Prioritizing the risk of plant pests by clustering methods; self-organising maps, k-means and hierarchical clustering

For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential to establish and cause significant impact in an endangered area. Such prioritization is often qualitative, subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years, cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to indicate the risk of new organism establishment. Such an approach is based on the premise that the cooccurrence of well-known global invasive pest species in a region is not random, and that the pest species profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational intelligence method called a Kohonen self-organizing map (SOM), a type of artificial neural network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a well known dimension reduction and visualization method especially useful for high dimensional data that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and recipient regions. More important, however SOM connection weights that result from the analysis can be used to rank the strength of association of each species within each regional assemblage. Species with high weights that are not already established in the target region are identified as high risk. However, the SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species risk assessment, and discuss other clustering methods such as k-means, hierarchical clustering and the incorporation of the SOM analysis into criteria based approaches to assess pest risk

Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healthy and diseased plant classification’ with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method

A Data-driven Approach for Detecting Stress in Plants Using Hyperspectral Imagery

A phenotype is an observable characteristic of an individual and is a function of its genotype and its growth environment. Individuals with different genotypes are impacted differently by exposure to the same environment. Therefore, phenotypes are often used to understand morphological and physiological changes in plants as a function of genotype and biotic and abiotic stress conditions. Phenotypes that measure the level of stress can help mitigate the adverse impacts on the growth cycle of the plant. Image-based plant phenotyping has the potential for early stress detection by means of computing responsive phenotypes in a non-intrusive manner. A large number of plants grown and imaged under a controlled environment in a high-throughput plant phenotyping (HTPP) system are increasingly becoming accessible to research communities. They can be useful to compute novel phenotypes for early stress detection. In early stages of stress induction, plants manifest responses in terms of physiological changes rather than morphological, making it difficult to detect using visible spectrum cameras which use only three wide spectral bands in the 380nm - 740 nm range. In contrast, hyperspectral imaging can capture a broad range of wavelengths (350nm - 2500nm) with narrow spectral bands (5nm). Hyperspectral imagery (HSI), therefore, provides rich spectral information which can help identify and track even small changes in plant physiology in response to stress. In this research, a data-driven approach has been developed to identify regions in plants that manifest abnormal reflectance patterns after stress induction. Reflectance patterns of age-matched unstressed plants are first characterized. The normal and stressed reflectance patterns are used to train a classifier that can predict if a point in the plant is stressed or not. Stress maps of a plant can be generated from its hyperspectral image and can be used to track the temporal propagation of stress. These stress maps are used to compute novel phenotypes that represent the level of stress in a plant and the stress trajectory over time. The data-driven approach is validated using a dataset of sorghum plants exposed to drought stress in a LemnaTec Scanalyzer 3D HTPP system. Advisers: Ashok Samal and Sruti Das Choudhur

Effect of Previous Crops and Soil Physicochemical Properties on the Population of Verticillium dahliae in the Iberian Peninsula

The soil infestation of Verticillium dahliae has significant Verticillium wilt of olive (VWO) with epidemiological consequences which could limit the expansion of the crop. In this context, there is a misunderstood history of the crops and soil property interactions associated with inoculum density (ID) increases in the soil. In this study, the effect of the combination of both factors was assessed on the ID of V. dahliae in the olive-growing areas of the Iberian Peninsula. Afterwards, the relationship of the ID to the mentioned factors was explored. The detection percentage and ID were higher in Spain than Portugal, even though the fields with a very favourable VWO history had a higher ID than that of the fields with a barely favourable history, regardless of the origin. The soil physicochemical parameters were able to detect the degree to which the ID was increased by the previous cropping history. By using a decision tree classifier, the percentage of clay was the best indicator for the V. dahliae ID regardless of the history of the crops. However, active limestone and the cation exchange capacity were only suitable ID indicators when <2 or 4 host crops of the pathogen were established in the field for five years, respectively. The V. dahliae ID was accurately predicted in this study for the orchard choices in the establishment of the olive

Integrative Advances in Rice Research

This book describes some recent advances in rice research in terms of crop breeding and improvement (Section 1), crop production and protection (Section 2), and crop quality control and food processing (Section 3). It contains fourteen chapters that cover such topics as two-line rice breeding in India, the different aspects of aromatic rice, bacterial diseases of rice, quality control and breeding strategies, and much more. This volume is a useful reference for professionals and graduate students working in all areas of rice science and technology

ADAPTIVE PROCESSING ARCHITECTURE OF MULTISENSOR SIGNALS FOR LOW-IMPACT TREATMENTS OF PLANT DISEASES.

