Decision making for Farmers: A Case Study of Agricultural Routing Planning

Agricultural business is shifting to a stronger integration of information technology and data analysis to optimise the management and operations of small- and large-scale farms. In particular, computer support for decision-making is critical for farmers who want to decrease the cost of operations and control their (semi-)automated fleet of agricultural machines. This paper develops an optimisation module for decision support in Agricultural Routing Planning (ARP). The output is expected to help farmers to decide on the most efficient route for their harvesting machines. Specifically, the aim of this study is to contribute to optimisation solutions by introducing a new methodology called a Lovebird Algorithm, to address the routing problem. The Lovebird Algorithm acts as an optimisation tool to screen alternatives and focus only on efficient ones. The experimental results show that the proposed algorithm can save 8% of the non-working distance compared to the Genetic Algorithm and Tabu Search

Fuzzy modeling of coffee productivity under different irrigation depths, water deficit and temperature

The coffee culture has great economic importance on the world stage, especially for Brazil. Considered one of the most traded commodities on the world's trading exchanges. Thus, the main objective of this study was to develop a system based on fuzzy rules to evaluate coffee productivity, using irrigation, soil water deficit and ambient temperature as the main production factors. The research was developed from searches of scientific data on the main variables for coffee production. The work was divided into two stages: the first in the scientific search for data collection and the second in the development of the fuzzy model. With this, it was parameterized that the input variables would be the temperature, the irrigation depth, and the water deficit of the soil and for the output variable the coffee productivity. Based on the model prediction, the fuzzy system showed which variable values are necessary for the best coffee productivity, by a set of rules involving the variation of water deficit (60%), temperature (30°C) and irrigation (300 mm), for a productivity of 24 sc ha-1. The performance of the fuzzy system was tested by comparing it with articles on the subject that relate coffee production with irrigation, water deficit and temperature of the environment and in almost all cases the model was efficient, reinforcing the assessment of the strength of the scheme, the analysis was extended to several scenarios relating the same three input variables

Fuzzy control system for variable rate irrigation using remote sensing

Variable rate irrigation (VRI) is the capacity to spatially vary the depth of water application in a field to handle different types of soils, crops, and other conditions. Precise management zones must be developed to efficiently apply variable rate technologies. However, there is no universal method to determine management zones. Using speed control maps for the central pivot is one option. Thus, this study aims to develop an intelligent fuzzy inference system based on precision irrigation knowledge, i.e., a system that can create prescriptive maps to control the rotation speed of the central pivot. Satellite images are used in this study because remote sensing offers quick measurements and easy access to information on crops for large irrigation areas. Based on the VRI-prescribed map created using the intelligent decisionmaking system, the pivot can increase or decrease its speed, reaching the desired depth of application in a certain irrigation zone. Therefore, considering the spatial variability in the crop has made the strategy of speed control more realistic than traditional methods for crop management. The intelligent irrigation system pointed out areas with lower leaf development, indicating that the pivot must reduce its speed, thus increasing the water layer applied to that area. The existence of well-divided zones could be observed; each zone provides a specific value for the speed that the pivot must develop for decreasing or increasing the application of the water layer to the crop area. Three quarters of the total crop area had spatial variations during water application. The set point built by the developed system pointed out zones with a decreased speed in the order of 50%. From the viewpoint of a traditional control, the relay from pivot percent timer should have been adjusted from 70% to 35% whenever the central pivot passed over that specific area. The proposed system obtained values of 37% and 47% to adjust the pivot percent timer. Therefore, it is possible to affirm that traditional control models used for central-pivot irrigators do not support the necessary precision to meet the demands of speed control determined by the developed VRI systems. Results indicate that data from the edaphoclimatic variables when well-fitted to the fuzzy logic can solve uncertainties and non-linearities of an irrigation system and establish a control model for high-precision irrigation

Enabling explainable artificial intelligence capabilities in supply chain decision support making

Explainable artificial intelligence (XAI) has been instrumental in enabling the process of making informed decisions. The emergence of various supply chain (SC) platforms in modern times has altered the nature of SC interactions, resulting in a notable degree of uncertainty. This study aims to conduct a thorough analysis of the existing literature on decision support systems (DSSs) and their incorporation of XAI functionalities within the domain of SC. Our analysis has revealed the influence of XAI on the decision-making process in the field of SC. This study utilizes the SHapley Additive exPlanations (SHAP) technique to analysis the online data using Python machine learning (ML) process. Explanatory algorithms are specifically crafted to augment the lucidity of ML models by furnishing rationales for the prognostications they produce. The present study aims to establish measurable standards for identifying the constituents of XAI and DSSs that augment decision-making in the context of SC. This study assessed prior research with regards to their ability to make predictions, utilization of online dataset, number of variables examined, development of learning capability, and validation within the context of decision-making, emphasizes the research domains that necessitate additional exploration concerning intelligent decision-making under conditions of uncertainty

Adaptabilidade e estabilidade de genótipos de algodão colorido utilizando lógica Fuzzy

