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[前沿资讯 ] AI-Accelerated Idea for App That Supports Farmers Wins Red Bull Basement World Final in Tokyo 进入全文
Red Bull Basement；Global Ag Tech Initiative；
Red Bull Basement, the global event empowering a new generation of innovators to launch world-changing, AI-accelerated startups, has crowned its winner for 2024: Team AgriConnect from the Philippines. The award ceremony was the culmination of a three-day World Final that brought national-winning teams from nearly 40 countries to Tokyo, Japan. More than 110,000 applications were submitted worldwide for the 2024 edition of Red Bull Basement. From ideation to execution, the national winners invited to Tokyo developed the skills, confidence, and networks needed to turn their visions into reality. The World Final was an intensive opportunity featuring workshops, AI training, and networking with international entrepreneurs, innovators, coaches and business leaders such as Jessica Hawk, Corporate Vice President for Data, AI, and Digital Applications at Microsoft, Amar Al Naimi, a Red Bull esports professional who has founded several businesses, and many more. Hawk said, “At Microsoft, our mission is to empower every person and every organization on the planet to achieve more. There’s a creative energy that these young people are bringing and connecting their creativity with the possibilities of generative AI – there’s magic in that. I expect that some truly legendary experiences are going to come from this type of forum.” On the concluding day, the teams showcased their ideas to the world in an Idea Gallery exhibition, and the Top 10 made a pitch to the global judging panel: Hans Yang, head of Microsoft for Startups; Letizia Royo-Villanova, early-stage investor at renowned Silicon Valley-based VC firm and accelerator Plug and Play; Jun Yuh, an influential entrepreneur and business mentor; Ryan Sagare, host and producer of the AMD Meet the Experts webinar series; and Molly Carlson, Red Bull cliff diving athlete and entrepreneur. The Top 10 ideas ranged from a mental strength training app empowering athletes to a sustainable vehicle battery, but it was AgriConnect founder Aldrin Sojourner Gamayon who captured the judges’ vote. The ingenious idea for an agriculture tracking app that allows even remote farmers to monitor their crops, build resilience and increase yield was inspired by the innovator’s own family. As the winner, Gamayon will embark on a three-week, all-expenses-paid trip to the California technology hub of Silicon Valley, where he will be immersed in a program of networking and mentorship with tech and VC leaders. “This means so much, because I’m doing it beyond myself. I’m doing this for Filipino farmers,” Gamayon said. Looking at his trophy, he added, “This wasn’t just about competing or winning. It’s about sharing moments and memories with people who are like-minded. I share this with all the other teams who are here.” Previous Red Bull Basement winner Audvice from Austria – an AI-powered private podcasting platform for HR and educational professionals – has gone on to secure multi-million-dollar investments, partner with global brands and attract a transformative acquisition deal. Red Bull Basement collaborated with Microsoft and AMD for this 2024 edition, focusing strongly on AI technologies to deliver the best possible experience. The Red Bull Basement Chatbot, powered by Microsoft, uses Azure OpenAI Service which includes AMD Instinct™ Accelerators and AMD EPYC™ Processors as part of its hardware infrastructure, to assist teams in developing ideas and creating business plans. These components are integral to the service’s ability to handle complex AI workloads efficiently. In addition, the national winners received an AI-enabled laptop with the latest AMD Ryzen™ AI processor technology to utilise Microsoft’s AI technologies during the critical Development Phase as they prepared for the World Final in Tokyo, Japan.
