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.

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.

Hyperspectral imaging is a popular tool used for non-invasive plant disease detection. Data acquired with it usually consist of many correlated features; hence most of the acquired information is redundant. Dimensionality reduction methods are used to transform the data sets from high-dimensional, to low-dimensional (in this study to one or a few features). We have chosen six dimensionality reduction methods (partial least squares, linear discriminant analysis, principal component analysis, RandomForest, ReliefF, and Extreme gradient boosting) and tested their efficacy on a hyperspectral data set of potato tubers. The extracted or selected features were pipelined to support vector machine classifier and evaluated. Tubers were divided into two groups, healthy and infested with Meloidogyne luci. The results show that all dimensionality reduction methods enabled successful identification of inoculated tubers. The best and most consistent results were obtained using linear discriminant analysis, with 100% accuracy in both potato tuber inside and outside images. Classification success was generally higher in the outside data set, than in the inside. Nevertheless, accuracy was in all cases above 0.6.

The objective of our research was to test hyperspectral imaging as a method for early detection and discrimination of biotic and abiotic stress in maize. We investigated the individual and combined effects of wireworm feeding and drought stress on leaf spectral responses and on various morphological and physiological traits of maize plants, selecting two hybrids with different tolerance to drought. Physiological parameters were determined at three time points (14, 21 and 28 days after adding wireworms and changing watering regime), along with hyperspectral imaging. Most of the differences in physiological characteristics between treatments were detected on day 21, when drought was the main cause of the negative physiological outcome, while the presence of wireworms only caused lower relative chlorophyll content, resulting in lower combined stress damage in some treatments. The morphological data showed greater wireworm damage to hybrid ZP341 and a greater negative effect of combined stress on hybrid FuturiXX. Hyperspectral imaging detected pest infestation and drought stress before they were detected by classical methods, with the highest overall accuracy on day 14 (84.7%) and the lowest on day 28 (67%). It can therefore be used as a method for early detection of wireworm infestation and/or drought in maize.

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. Key words: Remote sensing, plant protection, hyperspectral, multispectral, thermal, fluorescence, precision agriculture IZVLEČEKVelikokrat tradicionalni pristopi varstva rastlin pred rastlinskimi boleznimi in škodljivci temeljijo na vidnih simptomih, ki vključuje redno pregledovanje posameznih rastlin. Postopki so zato lahko dolgotrajni in s tem dragi. Bolezni in škodljivci imajo v prostoru pogosto heterogeno razporeditev, kar otežuje njihovo odkrivanje. Metode daljinskega zaznavanja omogočajo razmeroma natančno odkrivanje škodljivcev in bolezni na večjih območjih. Ker uporabljajo senzorji daljinskega zaznavanja tudi svetlobo izven nam vidnega spektra, je možno tudi zgodnje odkrivanje, t.j. odkrivanje pred razvojem vidnih znakov bolezni. To omogoča pravočasno, ustrezno in prostorsko natančno upravljanje z boleznimi in škodljivci. Sistemi daljinskega zaznavanja ustvarjajo velike količine podatkov, zato so bile v varstvo rastlin uvedene sodobne metode za analizo podatkov, na primer strojno učenje. Čeprav je možno zaznava bolezni in škodljivcev z uporabo posameznih senzorjev, lahko dosežemo najboljše rezultate z združevanjem različnih senzorjev, torej z uporabo različnih spektralnih območij ali fizioloških odzivov na svetlobo. Dosedanje raziskave so bile osredotočene na posamezne škodljivce in bolezni. Prihodnje raziskave se bodo morale osredotočiti na kombinirane okužbe ter vključevati tudi abiotske stresorje. Ključne besede: daljinsko zaznavanje, varstvo rastlin, hiperspekter, multispekter, toplotno slikanje, fluorescenca, precizno kmetijstvo

The western corn rootworm (WCR, Diabrotica virgifera virgifera LeConte) is one of the most important maize pests, and was introduced to Slovenia in 2003 by natural spread from both the East and West. In Slovenia, as in other European countries, different patterns of spread and population growth of the WCR have been observed in maize growing areas, depending on the density of maize fields in the area and the frequency of maize in the crop rotation. In this study, the spread, population growth and impact on agriculture practice of the WCR have been analysed using GIS tools. In the period from 2003 to 2007, the spread and population growth were observed to be different between western and eastern Slovenia due to differences in population pressure from neighbouring areas and the portion of maize in the area. In the West, the distribution and prevalence of WCR has remained approximately unchanged since the first year of introduction, while in the East, both spread and population growth have become more progressive. However, both parameters showed only moderate progress up to 2006, while in 2007, conditions became more favourable for WCR development and spread. Spread was influenced significantly, although there were only 45 captured imagos per positive PAL pheromone trap per year after 4 years of presence, on average. Moderate growth of the WCR population was a consequence of the implementation of area-wide containment measures in a delimited area, which resulted in a considerable decrease of monoculture maize growth. This altered pattern of agricultural practice was confirmed with GIS analysis in areas that were demarcated in 2004 and 2005. In the maize growing area in the East, the portion of area under monocultural maize cultivation decreased from 28% to 5% in demarcated areas of WCR from 2004 and 2005, and decreased from 18% to 2% in 2007. In the area demarcated in 2004 in the West, the portion of monocultural maize is decreased in 2007 to only 3%, compared with 34% in 2003. Obviously Slovenian farmers have followed the legislation to a great extent and adopted crop rotation as the main WCR control measure. Implementation of area-wide containment measures from the first year of WCR infestation resulted in slower population growth and expansion