Trabalho de Conclusão de Curso (Graduação)O algodão é uma das principais culturas do país, com grande importância para a economia. A cada ano se busca aumentar a produtividade, a fim de atender a demanda mundial pela fibra. Assim sendo, programas de melhoramento buscam por genótipos cada vez mais produtivos para recomendação. No entanto, devido a grande diversidade de ambientes em que o algodão é cultivado, torna-se necessário estudos sobre a existência de interação genótipo x ambiente e, caso exista, avaliar a adaptabilidade e estabilidade dos genótipos frente aos estímulos ambientais e sua previsibilidade em relação às oscilações do ambiente. Desta forma, este trabalho teve como objetivo verificar a presença de interação genótipo x ambiente para produtividade em genótipos de algodoeiro de fibra colorida e analisar as classificações dadas pelos métodos Centroide e Eberhart e Russel, a partir da lógica Fuzzy, para adaptabilidade e estabilidade. Os experimentos foram realizados na fazenda experimental Capim Branco, em Uberlândia-MG, na safra 2013/2014, 2014/2015, 2015/2016 e 2016/2017. Foram avaliados 12 genótipos de algodoeiro de fibra colorida, sendo 10 do Programa de Melhoramento Genético do Algodoeiro da Universidade Federal de Uberlândia: UFUJP-01, UFUJP-02, UFUJP-05, UFUJP-08, UFUJP-09, UFUJP-10, UFUJP-11, UFUJP-13, UFUJP-16, UFUJP-17 e duas testemunhas: BRS Rubi e BRS Topázio. O delineamento experimental foi de blocos completamente casualizado com três repetições. As análises realizadas foram: teste F para verificação da interação genótipo x ambiente, teste de Scott-Knott, adaptabilidade e estabilidade pelos métodos Centroide e Eberhart e Russel a partir da lógica Fuzzy. A produtividade apresentou interação GxA, que evidencia o comportamento diferencial dos genótipos frente as oscilações ambientais. Os métodos de análise de adaptabilidade e estabilidade apresentaram baixa concordância entre eles (33,3%), porém, a classificação dada utilizando a Lógica Fuzzy condiz bastante com os comportamentos de produtividade dos genótipos

Enabling explainable artificial intelligence capabilities in supply chain decision support making

Explainable artificial intelligence (XAI) has been instrumental in enabling the process of making informed decisions. The emergence of various supply chain (SC) platforms in modern times has altered the nature of SC interactions, resulting in a notable degree of uncertainty. This study aims to conduct a thorough analysis of the existing literature on decision support systems (DSSs) and their incorporation of XAI functionalities within the domain of SC. Our analysis has revealed the influence of XAI on the decision-making process in the field of SC. This study utilizes the SHapley Additive exPlanations (SHAP) technique to analysis the online data using Python machine learning (ML) process. Explanatory algorithms are specifically crafted to augment the lucidity of ML models by furnishing rationales for the prognostications they produce. The present study aims to establish measurable standards for identifying the constituents of XAI and DSSs that augment decision-making in the context of SC. This study assessed prior research with regards to their ability to make predictions, utilization of online dataset, number of variables examined, development of learning capability, and validation within the context of decision-making, emphasizes the research domains that necessitate additional exploration concerning intelligent decision-making under conditions of uncertainty

A Practical Review to Support the Implementation of Smart Solutions within Neighbourhood Building Stock

The construction industry has witnessed an increase in the use of digital tools and smart solutions, particularly in the realm of building energy automation. While realising the potential benefits of smart cities, a broader scope of smart initiatives is required to support the transition from smart buildings towards smart neighbourhoods, which are considered critical urban development units. To support the interplay of smart solutions between buildings and neighbourhoods, this study aimed to collect and review all the smart solutions presented in existing scientific articles, the technical literature, and realised European projects. These solutions were classified into two main sections, buildings and neighbourhoods, which were investigated through five domains: building-energy-related uses, renewable energy sources, water, waste, and open space management. The quantitative outcomes demonstrated the potential benefits of implementing smart solutions in areas ranging from buildings to neighbourhoods. Moreover, this research concluded that the true enhancement of energy conservation goes beyond the building’s energy components and can be genuinely achieved by integrating intelligent neighbourhood elements owing to their strong interdependencies. Future research should assess the effectiveness of these solutions in resource conservation

Characterising the agriculture 4.0 landscape - Emerging trends, challenges and opportunities

ReviewInvestment in technological research is imperative to stimulate the development of sustainable solutions for the agricultural sector. Advances in Internet of Things, sensors and sensor networks, robotics, artificial intelligence, big data, cloud computing, etc. foster the transition towards the Agriculture 4.0 era. This fourth revolution is currently seen as a possible solution for improving agricultural growth, ensuring the future needs of the global population in a fair, resilient and sustainable way. In this context, this article aims at characterising the current Agriculture 4.0 landscape. Emerging trends were compiled using a semi-automated process by analysing relevant scientific publications published in the past ten years. Subsequently, a literature review focusing these trends was conducted, with a particular emphasis on their applications in real environments. From the results of the study, some challenges are discussed, as well as opportunities for future research. Finally, a high-level cloud-based IoT architecture is presented, serving as foundation for designing future smart agricultural systems. It is expected that this work will positively impact the research around Agriculture 4.0 systems, providing a clear characterisation of the concept along with guidelines to assist the actors in a successful transition towards the digitalisation of the sectorinfo:eu-repo/semantics/publishedVersio