Comparison of Selected Dimensionality Reduction Methods for Detection of Root-Knot Nematode Infestations in Potato Tubers Using Hyperspectral Imaging.

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.

Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications.

Hyperspectral imaging has emerged as a pivotal technology in agricultural research, offering a powerful means to non-invasively monitor stress factors, such as drought, in crops like potato plants. In this context, the integration of attention-based deep learning models presents a promising avenue for enhancing the efficiency of stress detection, by enabling the identification of meaningful spectral channels. This study assesses the performance of deep learning models on two potato plant cultivars exposed to water-deficient conditions. It explores how various sampling strategies and biases impact the classification metrics by using a dual-sensor hyperspectral imaging systems (VNIR -Visible and Near-Infrared and SWIR-Short-Wave Infrared). Moreover, it focuses on pinpointing crucial wavelengths within the concatenated images indicative of water-deficient conditions. The proposed deep learning model yields encouraging results. In the context of binary classification, it achieved an area under the receiver operating characteristic curve (AUC-ROC-Area Under the Receiver Operating Characteristic Curve) of 0.74 (95% CI: 0.70, 0.78) and 0.64 (95% CI: 0.56, 0.69) for the KIS Krka and KIS Savinja varieties, respectively. Moreover, the corresponding F1 scores were 0.67 (95% CI: 0.64, 0.71) and 0.63 (95% CI: 0.56, 0.68). An evaluation of the performance of the datasets with deliberately introduced biases consistently demonstrated superior results in comparison to their non-biased equivalents. Notably, the ROC-AUC values exhibited significant improvements, registering a maximum increase of 10.8% for KIS Krka and 18.9% for KIS Savinja. The wavelengths of greatest significance were observed in the ranges of 475-580 nm, 660-730 nm, 940-970 nm, 1420-1510 nm, 1875-2040 nm, and 2350-2480 nm. These findings suggest that discerning between the two treatments is attainable, despite the absence of prominently manifested symptoms of drought stress in either cultivar through visual observation. The research outcomes carry significant implications for both precision agriculture and potato breeding. In precision agriculture, precise water monitoring enhances resource allocation, irrigation, yield, and loss prevention. Hyperspectral imaging holds potential to expedite drought-tolerant cultivar selection, thereby streamlining breeding for resilient potatoes adaptable to shifting climates.

Differential diagnosis of Triaenophorus crassus and T. nodulosus experimental infection in Cyclops abyssorum praealpinus (Copepoda) from the Alpine Lake Grundlsee (Austria) using PCR-RFLP.

In 2005, an epidemic of the cestode Triaenophorus crassus in Arctic charr, Salvelinus umbla from Lake Grundlsee (Austria) was reported, resulting in the closure of the local fisheries. The parasite uses a copepod and a salmonid fish as intermediate hosts, and the European pike (Esox lucius) as definitive host. In Lake Grundlsee procercoids of T. crassus, T. nodulosus and Eubothrium salvelini use the copepod Cyclops abyssorum praealpinus as their first intermediate host. We report on the development of a PCR-RFLP assay for the specific differentiation between the procercoids of the three cestode species. Ban I restriction sites within a 207-bp 18S rRNA-amplified fragment yielded two and three species-specific products to unequivocally distinguish between T. crassus and T. nodulosus, respectively. The assay is sufficiently sensitive to characterise a single procercoid of both Triaenophorus species in their copepod host, enabling a direct measure of infection. This assay will be useful in monitoring the progress of the control methods currently in place.

A new method for early assessment of effects of exposing two non-target crustacean species, Asellus aquaticus and Gammarus fossarum, to pesticides, a laboratory study.

A reliable method is needed for assessing the condition of aquatic animals and their resistance to toxic pollutants. The physiological responses of two freshwater crustaceans, Asellus aquaticus and Gammarus fossarum, following in vitro exposure to two pesticides (atrazine and imidacloprid), were measured by a combination of electron transport system (ETS) activity and respiration (R). Short-term exposure concentrations were selected according to standard toxicity tests and ranged from 0.01 mg L(-1) to 10 mg L(-1). When pesticide concentration was greater than 1 mg l(- 1) (which is below the LC(50) [48 hours] determined for both species), A. aquaticus and G. fossarum responded to short-term exposure with elevated levels of R and/or lower levels of ETS activity. One hour exposure to concentrations of up to 10 mg L(-1) showed an effect in both test species. Laboratory tests confirmed that G. fossarum is more sensitive to short-term pesticide exposure than A. aquaticus. The combination of these two methods provides a useful and effective tool for assessing the general condition of aquatic animals. It also enables to determine toxic effects on freshwater biota of specific or combined pollutants. ETS/R ratio may be used as a quick predictor of effects on organisms exposed to pesticides and other stress factors such as changes in temperature, light, salinity, oxygen concentration and food.

Effects of exposing two non-target crustacean species, Asellus aquaticus L., and Gammarus fossarum Koch., to atrazine and imidacloprid.

The physiological responses of two freshwater crustaceans, Asellus aquaticus L. and Gammarus fossarum Koch., following in vitro exposure to two pesticides were measured. Both species responded to short-term exposure with elevated levels of Respiration and/or lower levels of Electron Transport System (ETS) activity. 1 h exposure to concentrations of up to 10 mg L(-1) showed an effect in both test species. Laboratory tests confirmed that G. fossarum is more sensitive to short-term pesticide exposure than A. aquaticus. ETS/R ratio may be used as a quick predictor of effects on organisms exposed to pesticides.

From Genome to Field-Observation of the Multimodal Nematicidal and Plant Growth-Promoting Effects of Bacillus firmus I-1582 on Tomatoes Using Hyperspectral Remote Sensing.

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.

Pipeline for imaging, extraction, pre-processing, and processing of time-series hyperspectral data for discriminating drought stress origin in tomatoes.

Crop infestation with root-knot nematodes (RKN) and water deficiency lead to similar visible symptoms in the plant canopy. Identification of biotic or abiotic stress origin is therefore a problem, and currently the only reliable methods for determination of RKN infestation are invasive and applicable only for point-searches. In this study the applicability of hyperspectral remote sensing for early identification of drought stress and RKN infestations in tomato plants was tested. A four-stage image and data management pipeline was established: (1) image acquisition, (2) data extraction, (3) pre-processing, and (4) processing. •This pipeline reduces atmospheric impacts, facilitates data extraction (by using specially designed spectral libraries and supervised classification procedures), diminishes the impact of viewing geometry, and emphasized small spectral variations not apparent in the raw data.•By combining partial least squares - discriminant analysis and support vector machines with time series analysis, we achieved up to 100% classification success when determining watering regime and infestation, and their severity.•This pipeline could be at least partially automated, thus facilitating high throughput identification of stress origin in plants. Furthermore, the same pipeline could be applied to hyperspectral phenotyping procedures, which are gaining importance in breeding programs.