iPathology: Robotic Applications and Management of Plants and Plant Diseases

Yiannis Ampatzidis, Luigi De Bellis and Andrea Luvisi
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Yiannis Ampatzidis: Department of Agricultural and Biological Engineering, University of Florida, Southwest Florida Research and Education Center, 2685 FL-29, Immokalee, FL 34142, USA
Luigi De Bellis: Department of Biological and Environmental Sciences and Technologies, University of Salento, via Prov.le Monteroni, 73100 Lecce, Italy
Andrea Luvisi: Department of Biological and Environmental Sciences and Technologies, University of Salento, via Prov.le Monteroni, 73100 Lecce, Italy

Sustainability, 2017, vol. 9, issue 6, 1-14

Abstract: The rapid development of new technologies and the changing landscape of the online world (e.g., Internet of Things (IoT), Internet of All, cloud-based solutions) provide a unique opportunity for developing automated and robotic systems for urban farming, agriculture, and forestry. Technological advances in machine vision, global positioning systems, laser technologies, actuators, and mechatronics have enabled the development and implementation of robotic systems and intelligent technologies for precision agriculture. Herein, we present and review robotic applications on plant pathology and management, and emerging agricultural technologies for intra-urban agriculture. Greenhouse advanced management systems and technologies have been greatly developed in the last years, integrating IoT and WSN (Wireless Sensor Network). Machine learning, machine vision, and AI (Artificial Intelligence) have been utilized and applied in agriculture for automated and robotic farming. Intelligence technologies, using machine vision/learning, have been developed not only for planting, irrigation, weeding (to some extent), pruning, and harvesting, but also for plant disease detection and identification. However, plant disease detection still represents an intriguing challenge, for both abiotic and biotic stress. Many recognition methods and technologies for identifying plant disease symptoms have been successfully developed; still, the majority of them require a controlled environment for data acquisition to avoid false positives. Machine learning methods (e.g., deep and transfer learning) present promising results for improving image processing and plant symptom identification. Nevertheless, diagnostic specificity is a challenge for microorganism control and should drive the development of mechatronics and robotic solutions for disease management.

Keywords: machine vision; machine learning; vertical farming systems; mechatronics; smart machines; smart city (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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