Outlook of Various Soft Computing Data Preprocessing Techniques to Study the Pest Population Dynamics in Integrated Pest Management

Pratheepa, M.; Antony, J. Cruz

doi:10.1007/978-981-10-7455-4_11

Cited by 1 publication

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Local, regional and global performance of models and effects on resource use of research: how to direct efforts and resources, also in the context of current IPM programs and monitoring and forecasting as one of its principles (Berlin et al, 2018). • Big Data and IPM: Data types and collection (Zaza et al, 2018); Data processing (Pratheepa and Antony, 2018); Data valuation (Demirel and Kumral, 2021;Weersink et al, 2018)…”

Section: Further Readingmentioning

confidence: 99%

Advances in insect pest and disease monitoring and forecasting in horticulture

Vänninen¹

2022

Burleigh Dodds Series in Agricultural Science

View full text Add to dashboard Cite

Pest monitoring in horticulture is developing technologically to reduce time and labor needed for sampling and to produce more accurate pest predictions. New ways of detecting pests based on selective e-traps, e-noses, cameras and acoustic signatures are already in use or emerging. Remote sensing of pests requires the development of new economic injury levels and economic thresholds (ET). The relative importance of mechanistic and statistical models is changing due to AI-technologies and Big Data. The use of Big Data will force researchers to collect, use and value data differently than before. The incorporation of natural enemies in ETs will take place gradually and require researchers to acquire modeling skills. Research for advancing monitoring and forecasting also must include the socioeconomic factors that determine whether new technologies will be implemented by farmers. Developing trustworthy sampling plans and forecasting models,, and validating and implementing them in collaboration with stakeholders, remains important.

show abstract

Section: Further Readingmentioning

confidence: 99%

Advances in insect pest and disease monitoring and forecasting in horticulture

Vänninen¹

2022

Burleigh Dodds Series in Agricultural Science

View full text Add to dashboard Cite

show abstract

Outlook of Various Soft Computing Data Preprocessing Techniques to Study the Pest Population Dynamics in Integrated Pest Management

Cited by 1 publication

References 14 publications

Advances in insect pest and disease monitoring and forecasting in horticulture

Advances in insect pest and disease monitoring and forecasting in horticulture

Contact Info

Product

Resources

About