Early detection and monitoring of plant diseases by Bioelectric Impedance Spectroscopy

Borges, Elisabeth; Matos, António Pires de; Cardoso, J. M. R.; Correia, C.M.B.A.; Vasconcelos, T.; Gomes, N.

doi:10.1109/enbeng.2012.6331377

Cited by 24 publications

(7 citation statements)

References 10 publications

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“…The International Standards Committee has formally accepted methods developed using spectroscopy [12]. The commonly used spectroscopy techniques in plant diseases are mainly focused on visible-near infrared (VIS-NIR), electric impedance, and fluorescence spectroscopy [12][13][14][15]. Thus, imaging-based and RS techniques have attracted considerable attention because they can provide accurate geographic information [10].…”

Section: Introductionmentioning

confidence: 99%

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

et al. 2020

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The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

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Section: Introductionmentioning

confidence: 99%

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

et al. 2020

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“…A parallel field of investigation for enhancing "robotic pathologists" may involve the further development of manipulators and sampling hardware, shifting the research focus from "experienced robotic observer" toward "robotic observer and analyst." In recent years, research of on-site detection assay is growing fast and methods such as Loop-Mediated Isothermal Amplification (LAMP) [90] or the lab-on-chip based on Electrical Impedance Spectroscopy (EIS) [91] may permit fast molecular diagnosis in the field. Because of their speed, robustness, and simplicity, these tests should be integrated in traditionally vision-based robots, enhancing their diagnostic specificity when required.…”

Section: Discussionmentioning

confidence: 99%

iPathology: Robotic Applications and Management of Plants and Plant Diseases

2017

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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.

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“…has also been studied. The method based on bioimpedance indexes allows determining three distinct physiological states: healthy and watered plants, plants with a high level of hydric stress, and plants with disease [94], [95], [96]. For example, Amaro et al [82] integrate an EIS system in a sensor node to analyze the health condition of the tree and transfer the results through the LoRaWAN protocol.…”

Section: Tree Monitoringmentioning

confidence: 99%

A Survey on LoRa for Smart Agriculture: Current Trends and Future Perspectives

Pagano

Croce

Tinnirello

et al. 2023

IEEE Internet Things J.

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This paper provides a survey on the adoption of LoRa in the agricultural field, and reviews state-of-the-art solutions for Smart Agriculture, analyzing the potential of this technology in different infield applications. In particular, we consider four reference scenarios, namely irrigation systems, plantation and crop monitoring, tree monitoring, and livestock monitoring, which exhibit heterogeneous requirements in terms of network bandwidth, density, sensors' complexity, and energy demand, as well as latency in the decision process. We discuss how LoRa-based solutions can work in these scenarios, analyzing their scalability, interoperability, network architecture, and energyefficiency. Finally, we present possible future research directions and point out some open issues which might become the main research trends for the next years.

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Early detection and monitoring of plant diseases by Bioelectric Impedance Spectroscopy

Cited by 24 publications

References 10 publications

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

iPathology: Robotic Applications and Management of Plants and Plant Diseases

A Survey on LoRa for Smart Agriculture: Current Trends and Future Perspectives

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