Bayesian Non-Parametric Thermal Thresholds for <i>Helicoverpa armigera</i> and Their Integration into a Digital Plant Protection System
The development of temperature-driven pest risk thresholds is a prerequisite for the buildup and implementation of smart plant protection solutions. However, the challenge is to convert short and abrupt phenology data with limited distributional information into ecological relevant information. In t...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-10-01
|
| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/12/10/2474 |
| _version_ | 1797476069550325760 |
|---|---|
| author | Petros Damos Fokion Papathanasiou Evaggelos Tsikos Thomas Kyriakidis Malamati Louta |
| author_facet | Petros Damos Fokion Papathanasiou Evaggelos Tsikos Thomas Kyriakidis Malamati Louta |
| author_sort | Petros Damos |
| collection | DOAJ |
| description | The development of temperature-driven pest risk thresholds is a prerequisite for the buildup and implementation of smart plant protection solutions. However, the challenge is to convert short and abrupt phenology data with limited distributional information into ecological relevant information. In this work, we present a novel approach to analyze phenology data based on non-parametric Bayesian methods and develop degree-day (DD) risk thresholds for <i>Helicoverpa armigera</i> (Hübner) (Lepidoptera: Noctuidae) to be used in a decision support system for dry bean (<i>Phaseolus vulgaris</i> L.) production. The replication of each Bayesian bootstrap generates a posterior probability for each sampling set by considering that the prior unknown distribution of pest phenology is Dirichlet distribution. We computed R = 10,000 temperature-driven pest phenology replicates, to estimate the 2.5%, 50% and 95.5% percentiles (PC) of each flight generation peak in terms of heat summations. The related DD thresholds were: 114.04 (PC 2.5%) 131.8 (PC 50%) and 150.9 (PC 95.5%) for the first, 525.8 (PC 2.5%), 551.7 (PC 50%) and 577.6 (PC 95.5%) for the second and 992.7 (PC 2.5%), 1021.5 (PC 50%) and 1050 (PC 95.5%) for the third flight, respectively. The thresholds were evaluated by estimating the posterior differences between the predicted (2021) and observed (2022) phenology metrics and are in most cases in acceptable levels. The bootstrapped Bayesian risk thresholds have the advantage to be used in modeling short and noisy data sets providing tailored pest forecast without any parametric assumptions. In a second step the above thresholds were integrated to a sub-module of a digital weather-driven real time decision support system for precise pest management for dry bean crops. The system consists of a customized cloud based telemetric meteorological network, established over the border area of the Prespa National Park in Northern Greece, and delivers real time data and pest specific forecast to the end user. |
| first_indexed | 2024-03-09T20:53:43Z |
| format | Article |
| id | doaj.art-9638cf7eb5784a11b132f2bf8f41812b |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T20:53:43Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-9638cf7eb5784a11b132f2bf8f41812b2023-11-23T22:27:50ZengMDPI AGAgronomy2073-43952022-10-011210247410.3390/agronomy12102474Bayesian Non-Parametric Thermal Thresholds for <i>Helicoverpa armigera</i> and Their Integration into a Digital Plant Protection SystemPetros Damos0Fokion Papathanasiou1Evaggelos Tsikos2Thomas Kyriakidis3Malamati Louta4Department of Agriculture, School of Agricultural Sciences, University of Western Macedonia, 53100 Florina, GreeceDepartment of Agriculture, School of Agricultural Sciences, University of Western Macedonia, 53100 Florina, GreeceDepartment of Agriculture, School of Agricultural Sciences, University of Western Macedonia, 53100 Florina, GreeceDepartment of Electrical and Computer Engineering, Telecommunication Networks and Advanced Services Laboratory, University of Western Macedonia, 50103 Kozani, GreeceDepartment of Electrical and Computer Engineering, Telecommunication Networks and Advanced Services Laboratory, University of Western Macedonia, 50103 Kozani, GreeceThe development of temperature-driven pest risk thresholds is a prerequisite