Intelligent sensing for production of high-value crops Scientific and technical quality This thesis has been realized within the CROPS project. CROPS will develop scientific know-how for a highly configurable, modular and clever carrier platform that includes modular parallel manipulators and intelligent tools (sensors, algorithms, sprayers, grippers) that can be easily installed onto the carrier and are capable of adapting to new tasks and conditions. Several technological demonstrators will be developed for high value crops like greenhouse vegetables, fruits in orchards, and grapes for premium wines. The CROPS robotic platform will be capable of site-specific spraying (targets spray only towards foliage and selective targets) and selective harvesting of fruit (detects the fruit, determines its ripeness, moves towards the fruit, grasps it and softly detaches it). Another objective of CROPS is to develop techniques for reliable detection and classification of obstacles and other objects to enable successful autonomous navigation and operation in plantations and forests. The agricultural and forestry applications share many research areas, primarily regarding sensing and learning capabilities. The project started in October 2010 and will run for 48 month. The aim of this thesis is to lay the foundations, suggesting the guidelines, of one task addressed by the CROPS project, in particular, the aim of this work is to study the application of a VIS-NIR imaging approach (intelligent sensing), based on a relatively simple algorithm, to detect symptoms of powdery mildew and downy mildew disease at early stages of infection (sustainable production of high-value crops). Also a preliminary work for botrytis detection will be shown. Concept and objectives Many site-specific agricultural and forestry tasks, such as cultivating, transplanting, spraying, trimming, selective harvesting, and transportation, could be performed more efficiently if carried out by robotic systems. However, to date, agriculture and forestry robots are still not available, partly due to the complex, and often contradictory, demands for developing such systems. On the one hand, agro-forestry robots must be of reasonable cost, but on the other, they must be able to deal with complex, dynamic, and partly changing tasks. Addressing problems such as continuously changing conditions (e.g., rain and illumination), high variability in both the products (size, and shape) and the environment (location and soil properties), the delicate nature of the products, and hostile environmental conditions (e.g. dust, dirt, extreme temperature and humidity) requires advanced sensing, manipulation, and control. Since it is impossible to model a-priori all environments and task conditions, the robot must be able to learn new tasks and new working conditions. The solution to these demands lies in a modular and configurable design that will keep costs to a minimum by applying a basic configuration to a range of agricultural applications. At least a 95% yield rate is necessary for economical feasibility of an agro-forestry robotic system. Objectives An objective of CROPS project is to develop an \u201cintelligent tools\u201d (sensors, algorithms, sprayers) that can easily be installed onto a modular and clever carrier platform. The CROPS robotic platform will be capable of site-specific spraying (targeted spraying only on foliage and selected targets). Research efforts To achieve the novel systems described above, we will focus on intelligent sensing of disease detection on crop canopy (investigating different types and/or multiple sensors with decision making models). Technology evaluation Technology evaluation of the developed systems will include the performance evaluation of the different components (e.g., capacities, success rates/misses). Progress beyond the state-of-the-art Despite the extensive research conducted to date in applying robots to a variety of agriculture and forestry tasks (e.g., transplanting, spraying, trimming, selective harvesting), limited operating efficiencies (speeds, success rates) and lack of economic justification have severely limited commercialization. The few commercial autonomous agriculture and forestry robots that are available on the market include a cow milking robot, a robot for cutting roses (RomboMatic), and various remote-controlled forest harvesters. These robots either have a low level of autonomy or are able to perform only simple operations in structured and static environments (e.g. dairy farms and plant breeding facilities). Developing capabilities for robots operating in unstructured outdoor environments or dealing with the highly variable objects that exist in agriculture and forestry is still open-ended, and one of CROPS aims is to address this problem. Current state-of-the-art Field trials have routinely shown that most crop damage due to diseases and pests can be efficiently controlled when treatments are applied timely and accurately by hand to susceptible targets (i.e., by intelligent spraying). Site-specific spraying targeted solely to trees and/or to infected areas can reduce pesticide use by 20\u201340%. An issue of relevance to targeted agriculture is the detection of diseases in field crops. Since such events often have a visual manifestation, state-of-the-art methods for achieving this goal include fluorescence imaging or the analysis of spectral reflectance in carefully selected spectral bands. While reports of these methods used separately achieved performance at 75\u201390% accuracy, attempts to combine them have boosted disease discrimination accuracy to 95%. We must note here, however, that despite these promising results, very little research has been conducted on in-field disease detection. Expected progress The diseased detection approach for precision pesticide spraying will be developed investigating image processing techniques (after a laboratory spectral evaluation and greenhouse testing) for high-precision close-range targeted spraying to selectively and precisely apply chemicals solely to targets susceptible to specific diseases/pests, with a mean 90% success rate. Local changes in spectral reflection of parts of the canopy will be used as an indication of disease. \u201cSoft-sensor\u201d for detection of ripeness and diseases (noncontact rapid sensing system) will be developed by multispectral sensor (multispectral spectral camera). These \u201csoft sensor\u201d can be used as a decision model for targeted spraying