[前沿资讯 ] Drones Giving Produce Growers a New Perspective on Crop Scouting 进入全文
UNH Today；Global Ag Tech Initiative；
New research at the University of New Hampshire is assessing whether drones could be a tool for small and medium-sized New England farms to identify plant disease pressure earlier, more accurately and at a lower cost. The research represents a collaboration among New Hampshire Agricultural Experiment Station scientists, operations staff at UNH Farm Services, UNH Cooperative Extension field specialists, and a commercial crop advisor, who initially proposed the idea based on his observations about challenges faced by northern New England dairy farmers and the research expertise at the UNH College of Life Sciences and Agriculture. Harnessing Drone Technology for Crop Scouting and Monitoring Using drones, or Unpiloted Aerial Vehicles (UAVs), equipped with multispectral cameras, the team monitored for early signs of disease in test plots that contain both BMR and non-BMR corn varieties. The study tested the possibility of detecting fungal diseases — such as the Northern corn leaf blight (NCLB) — in brown mid-rib (BMR) corn varieties potentially weeks before it becomes visible to the naked eye. “When harvest time comes, often the BMR corn is brown from top to bottom — it doesn’t have the disease resistance of non-BMR corn, and that’s a big issue for farmers,” explains Tom Beaudry, a Commercial Crop Advisor who works with farmers across New Hampshire as well as in Massachusetts and Vermont. “From my perspective, drones provide a completely new way to look at a cornfield,” Beaudry adds. “I’m used to being on the ground, but seeing the fields from the air allows us to spot problems much earlier—sometimes before they’re even visible on the plant.” Making Technology Accessible to Small Farms Many existing UAV systems for agriculture are prohibitively expensive for small farms, often costing tens of thousands of dollars. A primary goal of this multi-year study is to determine how to lower these costs while ensuring the technology remains effective. According to Beaudry, drones are currently used on larger Midwest farms for disease and weed scouting purposes, both by individual farms and crop advising companies, in part because the costs can be spread over more acres. The research team believes that if they can show the effectiveness of using lower-cost drones that can provide technically accessible data could significantly help small and medium-sized New England farmers more sustainably manage their crops. Early disease detection can help farmers apply treatments sooner, optimize application quantities, adjust harvest schedules and make better crop management decisions, ultimately reducing yield losses and improving economic returns and food-production resiliency. “Scouting crops has always been important but having a perspective from a few hundred feet up allows you to see patterns in the field that you might otherwise miss,” says Carl Majewski, a UNH Extension field specialist and member of the research group. “Drones could help farmers identify issues with crop health, soil health, or weed infestations, and it would be easier to see changes over time.” For more, continue reading at unh.edu/unhtoday.
[前沿资讯 ] Muddy Machines Acquires Key Assets and IP from Fox Robotics 进入全文
Muddy Machines；Global Ag Tech Initiative；
Muddy Machines has announced that it has successfully acquired the majority of key assets and intellectual property from Fox Robotics, including its farm logistics robots. This strategic acquisition positions Muddy Machines as the go-to provider of robotic vehicles for use on farms, diversifies the product portfolio, and allows the business to offer a broader range of solutions to growers. “We are thrilled to integrate Fox Robotics’ innovative vehicle into our lineup,” said Chris Chavasse, CEO of Muddy Machines. “Their technology aligns perfectly with our mission to solve labor challenges in horticulture with electric robots that carry out labor-intensive fieldwork and increase worker productivity.” As part of the acquisition, Muddy Machines will rebrand Fox Robotics’ logistics robot platform to Squirrel. By diversifying our product portfolio, we can better serve the needs of growers. Squirrel complements our existing solutions by providing farmers with an efficient means to transport goods, supporting different parts of their workforce and operations. With this development, Muddy Machines is entering a pivotal stage of its growth and is currently raising funds to accelerate delivery of its products to market. We welcome inquiries from investors interested in supporting advancements in agricultural robotics. About Sprout and Squirrel Sprout, Muddy Machines’ existing robot, is a precision agriculture platform for specialty crops grown in open fields. Its initial tool, a selective green asparagus harvester, was successfully trialed earlier this year. With partnerships in place, Muddy Machines is developing additional tools for precision weeding, planting and spraying. This versatility unlocks true precision agriculture and the cultivation of high-value field vegetables. Squirrel expands Muddy Machines’ reach into the berry and fruit sector. Capable of operating both outdoors and in polytunnels, Squirrel addresses the unique challenges of berry and fruit farming. Initially serving as an autonomous carrier for heavy loads between workers, it increases worker productivity and addresses critical labor gaps in seasonal horticulture. “By combining the strengths of Sprout and Squirrel, we’re offering a comprehensive suite of electric robotic solutions that directly tackle the labor challenges faced by the horticulture industry,” added Chavasse. “Our robots are designed to assist farm workers by carrying out labor-intensive tasks, making their jobs easier and more efficient.” Addressing Economic Challenges Recent increases in the minimum wage and National Insurance contributions in the United Kingdom have placed additional financial pressures on growers, squeezing profit margins and making it more challenging to sustain traditional farming practices. By delivering advanced robotic solutions like Sprout and Squirrel, Muddy Machines aims to alleviate this pressure, offering growers efficient and cost-effective tools that support farm workers. “Our diversified product line will give farmers new tools to maintain productivity and profitability in the face of economic challenges,” said Chavasse. “We are committed to supporting the agricultural community with technology that solves their biggest challenge, the availability of labor.” The integration of these assets will accelerate the development of our existing projects and expand our offerings to serve customers better. With the addition of Squirrel, Muddy Machines is now better equipped than ever to lead the way in agricultural robotics, providing the electric robots necessary for the future of farming.
