Using nondeterministic learners to alert on coffee rust disease

Motivated by an agriculture case study, we discuss how to learn functions able to predict whether the value of a continuous target variable will be greater than a given threshold. In the application studied, the aim was to alert on high incidences of coffee rust, the main coffee crop disease in the world. The objective is to use chemical prevention of the disease only when necessary in order to obtain healthier quality products and reductions in costs and environmental impact. In this context, the costs of misclassifications are not symmetrical: false negative predictions may lead to the loss of coffee crops. The baseline approach for this problem is to learn a regressor from the variables that records the factors affecting the appearance and growth of the disease. However, the number of errors is too high to obtain a reliable alarm system. The approaches explored here try to learn hypotheses whose predictions are allowed to return intervals rather than single points. Thus,in addition to alarms and non-alarms, these predictors identify situations with uncertain classification, which we call warnings. We present 3 different implementations: one based on regression, and 2 more based on classifiers. These methods are compared using a framework where the costs of false negatives are higher than that of false positives, and both are higher than the cost of warning prediction

Early warning system for coffee rust disease based on error correcting output codes: a proposal

Colombian coffee producers have had to face the severe consequences of the coffee rust disease since it was first reported in the country in 1983. Recently, machine learning researchers have tried to predict infection through classifiers such as decision trees, regression Support Vector Ma­chines (SVM), non-deterministic classifiers and Bayesian Networks, but it has been theoretically and empirically demonstrated that combining multiple classifiers can substantially improve the classification perfor­mance of the constituent members. An Early Warning System (EWS) for coffee rust disease was therefore proposed based on Error Correcting Output Codes (ECOC) and SVM to compute the binary functions of Plant Density, Shadow Level, Soil Acidity, Last Nighttime Rainfall Intensity and Last Days Relative Humidity.Los productores de café colombianos han sufrido severas consecuencias por la Roya desde que fue reportada por primera vez en el país en el año 1983. Recientemente, investigadores de aprendizaje automático han intentado predecir la roya a través de clasificadores como: arboles de de­cisión, máquinas de vector de soporte, clasificadores no determinísticos y redes bayesianas, pero se ha demostrado teórica y empíricamente que la combinación de múltiples clasificadores puede mejorar sustancialmente el rendimiento en la clasificación. En este sentido es propuesto un sistema de alerta temprana para la roya en el café, basado en códigos de salida de corrección de error y máquinas de vector de soporte para calcular las funciones binarias de la densidad de planta, el nivel de sombra, la acidez del suelo, la intensidad de lluvia en la última noche, y en últimos días, con humedad relativa

Sistema de alerta temprana para la roya en el café basado en códigos de salida de corrección de error: una propuesta

Colombian coffee producers have had to face the severe consequences of the coffee rust disease since it was first reported in the country in 1983. Recently, machine learning researchers have tried to predict infection through classifiers such as decision trees, regression Support Vector Ma­chines (SVM), non-deterministic classifiers and Bayesian Networks, but it has been theoretically and empirically demonstrated that combining multiple classifiers can substantially improve the classification perfor­mance of the constituent members. An Early Warning System (EWS) for coffee rust disease was therefore proposed based on Error Correcting Output Codes (ECOC) and SVM to compute the binary functions of Plant Density, Shadow Level, Soil Acidity, Last Nighttime Rainfall Intensity and Last Days Relative Humidity.Los productores de café colombianos han sufrido severas consecuencias por la Roya desde que fue reportada por primera vez en el país en el año 1983. Recientemente, investigadores de aprendizaje automático han intentado predecir la roya a través de clasificadores como: arboles de de­cisión, máquinas de vector de soporte, clasificadores no determinísticos y redes bayesianas, pero se ha demostrado teórica y empíricamente que la combinación de múltiples clasificadores puede mejorar sustancialmente el rendimiento en la clasificación. En este sentido es propuesto un sistema de alerta temprana para la roya en el café, basado en códigos de salida de corrección de error y máquinas de vector de soporte para calcular las funciones binarias de la densidad de planta, el nivel de sombra, la acidez del suelo, la intensidad de lluvia en la última noche, y en últimos días, con humedad relativa

Un nuevo conjunto de datos para la detección de roya en cultivos de café Colombianos basado en clasificadores

