What is plant health diagnostic?

Healthy plants are essential for a dependable supply of seeds, plant growing material, and consequently for the sustainability of the food chain for humans and animals. Close surveillance of plant pests and diseased plants is therefore of most importance for the public well-being and the economy. Several players are involved in plant health and its protection. Plant health diagnostics consists in the detection, identification and characterization of plant pests (viruses, phytoplasma, bacteria, fungi, viroids and nematodes) and represents an essential step in the control of infectious diseases of cultivated plant varieties.


While Plant Protection Organizations worldwide have the tasks to coordinate strategies for prevention and control of plant pests’ introduction and spread, research institutes and private companies contribute to improve plant health by providing products and methods increasingly reliable and qualitative for plant health diagnostics.

The two main aspects of plant health diagnostic are:

Identification: analyzing which pest is causing symptoms by using suitable diagnostic procedures. In other words: what problem do I have?

Detection: searching, if a specific pest is present in a population of asymptomatic individuals by using a proven method/test. Or, in other words: do I have a problem?

Classic diagnostic techniques are based on observation and characterization of pathogens by visual examination (by eye or microscope) but also pathogens’ isolation and cultivation on artificial media.

Since the 1970s biotechnological methods have been established contributing to a faster and reliable diagnosis of plant pathogens. Two main approaches can be distinguished:

Immunological methods, based on the antibody / antigen interaction (e.g. ELISA test)

Molecular biology methods based on DNA/RNA amplification such as PCR, real-time PCR, microarray, etc)

Tests/methods suitable for plant health diagnostic need to be reliable at identifying or detecting a pathogen and therefore should be validated to a certain performance criteria such as analytical specificity, analytical sensitivity, reproducibility and repeatability.

There are also other criteria more related to the practical performance of the tests such as robustness and simplicity of implementation, possibility of processing a very large number of samples, possibility of automating the test steps, cost of tests, etc.