for the buildup and implementation of smart plant protection solutions. However, the challenge is to convert short and abrupt phenology data with limited distributional information into ecological relevant information. In this work, we present a novel approach to analyze phenology data based on non-parametric Bayesian methods and develop degree-day (DD) risk thresholds for <i>Helicoverpa armigera</i> (Hübner) (Lepidoptera: Noctuidae) to be used in a decision support system for dry bean (<i>Phaseolus vulgaris</i> L.) production. The replication of each Bayesian bootstrap generates a posterior probability for each sampling set by considering that the prior unknown distribution of pest phenology is Dirichlet distribution. We computed R = 10,000 temperature-driven pest phenology replicates, to estimate the 2.5%, 50% and 95.5% percentiles (PC) of each flight generation peak in terms of heat summations. The related DD thresholds were: 114.04 (PC 2.5%) 131.8 (PC 50%) and 150.9 (PC 95.5%) for the first, 525.8 (PC 2.5%), 551.7 (PC 50%) and 577.6 (PC 95.5%) for the second and 992.7 (PC 2.5%), 1021.5 (PC 50%) and 1050 (PC 95.5%) for the third flight, respectively. The thresholds were evaluated by estimating the posterior differences between the predicted (2021) and observed (2022) phenology metrics and are in most cases in acceptable levels. The bootstrapped Bayesian risk thresholds have the advantage to be used in modeling short and noisy data sets providing tailored pest forecast without any parametric assumptions. In a second step the above thresholds were integrated to a sub-module of a digital weather-driven real time decision support system for precise pest management for dry bean crops. The system consists of a customized cloud based telemetric meteorological network, established over the border area of the Prespa National Park in Northern Greece, and delivers real time data and pest specific forecast to the end user.https://www.mdpi.com/2073-4395/12/10/2474cotton bollwormpest managementprecise plant protectionsimulation and forecastdecision support system |
| spellingShingle | Petros Damos Fokion Papathanasiou Evaggelos Tsikos Thomas Kyriakidis Malamati Louta Bayesian Non-Parametric Thermal Thresholds for <i>Helicoverpa armigera</i> and Their Integration into a Digital Plant Protection System Agronomy cotton bollworm pest management precise plant protection simulation and forecast decision support system |
| title | Bayesian Non-Parametric Thermal Thresholds for <i>Helicoverpa armigera</i> and Their Integration into a Digital Plant Protection System |
| title_full | Bayesian Non-Parametric Thermal Thresholds for <i>Helicoverpa armigera</i> and Their Integration into a Digital Plant Protection System |
| title_fullStr | Bayesian Non-Parametric Thermal Thresholds for <i>Helicoverpa armigera</i> and Their Integration into a Digital Plant Protection System |
| title_full_unstemmed | Bayesian Non-Parametric Thermal Thresholds for <i>Helicoverpa armigera</i> and Their Integration into a Digital Plant Protection System |
| title_short | Bayesian Non-Parametric Thermal Thresholds for <i>Helicoverpa armigera</i> and Their Integration into a Digital Plant Protection System |
| title_sort | bayesian non parametric thermal thresholds for i helicoverpa armigera i and their integration into a digital plant protection system |
| topic | cotton bollworm pest management precise plant protection simulation and forecast decision support system |
| url | https://www.mdpi.com/2073-4395/12/10/2474 |
| work_keys_str_mv | AT petrosdamos bayesiannonparametricthermalthresholdsforihelicoverpaarmigeraiandtheirintegrationintoadigitalplantprotectionsystem AT fokionpapathanasiou bayesiannonparametricthermalthresholdsforihelicoverpaarmigeraiandtheirintegrationintoadigitalplantprotectionsystem AT evaggelostsikos bayesiannonparametricthermalthresholdsforihelicoverpaarmigeraiandtheirintegrationintoadigitalplantprotectionsystem AT thomaskyriakidis bayesiannonparametricthermalthresholdsforihelicoverpaarmigeraiandtheirintegrationintoadigitalplantprotectionsystem AT malamatilouta bayesiannonparametricthermalthresholdsforihelicoverpaarmigeraiandtheirintegrationintoadigitalplantprotectionsystem |