Model za podršku odlučivanju izbora sorte šljive zasnovan na fuzzy logici

The choice of the appropriate variety of fruit is one of the most important factors in establishing new orchards. It is necessary to choose the variety that will give the best results in meeting the investment goals. This paper offered an innovative decision support model for plum variety selection, based on expert decision making and fuzzy logic. The fuzzy MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) method was used. The research was conducted with the aim of improving plum production in Bosnia and Herzegovina (BiH). To achieve this, the knowledge of experts from the Republic of Serbia was used, because this country is currently the third in the world in plum production and have branded many plum varieties. The results obtained using this model showed that two plum varieties stand out - Čačanska rodna and Stanley. These results were also confirmed by the performed sensitivity analysis. The worst results were obtained by the Šumadijka variety. These results will help in the selection of plum varieties when establishing new orchards in BiH to achieve the best results in Bosnian plum production.Izbor odgovarajuće sorte voća je jedan od najvažnijih čimbenika kod podizanja novih voćnjaka. Potrebno je odabrati sortu koja će dati najbolje rezultate da bi se ispunili ciljevi ulaganja. Ovaj rad je ponudio inovativni model za podršku odlučivanju pri izboru sorti šljiva zasnovan na ekspertnom odlučivanju i fuzzy logici. Pri tome je korištena fuzzy MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) metoda. Istraživanje je provedeno s ciljem poboljšanja proizvodnje šljive u Bosni i Hercegovini (BiH). Za postizanje ovog cilja korištena su znanja stručnjaka iz Republike Srbije, jer je to trenutno treća zemlja u svijetu po proizvodnji šljive i brendirali su brojne sorte šljiva. Rezultati dobiveni korištenjem ovog modela pokazali su da se dvije sorte šljiva naročito izdvajaju od drugih, a to su sorte Čačanska rodna i Stanley. Rezultati su potvrđeni i provedenom analizom osjetljivosti. Najlošije rezultate je ostvarila sorta Šumadijka. Dobiveni rezultati će pomoći pri odabiru sorti šljiva za podizanje novih voćnjaka u BiH kojima bi se ostvarili najbolji nacionalni rezultati u proizvodnji šljive

Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: a mini review

Abstract:Site suitability problems in renewable energy studies have taken a new turn since the advent of geographical information system (GIS). GIS has been used for site suitability analysis for renewable energy due to its prowess in processing and analyzing attributes with geospatial components. Multi-criteria decision making (MCDM) tools are further used for criteria ranking in the order of influence on the study. Upon location of most appropriate sites, the need for intelligent resource forecast to aid in strategic and operational planning becomes necessary if viability of the investment will be enhanced and resource variability will be better understood. One of such intelligent models is the adaptive neuro-fuzzy inference system (ANFIS) and its variants. This study presents a mini-review of GIS-based MCDM facility location problems in wind and solar resource site suitability analysis and resource forecast using ANFIS-based models. We further present a framework for the integration of the two concepts in wind and solar energy studies. Various MCDM techniques for decision making with their strengths and weaknesses were presented. Country specific studies which apply GIS-based method in site suitability were presented with criteria considered. Similarly, country-specific studies in ANFIS-based resource forecasts for wind and solar energy were also presented. From our findings, there has been no technically valid range of values for spatial criteria and the analytical hierarchical process (AHP) has been commonly used for criteria ranking leaving other techniques less explored. Also, hybrid ANFIS models are more effective compared to standalone ANFIS models in resource forecast, and ANFIS optimized with population-based models has been mostly used. Finally, we present a roadmap for integrating GIS-MCDM site suitability studies with ANFIS-based modeling for improved strategic and operational planning

Multicriteria Model of Support for the Selection of Pear Varieties in Raising Orchards in the Semberija Region (Bosnia and Herzegovina)

Bosnia and Herzegovina (abbreviated BiH) has great potential for fruit production. BiH has over 1.5 million hectares of agricultural land. In addition, there are excellent climatic conditions for growing fruit. However, although there is a long tradition of fruit production in BiH, this production must be improved. This paper provides guidance on making decisions in fruit growing when there are multiple criteria. All criteria are divided into two groups: economic and technical criteria. The economic criteria are further divided into three subcriteria, namely: marketing costs, orchard construction costs and processing and transport costs. Technical criteria are divided into four subcriteria, namely: fruit, variety resistance, production characteristics and processing and transport. According to these, a multicriteria decision-making model based on linguistic values was created. In order to take advantage of these values, a fuzzy approach was applied. Using this approach, decision-making process is easier because decision making is tailored to human thinking. For the example of raising a new orchard in the area of Semberija, an evaluation of seven different varieties of pears was performed. This problem is solved by applying the method of multicriteria analysis (MCDA). To solve this research problem, the MABAC (Multi-attributive border approximation area comparison) method was used. Using the fuzzy MABAC method, the obtained results show that the Šampionka variety has the best indicators among observed varieties. In addition, the Konferans variety achieved good results, and these two varieties are the first choice for raising a new orchard of pears. The paper validates the results and performs sensitivity analysis. The contribution of this research is to develop a new model of decision making by using a new methodology that facilitates decision making on variety selection. This model and methodology provide a flexible way of making decisions in fruit growing