Coffee production is the main agricultural activity in Colombia. More than 350.000 Colombian families depend on coffee harvest. Since coffee rust disease was first reported in the country in 1983, these families have had to face severe consequences. Recently, machine learning approaches have built a dataset for monitoring coffee rust incidence that involves weather conditions and physic crop properties. This background encouraged us to build a dataset for coffee rust detection in Colombian crops through data mining process as Cross Industry Standard Process for Data Mining (CRISP-DM). In this paper we define a proper data to generate accurate models; once the dataset is built, this is tested using classifiers as: Support Vector Regression, Backpropagation Neural Networks and Regression Trees.La producción de café es la principal actividad agrícola en Colombia. Más de 350.000 familias colombianas dependen de la cosecha de café. En este sentido, la roya fue reportada por primera vez en el país en 1983, y desde entonces estas familias han tenido que enfrentar graves consecuencias. Recientemente, diversos enfoques basados en aprendizaje automático han construido un conjunto de datos para el monitoreo de la incidencia de la roya del café, teniendo en cuenta las condiciones climáticas y las propiedades físicas de los cultivos. Estas investigaciones motivaron la creación de un conjunto de datos para la detección de la roya en cultivos Colombianos a través del proceso de minería de datos CRISP-DM. En este trabajo se definió un conjunto de datos con el objetivo de generar clasificadores precisos; una vez construido el conjunto de datos, fue probado mediante tres clasificadores: Maquinas de vector de regresión, Redes neuronales con propagación hacia atrás y Árboles de regresión

RustOnt: An Ontology to Explain Weather Favorable Conditions of the Coffee Rust

Crop disease management in smart agriculture involves applying and using new technologies to reduce the impact of diseases on the quality of products. Coffee rust is a disease that factors such as poor agronomic management activities and climate conditions may favor. Therefore, it is crucial to identify the relationships between these factors and this disease to learn how to face its consequences and build intelligent systems to provide appropriate management or help farmers and experts make decisions accordingly. Nevertheless, there are no studies in the literature that propose ontologies to model these factors and coffee rust. This paper presents a new ontology called RustOnt to help experts more accurately model data, expressions, and samples related to coffee rust and apply it whilst taking into account the geographical location where the ontology is adopted. Consequently, this ontology is crucial for coffee rust monitoring and management by means of smart agriculture systems. RustOnt was successfully evaluated considering quality criteria such as clarity, consistency, modularity, and competence against a set of initial requirements for which it was built.project "System based on knowledge engineering for the agroecological management of coffee rust", grant 823-Formation of high-level human capital for the regions-Cauc

TIC na segurança fitossanitária das cadeias produtivas.

Com a intensificação da indústria agropecuária, têm crescido os desafios e as preocupações relacionadas à segurança sanitária dos alimentos produzidos. A circulação de volumes cada vez maiores desse tipo de mercadoria exige que as medidas necessárias para garantir sua segurança sanitária sejam implementadas de maneira rápida, eficiente e barata. O controle manual tradicionalmente utilizado muitas vezes não é capaz de atender a esses requisitos. Como resultado, tecnologias de informação e comunicação têm sido cada vez mais utilizadas para: 1) Aumentar o grau de automação e, consequentemente, a velocidade dos processos de controle fitossanitário. 2) Identificar problemas sanitários tão cedo quanto possível, minimizando possíveis prejuízos econômicos, ambientais e sociais. 3) Identificar, a partir de variáveis ambientais e históricas, áreas potencialmente sujeitas a problemas sanitários, antes mesmo destes se manifestarem. Este capítulo trata especificamente dos dois últimos itens. Na Seção 2, são mostradas iniciativas voltadas ao diagnóstico de doenças em plantas, explorando tecnologias como processamento digital de imagens e sistemas especialistas. A Seção 3, por sua vez, apresenta iniciativas voltadas à construção de modelos de previsão e sistemas de alerta de doenças de culturas agrícolas

