A Comprehensive Modeling Approach for Crop Yield Forecasts using AI-based Methods and Crop Simulation Models

Numerous solutions for yield estimation are either based on data-driven models, or on crop-simulation models (CSMs). Researchers tend to build data-driven models using nationwide crop information databases provided by agencies such as the USDA. On the opposite side of the spectrum, CSMs require fine data that may be hard to generalize from a handful of fields. In this paper, we propose a comprehensive approach for yield forecasting that combines data-driven solutions, crop simulation models, and model surrogates to support multiple user-profiles and needs when dealing with crop management decision-making. To achieve this goal, we have developed a solution to calibrate CSMs at scale, a surrogate model of a CSM assuring faster execution, and a neural network-based approach that performs efficient risk assessment in such settings. Our data-driven modeling approach outperforms previous works with yield correlation predictions close to 91\%. The crop simulation modeling architecture achieved 6% error; the proposed crop simulation model surrogate performs predictions almost 100 times faster than the adopted crop simulator with similar accuracy levels

Simulation of partnership formation between agents based on the concept of reputation.

Os conceitos de reputação e confiança, tão difundidos em outras áreas de pesquisa, mostraram-se úteis também em sistemas multiagentes (SMA), particularmente no processo de formação de parcerias, em que agentes selecionam parceiros com os quais irão cooperar. Apesar dos inúmeros trabalhos desenvolvidos na área de SMA que propõem modelos para o cálculo de reputação e confiança, um aspecto essencial do uso de tais modelos ainda não foi suficientemente estudado: como estes conceitos podem efetivamente ajudar agentes autônomos a agir em um sistema aberto. Do ponto de vista do agente, existem algumas questões importantes relacionadas a este cenário: (1) como escolher um parceiro, levando em conta a reputação do candidato e o custo associado à parceria? (2) o quão vantajoso é manipular informação? (3) como agir em uma sociedade norteada pelo conceito de reputação a fim de atingir melhores resultados? Este trabalho tem por objetivo promover uma análise de tais questões por meio da discussão de resultados obtidos com uma ferramenta de simulação denominada RePart, criada especificamente para este fim. As simulações apresentam como cenário um modelo simplificado de um mercado constituído por consumidores e empresas.The concepts of reputation and trust, largely researched in other fields, have proved to be also very useful in multi-agent systems (MAS), specially in the process of partnership formation, in which agents select partners to cooperate with. Despite all research that has been carried out in the past few years concerning different reputation and trust models for autonomous agents, an essential aspect related to the use of these models was not sufficiently stressed: how these concepts may effectively help an autonomous agent in order to better evolve in an open MAS scenario. From an agent perspective, there are some important questions regarding this issue: (1) how to choose a partner, taking into account its reputation and the costs associated with the partnership? (2) is it worth to manipulate information? (3) how to act in a society guided by the concept of reputation in order to achieve better results? This work promotes the analysis of these questions through the discussion of the results obtained from a reputation-based partnership formation simulator called RePart, which was specifically designed for this goal. The simulations present a simplified consumer/enterprise market scenario as a target domain