Emmanuel Lasso

Graph Patterns as Representation of Rules Extracted from Decision Trees for Coffee Rust Detection

Diseases in Agricultural Production Systems represent one of the biggest drivers of losses and poor quality products. In the case of coffee production, experts in this area believe that weather conditions, along with physical properties of the crop are the main variables that determine the development of a disease known as Coffee Rust. On the other hand, several Artificial Intelligence techniques allow the analysis of agricultural environment variables in order to obtain their relationship with specific problems, such as diseases in crops. In this paper an extraction of rules to detect rust in coffee from induction of decision trees and expert knowledge is addressed. Finally, a graph-based representation of these rules is submitted, in order to obtain a model with greater expressiveness and interpretability.

Decision Support System for Coffee Rust Control Based on Expert Knowledge and Value-Added Services

In coffee production, the quality and quantity of harvests depends on the diseases treatment. One of the diseases with the most negative impact is the Coffee Rust. It causes losses around 30% in Colombian coffee crops. Sever-al studies propose the use of computer sciences techniques for the automat-ic detection of conditions that trigger epidemics. On the other hand, the knowledge of experts in the control of the disease needs to be disseminated in a more agile way so that the farmers can make correct decisions to avoid great losses in the production. This paper presents a Decision Support System (DSS) for Coffee Rust Control based on expert knowledge. It recommends the best alternative for the application of fungicides and proposes some value-added services based on the integration of its functionalities with those offered in the services of an Early Warning System (EWS) for Coffee Rust. Our proposal represents a highly scalable and flexible solution for the disease management at farmer level.

Towards an Alert System for Coffee Diseases and Pests in a Smart Farming Approach Based on Semi-supervised Learning and Graph Similarity

Smart Farming represents a new approach based on management of observation, measurement and response to internal and external variations in crops. This approach is closely related to a current trend area in Information and Communication Technologies such as Big Data. The application of machine learning techniques to agriculture data allows to assist in decision making and predict what will happen in the future (predictive analysis). From predictive models, the inexact graph matching would allow to establish the probability of occurrence of one or another disease or in such case the presence of a pest, based on the analysis of the crop conditions. This paper presents a review of some areas involved in the definition of an alert system for diseases and pests in a Smart Farming approach, based on machine learning and graph similarity. Finally, the integration of the mentioned areas for their application in coffee crops is proposed.

A Cloud-Based Platform for Decision Making Support in Colombian Agriculture: A Study Case in Coffee Rust

In the last years, the yield of Colombian crops has been affected by climate change. The weather variation affects the Colombian crops with the occurrence of diseases as coffee rust. To address the coffee rust control, we proposed a cloud-based platform for decision making support named AgroCloud. The coffee crop weather of 100 municipalities from upper basin of the Cauca river were monitored. This information was used to improve the disease control process. User Acceptance Test carried out with domain end users show that the platform is useful and is easily usable.

Expert system for coffee rust detection based on supervised learning and graph pattern matching

Diseases in agricultural production systems represent one of the main reasons of losses and poor-quality products. For coffee production, experts in this area suggest that weather conditions and crop physical properties are the main variables that determine the development of coffee rust. This paper proposes an extraction of rules to detect coffee rust from induction of decision trees and expert knowledge. In order to obtain a model with greater expressiveness and interpretability, a graph-based representation is proposed. Finally, the extracted rules are evaluated using an expert system supported on graph pattern matching.

Water quality detection based on a data mining process on the California estuary

Freshwater is considered one of the most important renewable natural resources of the planet. In this sense, it is vital to study and evaluate the water quality in rivers and basins. The USA and especially the border states like California face the same water problems as its southern neighbours, such as the deterioration of public drinking water systems and the continued appearance of pollutants that threaten domestic water sources. This implies the need to monitor and analyse the water supplies in each region. Several researches have been conducted to develop water quality detection systems through supervised learning algorithms. However, these research approaches set aside the data processing to improve the performance of supervised learning algorithms. This paper presents an improvement of data processing techniques for a water quality detection system based on supervised learning and data quality techniques for the California estuary.

Improving Early Warning Systems for Agriculture Based on Web Service Adaptation

Early warning systems are designed to inform the largest number of users, such as a country or a region, about a risky situation. However, in specific domains such as agriculture, it is commonly required that these alerts be more specific according to the crops location and their properties, consequently the web services of these systems must be adapted. On the other hand, the Enterprise Services Bus with its mediation capabilities (such as message transformation and routing) and Complex Event Processing with their monitoring characteristics can be integrated to meet the adaptation requirements of web services at runtime. This paper presents an improvement for Early Warning System for coffee production that, according to the area in which a crop is located and its phenology, manages the adaptation of alerts for coffee rust, based on the integration of an Enterprise Services Bus and a Complex Events Processing.

Coffee Rust Detection Based on a Graph Similarity Approach

Diseases affecting agricultural sectors are often closely related to weather conditions and crop management. In this regard, different researches have focused on identifying patterns that lead to the incidence of these diseases. This research was carried out in order to detect favorable conditions for rust in coffee trees (hemileia vastatrix) based on a graph representation of the Agroclimatic information of the crops. Furthermore, we adapted 4 error-correcting graph pattern matching algorithms, classified taking into account the precision and the execution time, in order to find a similarity percentage between current conditions of a coffee crop and the graph patterns that describe coffee rust infection rates.

Data Processing for a Water Quality Detection System on Colombian Rio Piedras Basin

Freshwater is considered one of the most important of planet’s renewable natural resources. In this sense, it is vital to study and evaluate the water quality in rivers and basins. A study area is Rio Piedras Basin, which is the main water supplier source of 9 rural communities in Colombia. Nevertheless, these communities do not make a water quality control. Different research has been conducted to develop water quality detection systems through supervised learning algorithms. However, these research approaches set aside the data processing for improve the outcomes of supervised learning algorithms. This paper presents an improvement of data processing techniques for a water quality detection system based on supervised learning and data quality techniques for Rio Piedras Basin.