SporSenZ soil microbial screening for early crop disease detection

Overview

SporSenZ is a proprietary soil microbial screening tool developed by FungiAlert that identifies which microbes are active and dominant in agricultural soils. By detecting crop pathogens before visible disease symptoms emerge, SporSenZ enables growers and agronomists to take preventive action rather than reactive measures. The technology supports the development of disease forecast algorithms by combining soil microbial profiles with complementary agronomic data sources, such as Climate FieldView, along with pathogen genotyping and phenotyping. This integration helps untangle the complex abiotic and biotic interactions that drive crop disease manifestation.

Technical specifications

• Early pathogen detection: Identifies active fungal and oomycetal pathogens in soil before disease symptoms appear in crops.
• Microbial profiling: Maps the active and dominant microbial community in soils across a growing season.
• Broad applicability: Validated across specialty crops, row crops, hydroponic systems, and orchards in 25 countries.
• Data integration: Designed to combine SporSenZ microbial data with agronomic platforms and climate data for disease forecast modelling.
• Quality assured: Manufacturing, analysis, and reporting processes certified to ISO 9001:2015 standards.
• Scalable sampling protocol: Proposed deployment of 5 SporSenZ per hectare, sampled at two-week intervals across diseased-prone and healthy fields for detailed seasonal mapping.

Technology readiness level

SporSenZ has progressed beyond initial proof of concept. To date, over 1,319 SporSenZ units have been analysed from more than 60 end users across 25 countries, providing extensive real-world validation across diverse cropping systems. ISO 9001:2015 certification further confirms process quality for manufacturing, analysis, and result delivery. The next stage of development involves a large-scale collaborative field study through commercial digital-agriculture platforms to generate the dense, time-course datasets needed to build robust disease forecast algorithms, advancing the technology toward commercial forecasting applications.