Highlights  Detection and discrimination of plant stress origin using hyperspectral imaging.  Nematode infestation can be reliably differentiated from the water deficiency.  Abiotic drought resulted in the most obvious differences in the light spectrum.  Identification of nematode infestation possible with specific spectral regions.  Reliable prediction of nematode infestation even in early stages of infestation.

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Root-knot nematodes are considered the most important group of plant-parasitic nematodes due to their wide range of plant hosts and subsequent role in yield losses in agricultural production systems. Chemical nematicides are the primary control method, but ecotoxicity issues with some compounds has led to their phasing-out and consequential development of new control strategies, including biological control. We evaluated the nematicidal activity of Bacillus firmus I-1582 in pot and microplot experiments against Meloidogyne luci. I-1582 reduced nematode counts by 51% and 53% compared to the untreated control in pot and microplot experiments, respectively. I-1582 presence in the rhizosphere had concurrent nematicidal and plant growth-promoting effects, measured using plant morphology, relative chlorophyll content, elemental composition and hyperspectral imaging. Hyperspectral imaging in the 400–2500 nm spectral range and supervised classification using partial least squares support vector machines successfully differentiated B. firmus-treated and untreated plants, with 97.4% and 96.3% accuracy in pot and microplot experiments, respectively. Visible and shortwave infrared spectral regions associated with chlorophyll, N–H and C–N stretches in proteins were most relevant for treatment discrimination. This study shows the ability of hyperspectral imaging to rapidly assess the success of biological measures for pest control.

In 2005, an epidemic of the pike tapeworm Triaenophorus crassus Forel, 1868 broke out in the Arctic charr (Salvelinus umbla (L. 1758)) stock of Lake Grundlsee, Austria. Besides the definitive host Northern pike (Esox lucius L. 1758), which was introduced into the lake in the 1960s, the cestode requires copepods as first and salmonid fish as second intermediate hosts. Within 2 years, the prevalence of the cestode in medium sized Arctic charr increased to almost 100% and the abundance reached a maximum of 55 cysts per fish, leading to the closure of the fishery. Such a massive infection of Arctic charr has never been reported. High pike abundance and the occurrence of a suitable copepod host facilitated the outbreak. The only first intermediate host Cyclops abyssorum praealpinus Kiefer, 1933 predominated the zooplankton community during May, when cestode coracidia hatch from eggs. Only during this infectious period, C. abyssorum praealpinus was eaten by Arctic charr (2–50% of prey organisms). Low fishing pressure on pike enabled the development of a large population that served as reservoir for T. crassus with up to 687 cestodes per fish. To contain the epidemic, 1671 pike were removed between 2008 and 2013. Infection of Arctic charr decreased to 60% and a maximum number of 16 cysts in 2013.

Plant pests and disease detection using optical sensors Traditional agricultural plant pest and disease management practices are based on visible characteristics and require that plants are checked individually, making these practices time consuming and therefore costly. Plant pests and diseases also often exhibit a heterogeneous distribution, making detection more difficult. Remote sensing methods enable comparatively accurate detection of pests and diseases over larger areas. Furthermore, because remote sensing sensors utilize light outside the human visible spectrum, presymptomatic detection becomes possible, thus facilitating timely, appropriate and spatially accurate management practices. Because remote sensing systems generate large amount of data, novel data analysis methods, such as machine learning, were introduced to plant protection. While pest and disease detection is possible using individual sensors, best results can be obtained by combining different sensors, utilizing different spectral ranges or physiological responses to light. A large amount of data and information has been generated in the past, but this research has mostly been focused on individual pathogens. Future research will have to focus on combined infections or infestations, and include abiotic stressors as well.

Graphical abstract

Root-knot nematodes (Meloidogyne spp.) are considered the most aggressive, damaging, and economically important group of plant-parasitic nematodes and represent a significant limiting factor for potato (Solanum tuberosum) production and tuber quality. Meloidogyne luci has previously been shown to be a potato pest having significant reproductive potential on the potato. In this study we showed that M. luci may develop a latent infestation without visible symptoms on the tubers. This latent infestation may pose a high risk for uncontrolled spread of the pest, especially via seed potato. We developed efficient detection methods to prevent uncontrolled spread of M. luci via infested potato tubers. Using hyperspectral imaging and a molecular approach to detection of nematode DNA with real-time PCR, it was possible to detect M. luci in both heavily infested potato tubers and tubers without visible symptoms. Detection of infested tubers with hyperspectral imaging achieved a 100% success rate, regardless of tuber preparation. The real-time PCR approach detected M. luci with high sensitivity.