X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

Cruz, Albert C.; Luvisi, Andrea; Bellis, Luigi De; Ampatzidis, Yiannis

doi:10.3389/fpls.2017.01741

Cited by 144 publications

(78 citation statements)

References 44 publications

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“…Detecting symptoms of diseases is a large potential provided by deep learning. For example, CNNs already help detect plant diseases in olive trees 41 , cassavas (Manihot esculenta) 42 or various crops 43 .…”

Section: Population Monitoringmentioning

confidence: 99%

Applications for deep learning in ecology

Christin

Hervet

Lecomte

2018

Preprint

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A lot of hype has recently been generated around deep learning, a novel group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning has revolutionized several research fields such as bioinformatics and medicine with its flexibility and ability to process large and complex datasets. As ecological datasets are becoming larger and more complex, we believe these methods can be useful to ecologists as well. In this paper, we review existing implementations and show that deep learning has been used successfully to identify species, classify animal behaviour and estimate biodiversity in large datasets like camera‐trap images, audio recordings and videos. We demonstrate that deep learning can be beneficial to most ecological disciplines, including applied contexts, such as management and conservation. We also identify common questions about how and when to use deep learning, such as what are the steps required to create a deep learning network, which tools are available to help, and what are the requirements in terms of data and computer power. We provide guidelines, recommendations and useful resources, including a reference flowchart to help ecologists get started with deep learning. We argue that at a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be effectively processed by humans anymore, deep learning could become a powerful reference tool for ecologists.

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Section: Population Monitoringmentioning

confidence: 99%

Applications for deep learning in ecology

Christin

Hervet

Lecomte

2018

Preprint

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“…Development of computer image analysis programs based on machine learning may facilitate symptom screening in the field (Cruz et al . ), although this approach still relies on inspecting individual plants. Aerial surveillance methods are also being developed to identify symptomatic plants across large areas (Zarco‐Tejada et al .…”

Section: Assessing Vector Abundance and Diversitymentioning

confidence: 99%

Sharpshooters: a review of what moves Xylella fastidiosa

et al. 2019

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Sharpshooters (Hemiptera: Cicadellidae: Cicadellinae) and spittlebugs (Hemiptera: Aphrophoridae: Aphrophorinae) have a worldwide distribution and are often associated with many crops. Because the geographic range of sharpshooters and spittlebugs often overlaps with the range of the plant pathogenic bacterium Xylella fastidiosa, the importance of these groups of insects in spreading X. fastidiosa rises to the level of a major socioeconomic problem. Managing diseases caused by X. fastidiosa is challenging for several reasons. First, multiple sequence types of the pathogen have been identified, with sequence type affecting host plant specificity. Second, multiple vector species may be present in an agroecosystem, and the host range of vector species is often broad. Although multiple vector control options such as biological, chemical, physical and cultural controls have been described, disease control programs must take into account biological and ecological parameters of locally dominant X. fastidiosa sequence types and vector species to develop suitable control strategies. Our review demonstrates how fundamental principles of vector reproductive biology, behaviour, nutrition and population and community ecology have influenced research on insect vectors and provides insights into priority research areas.

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“…This modification of preprogrammed system parameters makes the devices "smart". The proposed system is an example of IoT in agriculture [32,33]. With the VC system, the user can easily access data from the WSN and control the SC system, remotely from his office/house (indoor application).…”

Section: Voice-controlled (Vc) Systemmentioning

confidence: 99%

Voice-Controlled and Wireless Solid Set Canopy Delivery (VCW-SSCD) System for Mist-Cooling

et al. 2018

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California growers in the San Joaquin Valley believe that climate change will affect the pistachio yield dramatically. As the central valley fog disappears, insufficient dormant chill accumulation results in poor flowering synchrony, flower quality, and fruit set in this dioecious species. We have developed a novel, user-friendly, and low-cost Voice-Controlled Wireless Solid Set Canopy Delivery (VCW-SSCD) system to increase bud chill accumulation with evaporative cooling on sunny (winter) days. This system includes: (i) an automated solid-state canopy delivery (SSCD) system; (ii) a wireless weather-, crop-related data acquisition system; (iii) a Voice-Controlled (VC) system using Amazon Alexa; (iv) a mobile application to visualize the collected data and wirelessly control the SSCD system; and (v) a smart control system. The proposed system was deployed and evaluated in a commercial pistachio orchard in Bakersfield, CA. The system worked well with no reported errors. Results demonstrated the system's ability to cool bud temperatures in a low relative humidity climate. At an ambient temperature of 10-20 • C, bud temperatures were lowered 5-10 • C.

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X-FIDO: An Effective Application for Detecting Olive Quick Decline Syndrome with Deep Learning and Data Fusion

Cited by 144 publications

References 44 publications

Applications for deep learning in ecology

Applications for deep learning in ecology

Sharpshooters: a review of what moves Xylella fastidiosa

Voice-Controlled and Wireless Solid Set Canopy Delivery (VCW-SSCD) System for Mist-Cooling

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