从检测到保护:光学传感器、机器人和人工智能在现代植物病害管理中的作用。

IF 2.6 2区 农林科学 Q2 PLANT SCIENCES Phytopathology Pub Date : 2024-08-01 DOI:10.1094/PHYTO-01-24-0009-PER
Anne-Katrin Mahlein, Jayme G Arnal Barbedo, Kuo-Szu Chiang, Emerson M Del Ponte, Clive H Bock
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引用次数: 0

摘要

在过去十年中,人们认识到需要采用创新方法来监测和管理植物病害,以满足现代农业的精准需求。在过去的 15 年中,植物病害的检测、监测和管理取得了重大进展,这主要得益于尖端技术的推动。传感器、人工智能、微传感器网络和自动驾驶汽车等先进工具推动了精准农业的最新进展。这些技术推动了新型耕作系统的发展,使作物的管理具有针对性,与传统的大面积作物同质化管理形成鲜明对比。该领域的研究通常是高度协作和跨学科的工作。它汇集了来自植物病理学、计算机科学、统计学、工程学和农学等不同领域的专家。尽管取得了进展,但要将决策或自动化的精确性进步转化为农业实践仍是一项挑战。提高病害检测的准确性和及时性仍是当务之急,而数据驱动的人工智能系统将发挥关键作用。本视角探讨了在实施植物病害管理数字技术过程中面临的关键问题和挑战。它强调了将创新技术进步与传统病虫害综合防治(IPM)相结合的紧迫性。它强调了在为特定地点的处理确定控制阈值以及将数字技术的使用与监管框架进行必要的协调方面尚未解决的问题。重要的是,论文呼吁加强研究工作、广泛传播知识和开展教育,以优化植物病害管理数字工具的应用。
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From Detection to Protection: The Role of Optical Sensors, Robots, and Artificial Intelligence in Modern Plant Disease Management.

In the past decade, there has been a recognized need for innovative methods to monitor and manage plant diseases, aiming to meet the precision demands of modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largely propelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificial intelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowing for targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highly collaborative and interdisciplinary endeavor. It brings together experts from diverse fields such as plant pathology, computer science, statistics, engineering, and agronomy to forge comprehensive solutions. Despite the progress, translating the advancements in the precision of decision-making or automation into agricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing the accuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. This perspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscores the urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding the establishment of control thresholds for site-specific treatments and the necessary alignment of digital technology use with regulatory frameworks. Importantly, the paper calls for intensified research efforts, widespread knowledge dissemination, and education to optimize the application of digital tools for plant disease management, recognizing the intersection of technology's potential with its current practical limitations.

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来源期刊
Phytopathology
Phytopathology 生物-植物科学
CiteScore
5.90
自引率
9.40%
发文量
505
审稿时长
4-8 weeks
期刊介绍: Phytopathology publishes articles on fundamental research that advances understanding of the nature of plant diseases, the agents that cause them, their spread, the losses they cause, and measures that can be used to control them. Phytopathology considers manuscripts covering all aspects of plant diseases including bacteriology, host-parasite biochemistry and cell biology, biological control, disease control and pest management, description of new pathogen species description of new pathogen species, ecology and population biology, epidemiology, disease etiology, host genetics and resistance, mycology, nematology, plant stress and abiotic disorders, postharvest pathology and mycotoxins, and virology. Papers dealing mainly with taxonomy, such as descriptions of new plant pathogen taxa are acceptable if they include plant disease research results such as pathogenicity, host range, etc. Taxonomic papers that focus on classification, identification, and nomenclature below the subspecies level may also be submitted to Phytopathology.
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