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Actuators and sensors for application in agricultural robots: A review

In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future

Automatic Grading System Of Incoming Raw Unclean Edible Bird Nest Using Deep Learning Model

The grading system for raw unclean EBN plays a vital role in determining the market price between the EBN industry and swiftlet farming. The system also acts as a primary process to monitor the quality of EBN in the production line. However, the human visual system is subjective and based on the workers' experience, hindering a high performance in the grading system. Although the machine learning classifiers such as ANFIS and KMBA were more standardized and accurate, they required experience workers with the specific operation technique for the application. Therefore, a deep learning model with the self-learning ability on the feature extraction process and low human intervention was developed to solve the drawbacks of the human visual system and conventional algorithms. The transfer learning approach could save more computational power via a pre-trained model than build the model from scratch. It also reduces the labour-intensive and time-consuming issues in collecting the vast dataset to train the model. As a result, the best-fine-tuned model was ResNet50, with the highest accuracy of 92.51% among the five pre-trained models selected in identifying 13 of the EBN grades. The performance of the fine-tuned model outperformed the conventional classifiers of ANFIS (88.24%) and KMBA (85.60%) in the EBN grading system. Neuron activation and Grad-CAM analyses were proposed for visualizing the model's prediction on the EBN grades. The investigations aim to provide strong evidence that the fine-tuned model had learned the distinctive and relevant features for predicting the EBN grades. The EBN samples also fed into the deep dream images to enhance the features had detected by the model to indicate the respective EBN grades. The methods provide a better understanding to humans in the model's prediction for increasing the trustability of the model in the automatic EBN grading system

Automated assessment for early and late blight leaf diseases using extended segmentation and optimized features

Early and late blight diseases lead to substantial damage to vegetable crop productions and economic losses. As a modern solution, machine learning-based plant disease assessment aims to assess the disease incidence and severity through the disease region of interest (ROI) and its extracted features. In the case of existing conventional classifier methods, extracting the features involves generalized ROI segmentation that loosely follows the disease inference. As a result, accuracy is reduced, and the fuzzy boundary region that carries potential properties for improving feature characterization capability is truncated from the ROI. Besides, most of the existing practices extract only the global features, This leads to redundant and extensive feature vector, which causes increased complexity and underperformance. Furthermore, individual lesion severity is not considered in the assessment. This thesis addresses the issue of the ROI segmentation by using color thresholding based on ratios of leaf green color intensity to incorporate the fuzzy boundary region, denoted as extended ROI (EROI). Secondly, the issue of the feature extraction is addressed by the proposed localized feature extraction method to reduce complexity and improve disease classification performance. Based on the color and texture morphological properties of the individual lesions within the EROI, color coherence vector and local binary patterns features are extracted. As a result, a pathologically optimized feature vector is obtained, which is used to build a support vector machine classifier to classify between the disease types of early blight, late blight, and healthy leaves. lastly, a 2-tier assessment is proposed. The disease type classification is given as the first tier, while the leaf lesion area ratios of the individual lesions are given as severity quantification for the second tier. Overall, the proposed EROI segmentation method reduced under-segmentation by up to 80%. The proposed optimized feature reduced the execution run-time by up to 50% and achieved an average classification performance of up to 99%. Finally, the quantified severity is in close agreement with the ground truth by achieving an average accuracy of 93%

Mechatronic Systems

Mechatronics, the synergistic blend of mechanics, electronics, and computer science, has evolved over the past twenty five years, leading to a novel stage of engineering design. By integrating the best design practices with the most advanced technologies, mechatronics aims at realizing high-quality products, guaranteeing at the same time a substantial reduction of time and costs of manufacturing. Mechatronic systems are manifold and range from machine components, motion generators, and power producing machines to more complex devices, such as robotic systems and transportation vehicles. With its twenty chapters, which collect contributions from many researchers worldwide, this book provides an excellent survey of recent work in the field of mechatronics with applications in various fields, like robotics, medical and assistive technology, human-machine interaction, unmanned vehicles, manufacturing, and education. We would like to thank all the authors who have invested a great deal of time to write such interesting chapters, which we are sure will be valuable to the readers. Chapters 1 to 6 deal with applications of mechatronics for the development of robotic systems. Medical and assistive technologies and human-machine interaction systems are the topic of chapters 7 to 13.Chapters 14 and 15 concern mechatronic systems for autonomous vehicles. Chapters 16-19 deal with mechatronics in manufacturing contexts. Chapter 20 concludes the book, describing a method for the installation of mechatronics education in schools