Songbird-mediated Insect Pest Control in Low Intensity New England Agriculture

Global agricultural intensification has caused large-scale wildlife declines, but agricultural lands that maintain natural habitats can support healthy wildlife populations and receive significant ecosystem services from these natural communities. However, how on-farm biodiversity results in beneficial ecosystem services is highly variable and is reported to differ among taxa and guilds. One group that has attracted attention for their potential beneficial role in reducing pest abundance are birds. Understanding the role of bird communities and individual species in pest control could be important for managing farms under a low intensity agroecological framework. In New England, farmers are increasingly applying low intensity agricultural practices, and these low intensity farms have high conservation value for bird communities. The value of bird communities to on-farm productivity, however, remains poorly understood. Therefore, we quantified the amount of insect pest control provided by birds to three important crops to New England farmers: brassicas (e.g., kale, broccoli), cucurbits (e.g., squash, cucumber), and Solanaceae (e.g., eggplant, potato). We also examined the role of different songbird species in the provision of pest control in this system. To determine the amount of pest control services provided by birds in this system, we conducted an exclusion experiment at nine low intensity farms in Franklin and Hampshire counties of Massachusetts. Birds were excluded from crops, and pest abundance and leaf damage were compared between exclusion plots and immediately adjacent control plots. In brassica crops, the abundance of imported cabbageworm (Pieris rapae) and diamondback moth (Plutella xylostella) were significantly reduced, while cabbage looper (Trichoplusia ni) was not significantly affected. In cucurbit crops, all life stages of squash bugs (Anasa tristis) were significantly reduced, though striped cucumber beetle (Acalymma vittatum) populations were not significantly changed. In Solanaceous crops, bird presence caused significantly larger populations of Colorado potato beetle (Leptinotarsa decemlineata) larvae, while the other life stages of Colorado potato beetle and aphids (superfamily Aphidoidea) were not significantly affected. Leaf damage was reduced by bird presence in all three crop types, though this effect was only significant for cucurbits. The varied effects of bird predation in different crop types highlights the need for crop-specific knowledge in applying agroecological pest management in New England. To determine the roles of different bird species in insect pest control, bird diets were studied at 11 low intensity farms in western Massachusetts. DNA metabarcoding was used to determine the frequency of crop pests and pest natural enemies in fecal samples collected from birds on each farm. We found evidence of pest species being consumed in 12.6% of the 737 total fecal samples collected, while pest natural enemies were present in 2.0% of samples. Among bird species, Gray Catbirds and Common Yellowthroats were determined to feed on crop pests significantly more frequently than Song Sparrows, while no bird species effect was found for natural enemy frequency. The only crop pest surveyed in our exclosure experiment which was present in fecal samples was Colorado potato beetle. Though birds preyed on Colorado potato beetle, they also preyed on two known predators of Colorado potato beetle eggs and larvae: Chrysopa oculata and Chrysoperla rufilabris. This provides evidence that the increase in Colorado potato beetle larvae we observed when birds were present was due to ecological release. Combined, our results show that birds provide important, though variable, insect pest control services on low intensity New England farms. Bird predation had primarily beneficial impacts on crops, suppressing abundance of several pest species and decreasing or minimally affecting leaf damage. The effects of bird predation on pest abundance and damage can be integrated into farm management to control insect pests without reliance on expensive, and sometimes damaging, outside inputs like pesticides. Promotion of woody, non-crop habitats on farms can promote species like Gray Catbirds and Common Yellowthroats that feed more frequently on insect pests. Management of New England farmlands for bird pest control may support healthy bird communities and improve agricultural output

Human behaviour and ecosystem services in sustainable farming landscapes : an agent-based model of socio-ecological systems

Agricultural areas represent around 40% of the earth surface and provide a variety of products and services essential to human societies. However, with policy reforms, market liberalisation and climate change issues, continuous land use and cover change (LUCC) brings uncertainty in the quantity and quality of ecosystem services supplied for the future generations. The processes of LUCC have been explored using top-down approaches at global and regional level but more recent methods have focused on agents’ interactions at smaller scale. This approach is better suited to understanding and modelling complex socio-ecological systems, which emerge from individual actions, and therefore for developing tools which improve policy effectiveness. In recent years, there has also been increasing interest in gaining more detailed understanding of the impacts of LUCC on the range of ecosystem services associated with different landscapes and farming practices. The objectives of this thesis are: 1/ to understand and model the internal processes of LUCC at local scale, i.e. farmer behaviour, 2/ to explore heterogeneous farmer decision making and the impacts it has on LUCC and on ecosystem services and 3/ to inform policy makers for improving the effectiveness of land-related policies. This thesis presents an agent-based modelling framework which integrates psycho-social models of heterogeneous farmer decisions and an ecological model of skylark breeding population. The model is applied to the Lunan, a small Scottish arable catchment, and is empirically-grounded using social surveys, i.e. phone interviews and choice-based conjoint experiments. Based on ecological attitudes and farming goals, three main types of farmer agents were generated: profit-oriented, multifunctionalist, traditionalist. The proportion of farmer types found within the survey was used to scale-up respondent results to the agent population, spatially distributed within a GIS-based representation of the catchment. Under three socio-economic scenarios, based on the IPCC-SRES framework, the three types of farmers maximise an utility function, which is disaggregated into economic, environmental and social preferences, and apply the farm strategy (i.e. land uses, management style, agri-environmental measures) that best satisfies them. Each type of agents demonstrates different reactions to market and policy pressures though farmers seem to be constrained by lack of financial opportunities and are therefore unable to fully comply with environmental and social goals. At the landscape level, the impacts on ecosystem services, in particular the skylark local population, depend strongly on policy objectives, which can be antagonist and create trade-offs in the provision of different services, and on farmer socio-environmental values. A set of policy recommendations is offered that encompasses the heterogeneity of farmer decision-making with the aim of meeting sustainable targets. Finally, further improvements of the conceptual and methodological framework are discussed

