Detection of leek rust disease under field conditions using hyperspectral proximal sensing and machine learning

Rust disease is an important problem for leek cultivation worldwide. It reduces market value and in extreme cases destroys the entire harvest. Farmers have to resort to periodical full-field fungicide applications to prevent the spread of disease, once every 1 to 5 weeks, depending on the cultivar and weather conditions. This implies an economic cost for the farmer and an environmental cost for society. Hyperspectral sensors have been extensively used to address this issue in research, but their application in the field has been limited to a relatively low number of crops, excluding leek, due to the high investment costs and complex data gathering and analysis associated with these sensors. To fill this gap, a methodology was developed for detecting leek rust disease using hyperspectral proximal sensing data combined with supervised machine learning. First, a hyperspectral library was constructed containing 43,416 spectra with a waveband range of 400-1000 nm, measured under field conditions. Then, an extensive evaluation of 11 common classifiers was performed using the scikit-learn machine learning library in Python, combined with a variety of wavelength selection techniques and preprocessing strategies. The best performing model was a (linear) logistic regression model that was able to correctly classify rust disease with an accuracy of 98.14%, using reflectance values at 556 and 661 nm, combined with the value of the first derivative at 511 nm. This model was used to classify unlabelled hyperspectral images, confirming that the model was able to accurately classify leek rust disease symptoms. It can be concluded that the results in this work are an important step towards the mapping of leek rust disease, and that future research is needed to overcome certain challenges before variable rate fungicide applications can be adopted against leek rust disease

Practical recommendations for hyperspectral and thermal proximal disease sensing in potato and leek fields

Thermal and hyperspectral proximal disease sensing are valuable tools towards increasing pesticide use efficiency. However, some practical aspects of the implementation of these sensors remain poorly understood. We studied an optimal measurement setup combining both sensors for disease detection in leek and potato. This was achieved by optimising the signal-to-noise ratio (SNR) based on the height of measurement above the crop canopy, off-zenith camera angle and exposure time (ET) of the sensor. Our results indicated a clear increase in SNR with increasing ET for potato. Taking into account practical constraints, the suggested setup for a hyperspectral sensor in our experiment involves (for both leek and potato) an off-zenith angle of 17 degrees, height of 30 cm above crop canopy and ET of 1 ms, which differs from the optimal setup of the same sensor for wheat. Artificial light proved important to counteract the effect of cloud cover on hyperspectral measurements. The interference of these lamps with thermal measurements was minimal for a young leek crop but increased in older leek and after long exposure. These results indicate the importance of optimising the setup before measurements, for each type of crop

Recommendations for the standardisation of open taxonomic nomenclature for image-based identifications

This paper recommends best practice for the use of open nomenclature (ON) signs applicable to image-based faunal analyses. It is one of numerous initiatives to improve biodiversity data input to improve the reliability of biological datasets and their utility in informing policy and management. Image-based faunal analyses are increasingly common but have limitations in the level of taxonomic precision that can be achieved, which varies among groups and imaging methods. This is particularly critical for deep-sea studies owing to the difficulties in reaching confident species-level identifications of unknown taxa. ON signs indicate a standard level of identification and improve clarity, precision and comparability of biodiversity data. Here we provide examples of recommended usage of these terms for input to online databases and preparation of morphospecies catalogues. Because the processes of identification differ when working with physical specimens and with images of the taxa, we build upon previously provided recommendations for specific use with image-based identifications

The Magnitude of Global Marine Species Diversity

Background: The question of how many marine species exist is important because it provides a metric for how much we do and do not know about life in the oceans. We have compiled the first register of the marine species of the world and used this baseline to estimate how many more species, partitioned among all major eukaryotic groups, may be discovered. Results: There are ∼226,000 eukaryotic marine species described. More species were described in the past decade (∼20,000) than in any previous one. The number of authors describing new species has been increasing at a faster rate than the number of new species described in the past six decades. We report that there are ∼170,000 synonyms, that 58,000–72,000 species are collected but not yet described, and that 482,000–741,000 more species have yet to be sampled. Molecular methods may add tens of thousands of cryptic species. Thus, there may be 0.7–1.0 million marine species. Past rates of description of new species indicate there may be 0.5 ± 0.2 million marine species. On average 37% (median 31%) of species in over 100 recent field studies around the world might be new to science. Conclusions: Currently, between one-third and two-thirds of marine species may be undescribed, and previous estimates of there being well over one million marine species appear highly unlikely. More species than ever before are being described annually by an increasing number of authors. If the current trend continues, most species will be discovered this century

