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 demandsof modern agriculture. Over the last 15 years, significant advances in the detection, monitoring, and management of plant diseases have been made, largelypropelled by cutting-edge technologies. Recent advances in precision agriculture have been driven by sophisticated tools such as optical sensors, artificialintelligence, microsensor networks, and autonomous driving vehicles. These technologies have enabled the development of novel cropping systems, allowingfor targeted management of crops, contrasting with the traditional, homogeneous treatment of large crop areas. The research in this field is usually a highlycollaborative 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 intoagricultural practice remains a challenge. The knowledge transfer to agricultural practice and extension has been particularly challenging. Enhancing theaccuracy and timeliness of disease detection continues to be a priority, with data-driven artificial intelligence systems poised to play a pivotal role. Thisperspective article addresses critical questions and challenges faced in the implementation of digital technologies for plant disease management. It underscoresthe urgency of integrating innovative technological advances with traditional integrated pest management. It highlights unresolved issues regarding theestablishment 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 diseasemanagement, recognizing the intersection of technology’s potential with its current practical limitations.

Bibliographic Details

Main Authors:	MAHLEIN, A. K., BARBEDO, J. G. A., CHIANG, K. S., DEL PONTE, E. M., BOCK, C. H.
Other Authors:	ANNE-KATRIN MAHLEIN, INSTITUTE OF SUGAR BEET RESEARCH; JAYME GARCIA ARNAL BARBEDO, CNPTIA; KUO-SZU CHIANG, NATIONAL CHUNG HSING UNIVERSITY; EMERSON M. DEL PONTE, UNIVERSIDADE FEDERAL DE VIÇOSA; CLIVE H. BOCK, UNITED STATES DEPARTMENT OF AGRICULTURE.
Format:	Artigo de periódico biblioteca
Language:	eng
Published:	2024-09-03
Subjects:	Precisão, Inteligência artificial, Sensores óticos, Detecção de doenças de plantas, Robótica, Optical sensors, Plant disease detection, Robotics, Accuracy, Artificial intelligence, Plant diseases and disorders,
Online Access:	http://www.alice.cnptia.embrapa.br/alice/handle/doc/1167072 https://doi.org/10.1094/PHYTO-01-24-0009-PER
Tags:	Add Tag No Tags, Be the first to tag this record!