[学术文献 ] 采摘机器人全果园视觉感知及自主作业综述 进入全文
智慧农业(中英文)；Smart Agriculture；
【目的/意义】 采摘机器人是智慧农业的重要组成部分，其感知、规划、控制相关基础方法理论目前已有系统化研究。然而，构建具备全果园“感知-移动-采摘”一体化作业能力的实用型采摘系统仍面临诸多挑战。针对该问题，本文调研并报道了本领域近期案例，将全果园自主作业的关键技术划分为局部目标感知、全局地图构建和自主作业行为规划三个子问题并进行综述。 【进展】 首先回顾了近距离、局部范围内水果目标的精细视觉感知方法，包括基于低级特征融合、高级特征学习、RGB-D信息融合，以及多视角信息融合的4种方法；介绍与分析了全局尺度下的果园地图构建与大规模场景视觉感知案例；在感知的基础上，调研分析采摘机器人自主作业行为规划方法，包括底盘移动路径规划、机械臂视点规划与避障路径规划等方面的最新研究；最后对采摘机器人自主作业系统构建案例进行报道与分析。 【结论/展望】 感知、移动、采摘模块的高效协同是实现采摘机器人从基础功能样机进一步迈向实用型机器的关键，已有的视觉感知、规划与控制算法的鲁棒性与稳定性均需增强，协同程度需进一步提高。此外，提及了采摘机器人应用的几个开放性研究问题，并描述了其未来发展趋势。
[学术文献 ] 设施农业机器人导航关键技术研究进展与展望 进入全文
智慧农业(中英文)；Smart Agriculture；
【目的/意义】 随着科学技术的快速发展和劳动力成本的不断提高，机器人在设施农业领域的应用越来越广泛。设施环境复杂多样，如何让机器人实现稳定、精准、快速地导航仍然是当前需要解决的问题。 【进展】 本文基于设施农业智能机器人的自动导航关键技术展开综述。在自主定位与地图构建方面，详细介绍了信标定位、惯性定位、即时定位与建图技术，以及融合定位方法。其中，依据使用的传感器不同，即时定位与建图技术可进一步划分为视觉、激光和融合三种不同类型。在全局路径规划方面，探讨了点到点局部路径规划和全局遍历路径规划在设施农业中的应用。针对规划目标数量的不同，详细介绍了单目标路径规划和多目标路径规划。此外，在机器人的自动避障技术方面，讨论了一系列设施农业中常用的避障控制算法。 【结论/展望】 总结了当前设施农业智能机器人自动导航技术面临的挑战，包括复杂环境、遮挡严重、成本高、作业效率低、缺乏标准化平台和公开数据集等问题。未来研究应重点关注多传感器融合、先进算法优化、多机器人协同作业，以及数据标准化与共享平台的建设。这些方向将有助于提升机器人在设施农业中的导航精度、效率和适应性，为智能农业的发展提供参考和建议。
[学术文献 ] 农田土壤理化参数快速获取技术研究进展与展望 进入全文
智慧农业(中英文)；Smart Agriculture；
[目的/意义] 土壤是农业基本的生产资料，其质量与农业高效生产和可持续发展密切相关。由于以往对农田的高强度利用以及土壤侵蚀等原因，导致部分农田出现土壤有机质明显下降、地力减弱和生态功能退化等现象。土壤理化参数作为揭示土壤空间特征、评估土壤肥力的关键指标，对农田可持续利用起着至关重要的作用。因此，土壤理化参数信息的快速获取极为必要。［进展］探讨了农田土壤理化参数获取技术的研究意义，总结了当前用于农田土壤理化参数信息获取的主要技术，包括以电化学分析和光谱分析为主的实验室快速检验技术，以电磁感应、探地雷达、多光谱、高光谱和热红外为主的近地快速感知技术，以直接反演法、间接反演法和结合分析法为主的卫星遥感技术，以及近年的新型快速获取技术，如生物传感、环境磁学、太赫兹光谱和伽马能谱等，梳理了各方法的优缺点及适用情况。［结论/展望］结合农田环境的作业需求，依据未来研究的侧重方向提出发展建议，包括开发便携化、智能化和经济型的近地土壤信息获取系统及设备，实现土壤信息的原位快速检测。优化低空土壤信息获取平台的性能，完善数据的解译方法；联合多因素构建卫星遥感反演模型，利用多种共享开放的云计算平台实现数据的深度挖掘。深入探索多源数据融合在土壤理化参数信息获取中的研究与应用，构建泛化能力强、可靠性高的土壤信息感知算法和模型等，从而实现土壤理化参数信息快速、精准和智能化获取。