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Ensemble models to assess the risk of exotic plant pathogens in a changing climate

In recent decades, species distribution models (SDMs) have been widely used in many ecological, environmental and climate-change research studies to model invasive species establishment. These models associate recorded locations of species with environmental variables. Nevertheless, the few studies that attempt to model the climate suitability of plant pathogens before their arrival into a new area mainly rely on a single model projection. In this research, eleven species distribution models (in the form of three modelling approaches which include correlative and mechanistic models) were used to project the climate suitability of three target species; kiwifruit bacterial canker (Pseudomonas syringae pv. actinidia) (Psa), dwarf bunt of wheat (Tellitia controversa) and guava rust (Puccinia psidii) for New Zealand and over a global scale. The climate suitability of target species was modelled using CLIMEX as a semi-mechanistic model, MaxEnt as a presence-only correlative model and Multi-Model Framework (which includes nine correlative models). While there were similarities with regard to climate suitability for target species projected by the models over both local and global scales, there were differences in their projection with respect to the degree and extent of suitability, making it hard to select one “best” model. All models were found to have their differences and weaknesses that are largely the result of difference in the theoretical basis and structure of each model. For example, compared with CLIMEX and the Multi-Model Framework, MaxEnt showed lower transferability of projection into new areas. Additionally, as a semi -mechanistic model, the uncertainty of CLIMEX projections was found to be increased by subjectivity in the parameter setting process. To illustrate the impact of parameter variability on the uncertainty of CLIMEX projections, a sensitivity analysis was performed on one of the target species (dwarf bunt) to measure the effect of error in important parameters on model output. The sensitivity analysis showed that for dwarf bunt, CLIMEX outputs were very sensitive to upper temperature threshold and soil moisture parameters, which highlight that sensitivity analysis, should be an integral part of any CLIMEX modelling. For Multi-Model, despite the advantages such as calculating different performance criteria, the importance and contribution of selected variables and their influence on model output is not given. Because of differences in model projection, a method was developed to benefit from the information provided by all the types of models, by combining the results of different model output into an ensemble, or more specifically, a consensus model. A variant of committee averaging was used where model outputs are converted to binary maps (presence- absence) which allow any kind of algorithm and output to be included. The resulting consensus model highlighted the areas where more than half of the models agreed on the climate suitability for target species establishment. Such a model that relies on agreement of model projections indicates with a level of certainty or uncertainty what is likely to happen and consequently can highlight areas, both locally and globally, that have a higher risk of target species establishment. Finally, the effect of climate change on climate suitability of target species was investigated using two scenarios (A1B and A2) for 2030 and 2090. The results showed that, the suitable areas decreased for Psa and dwarf bunt at different levels while guava rust suitability increased. The results of this thesis confirm that models with different theoretical foundation will give dissimilar predictions, and it is difficult to determine conclusively whether one model is superior to others. Among other recommendations, I strongly advise that researchers and risk assessors should not rely on a single-model projection. If time and resources are available, an appropriate ensemble of models should be used to investigate the climate suitability of plant pathogens. Keywords: Plant pathogens, kiwifruit bacterial canker (Psa), dwarf bunt, guava rust, climate suitability, Species distribution models (SDMs), CLIMEX, MaxEnt, Multi-Model Framework, correlative models, semi-mechanistic models, sensitivity analysis, consensus model, ensemble models, climate change, range expansion