Temporal dynamics of sapota pest damage and Phytophthora disease: insights from time series and machine learning models
The integration of statistical and machine learning models provides robust insights into sapota pest and disease epidemiology under climatic variability. These findings highlight the importance of temperature, humidity, and rainfall in shaping pest-pathogen interactions and provide predictive tools to design timely, targeted, and climate-resilient management strategies for sapota cultivation.
Authors
- Meenakshi Malik (ORCID: https://orcid.org/0000-0001-9118-8819)
- Amoghavarsha Chittaragi (ORCID: https://orcid.org/0000-0002-5941-9661)
- Devaramane Raghavendra (ORCID: https://orcid.org/0000-0002-2571-024X)
- Balanagouda Patil (ORCID: https://orcid.org/0000-0003-0026-0956)
- Niranjan Singh
- Bachu Lakshmi Manisha
Institutions
- Indian Agricultural Statistics Research Institute (IN)
- Indian Council of Agricultural Research (IN)
- University of Agricultural Sciences, Bangalore (IN)
- National Research Centre for Integrated Pest Management (IN)
- University of Agricultural and Horticultural Sciences (IN)
Publication Details
- Journal
- Frontiers in Plant Science
- Published
- 2025-09-23
- DOI
- https://doi.org/10.3389/fpls.2025.1659709
- Citations
- 2
- Primary Topic
- Plant Pathogens and Resistance
- Type
- article
- Field-Weighted Citation Impact
- 3.50