收获技术，并对印度花生收获机进行了简述。最后，在总结花生机械化收获装备特点的基础上，分析了中国花生收获机械存在的问题，并对未来发展趋势进行了展望。指出花生机械化收获装备将进入以智能化、精细化、高效化为主导的新阶段。
[学术文献 ] 农业装备行驶滑动辨识与控制研究现状与展望 进入全文
农业机械学报
农业环境中农业装备时常发生行驶滑动现象且具有明显不确定性，滑动现象使行驶机构处于不可控状态，从而影响作业精度，严重阻碍了种植、中耕管理和收获等需要精准作业环节的农业装备信息化及智能化发展。本文从滑动原理、滑动辨识及行驶滑动控制方面，分别对滑动力学特性、滑动辨识方法和考虑滑动的路径跟踪控制的国内外研究现状进行综述。滑动原理方面，着重阐述了针对不同行驶机构的结构特点和行驶地面环境建立的多种行驶机构与地面的系统模型。滑动辨识方面，分别对基于数学模型和基于数据驱动两类方法进行分析，揭示各方法优势与局限性。行驶滑动控制方面，重点归纳了应用于农业装备的路径跟踪控制方法，指出了目前行驶滑动控制研究方法局限性。最后，指出行驶滑动辨识研究对于农业装备自动化发展具有重要意义，未来农业装备行驶滑动研究可以从滑动力学理论模型、滑动实时辨识方法、行驶滑动控制方法等方面开展深入研究。
[学术文献 ] 智能时代呼唤新的科研方法 进入全文
科技导报
智能化科研（AI4R）是科研方法的重大变革。提出科技界不仅要关注科学智能（AI for Science,AI4S），更要重视技术智能（AI for Technology,AI4T）；不仅要关注大语言模型（LLM），更要重视大科学模型（LSM）。同时提出，人工智能的突破主要不是靠大算力，而是计算模型的转变，中国应当争取在基础模型上做出颠覆性的创新；智能化科研适合做复杂问题的组合搜索，神经网络模型也许已接近能处理困难问题的复杂度阈值点；智能化科研的一种趋势是放弃绝对性，拥抱不确定性，一定时期内要适当容忍“黑盒模型”。
[学术文献 ] Farmers' acceptance of robotics and unmanned aerial vehicles: A systematic review 进入全文
AGRONOMY JOURNAL
Recently, new agricultural technologies have emerged to increase sustainable food production, often referred to as "Agriculture 4.0." They consist of more advanced precision agriculture technologies, such as sensors, robotics, and unmanned aerial vehicles (UAVs), which can lead to environmental and economic benefits for farmers and society. Despite the potential benefits of Agriculture 4.0 technologies, the adoption rate remains low. The objective of this study was to gain insight into the factors affecting the adoption of agricultural field robotics and UAVs using a systematic literature review approach that analyzed 23 relevant studies identified from two scientific databases (i.e., Web of Science and Scopus). Data regarding methodological aspects and results were extracted. Even though the current adoption of agricultural robots and UAVs is still limited, farmers seem highly interested. Most studies have used a quantitative approach to study the intention to adopt or the adoption of robotics or UAVs. Surprisingly, few studies have used an existing theory to explain the (intended) adoption. Age, gender, income, education, farm size, perceived usefulness, expected economic and environmental benefits, an attitude of confidence, and the perceived ease of use have been the internal factors most often identified. Meanwhile, the price of the technology, compatibility with other software and equipment, and labor scarcity are the most important external factors.
[学术文献 ] Robotic arms in precision agriculture: A comprehensive review of the technologies, applications, challenges, and future prospects 进入全文
Computers and Electronics in Agriculture
In precision agriculture, robotic arms exhibit significant technical advantages, such as enhancing operational precision and efficiency, reducing labor costs, and supporting environmental sustainability. This paper provides a comprehensive overview of the application of ground-based robotic arms in precision agriculture, analyzing the hardware and software aspects and current application status across various agricultural settings, and discussing challenges and prospects in this field. First, this paper explores precision agriculture and agricultural robotic arms, highlighting their critical roles in enhancing agricultural efficiency and automation. Further, it addresses the challenges plaguing the practical applications of robotic arms and compares innovative robotic arm technologies with traditional models to establish a foundation for understanding these advancements in modern agriculture. Additionally, this paper analyses the hardware of robotic arms, including rigid and flexible manipulators, drivers, end-effectors, sensors, and controllers, emphasizing the importance of innovation and optimization for improved performance. For the software systems, this paper focused on classic workflows and advanced algorithms for perception, motion planning, and control, as these are essential for the precise and adaptable functioning of robotic arms in diverse agricultural environments. Furthermore, this paper reviews the research and application status of robotic arms across various settings, including greenhouses (e.g., ground planting, desktop planting, and vertical planting), fields (e.g., dry fields, moist, and paddy fields), and orchards (e.g., fruit tree orchards, vineyard orchards, and ground-level orchards) to demonstrate their broad applicability and efficient operational capabilities in diverse conditions. Lastly, this paper discusses the challenges and prospects of robotic arms, emphasizing the significance of integrating disciplines, such as agronomy and biomimetics, big data, artificial intelligence, digital twinning, and human-machine interaction. Advancements in these areas are pivotal for the progress of robotic arm technology and for introducing innovative, efficient solutions to precision agriculture. In summary, this review reveals the immense potential of the application of robotic arms in precision agriculture. With ongoing technological advancements, these robotic arms are expected to play an increasingly crucial role in future agricultural production, making substantial contributions to achieving more efficient, innovative, and sustainable farming practices, heralding a new era in agricultural technology. This paper will serve as a valuable guide for researchers and practitioners, offering comprehensive insights into the use of robotic arms in precision agriculture and providing essential knowledge for advancing the field.