The two phases of the Cambrian Explosion

Abstract The dynamics of how metazoan phyla appeared and evolved – known as the Cambrian Explosion – remains elusive. We present a quantitative analysis of the temporal distribution (based on occurrence data of fossil species sampled in each time interval) of lophotrochozoan skeletal species (n = 430) from the terminal Ediacaran to Cambrian Stage 5 (~545 – ~505 Million years ago (Ma)) of the Siberian Platform, Russia. We use morphological traits to distinguish between stem and crown groups. Possible skeletal stem group lophophorates, brachiopods, and molluscs (n = 354) appear in the terminal Ediacaran (~542 Ma) and diversify during the early Cambrian Terreneuvian and again in Stage 2, but were devastated during the early Cambrian Stage 4 Sinsk extinction event (~513 Ma) never to recover previous diversity. Inferred crown group brachiopod and mollusc species (n = 76) do not appear until the Fortunian, ~537 Ma, radiate in the early Cambrian Stage 3 (~522 Ma), and with minimal loss of diversity at the Sinsk Event, continued to diversify into the Ordovician. The Sinsk Event also removed other probable stem groups, such as archaeocyath sponges. Notably, this diversification starts before, and extends across the Ediacaran/Cambrian boundary and the Basal Cambrian Carbon Isotope Excursion (BACE) interval (~541 to ~540 Ma), ascribed to a possible global perturbation of the carbon cycle. We therefore propose two phases of the Cambrian Explosion separated by the Sinsk extinction event, the first dominated by stem groups of phyla from the late Ediacaran, ~542 Ma, to early Cambrian stage 4, ~513 Ma, and the second marked by radiating bilaterian crown group species of phyla from ~513 Ma and extending to the Ordovician Radiation

The Automation of Hyperspectral Training Library Construction: A Case Study for Wheat and Potato Crops

The potential of hyperspectral measurements for early disease detection has been investigated by many experts over the last 5 years. One of the difficulties is obtaining enough data for training and building a hyperspectral training library. When the goal is to detect disease at a previsible stage, before the pathogen has manifested either its first symptoms or in the area surrounding the existing symptoms, it is impossible to objectively delineate the regions of interest containing the previsible pathogen growth from the areas without the pathogen growth. To overcome this, we propose an image labelling and segmentation algorithm that is able to (a) more objectively label the visible symptoms for the construction of a training library and (b) extend this labelling to the pre-visible symptoms. This algorithm is used to create hyperspectral training libraries for late blight disease (Phytophthora infestans) in potatoes and two types of leaf rust (Puccinia triticina and Puccinia striiformis) in wheat. The model training accuracies were compared between the automatic labelling algorithm and the classic visual delineation of regions of interest using a logistic regression machine learning approach. The modelling accuracies of the automatically labelled datasets were higher than those of the manually labelled ones for both potatoes and wheat, at 98.80% for P. infestans in potato, 97.69% for P. striiformis in soft wheat, and 96.66% for P. triticina in durum wheat

Potential of laboratory hyperspectral data for in-field detection of Phytophthora infestans on potato

Researchers have shown increasing interest in hyperspectral imaging for detecting potato late blight disease (Phytophthora infestans). Because it is difficult to get accurate spectral signatures of disease development in field conditions, especially at early disease stages, previous works focused on laboratory measurements under controlled conditions. However, the extrapolation of results from a laboratory to a field setting has proven difficult. The current work evaluates the use of laboratory hyperspectral data to train an in-field detection model for potato late blight. A hyperspectral training library was constructed from six detached leaf trays, containing 8585 spectra labelled into a healthy class and five progressive stages of disease development. After smoothing and normalisation, a logistic regression model was trained on 70.0% of this data, with 30.0% reserved for validation. Twelve hyperspectral images taken in field conditions were then classified, for two potato cultivars (susceptible and resistant to late blight), at high and low disease pressure. The classification accuracy of laboratory data was 94.1%, which was not sufficient to detect field symptoms, using infield collected dataset. When spectra pre-processing was changed by including first derivation and adopting a new normalisation strategy, a new model resulted in a lower classification accuracy of 80.8%, validated on labelled laboratory spectra, but was able to detect symptoms in field conditions. The correlation between visual disease scoring and the classification result of the field disease model yielded an R-2 value of 0.985. It could be concluded that it was possible to train a model on laboratory data for in-field disease detection