Biology, ecology and management of the Keurboom moth, Leto venus Cramer and the leafhopper Molopopterus sp. Jacobi in cultivated Honeybush (Cyclopia spp.)

Honeybush, Cyclopia spp. Vent (Fabaceae), farmers have raised pest concerns following commercial cultivation. The Keurboom moth Leto venus Cramer (Lepidoptera: Hepialidae) and the leafhopper Molopopterus sp. Jacobi (Hemiptera: Cicadellidae), are two of the major pests identified in cultivated Honeybush. Laboratory and field studies were conducted to gain an understanding of the biology of these two pests to inform future pest management solutions. Additionally, entomopathogenic fungi were isolated from Honeybush farms and screened for virulence against Molopopterus sp. as a possible management strategy. This study showed that the L. venus infestation on Honeybush was a product of four fixed effects; stem diameter, species of Cyclopia, Farm location and age of the plants. Cyclopia subternata, had the highest likelihood of infestation. Increase in age of the plants resulted in an increase in the stem diameter and therefore a higher probability of infestation. Stem diameter was also shown to be a significant predictor of infestation likelihood. Infestation severity, determined by the number of larvae per plant, was shown to be influenced by three fixed effects; stem diameter, plant species and Farm location. The results also showed that L. venus prefers to initiate penetration at, or just aboveground level. Laboratory studies showed that the leafhopper Molopopterus sp. undergoes five nymphal instars with an average egg incubation time of 20 days, development time from 1st instar to adult of 26 days and average generation time of 47 days. Laboratory experiments revealed variations in host preference by the leafhopper over a period of 15 days. Cyclopia longifolia was identified to be the most preferred species for feeding compared to the two other commonly cultivated species, C. subternata and C. maculata. The results were consistent with those obtained from the field survey which showed that leafhopper density was influenced by four fixed effects; plant species, age of the plant, Farm location and harvesting practices. There were significant differences in leafhopper density in different species with C. longifolia having the highest number of leafhoppers per plant. There were differences in leafhopper density in different farms as 57% of the sampled farms had leafhopper infestations, of these farms, Lodestone and Kurland had the highest leafhopper densities. Harvested plants were shown to have significantly higher leafhopper density than non-harvested plants. Age was also shown to influence leafhopper density, which reduced with an increase in the age of the plants. A total of 20 fungal isolates were recovered from 98 soil samples of which 70% were from Honeybush fields and 30% were from surrounding refugia. Fusarium oxysporum isolates comprised 20% of the recovered isolates, with Metarhizium anisopliae isolates making up the remainder. Laboratory bioassays against adults and nymphs of the leafhopper, Molopopterus sp., showed that F. oxysporum isolates induced 10 – 45% mortality and M. anisopliae isolates induce 30 – 80% mortality. Metarhizium anisopliae isolates J S1, KF S3, KF S11, KF S13, LS1 and LS2 were the most virulent and induced over 60% mortality in both Molopopterus sp. nymphs and adults. The results of this study showed pest preference towards different Cyclopia species. As such, they should be managed differently. Furthermore, L. venus was observed to occur in low densities, hence, it cannot be considered a major pest. However, Molopopterus sp. recorded high population densities making it a major pest in Honeybush production. Positive results indicated that some of the isolated fungal isolates have potential for control, an avenue worth investigating further.Thesis (MSc) -- Faculty of Science, Department of Zoology and Entomology, 202