A Methodology Based on Machine Learning and Soft Computing to Design More Sustainable Agriculture Systems

©2023. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the Published, version of a Published Work that appeared in final form in Sensors. To access the final edited and published work see https://doi.org/10.3390/s23063038Advances in new technologies are allowing any field of real life to benefit from using these ones. Among of them, we can highlight the IoT ecosystem making available large amounts of information, cloud computing allowing large computational capacities, and Machine Learning techniques together with the Soft Computing framework to incorporate intelligence. They constitute a powerful set of tools that allow us to define Decision Support Systems that improve decisions in a wide range of real-life problems. In this paper, we focus on the agricultural sector and the issue of sustainability. We propose a methodology that, starting from times series data provided by the IoT ecosystem, a preprocessing and modelling of the data based on machine learning techniques is carried out within the framework of Soft Computing. The obtained model will be able to carry out inferences in a given prediction horizon that allow the development of Decision Support Systems that can help the farmer. By way of illustration, the proposed methodology is applied to the specific problem of early frost prediction. With some specific scenarios validated by expert farmers in an agricultural cooperative, the benefits of the methodology are illustrated. The evaluation and validation show the effectiveness of the proposal

Kinematic Adaptations of Forward And Backward Walking on Land and in Water

The aim of this study was to compare sagittal plane lower limb kinematics during walking on land and submerged to the hip in water. Eight healthy adults (age 22.1 ± 1.1 years, body height 174.8 ± 7.1 cm, body mass 63.4 ± 6.2 kg) were asked to cover a distance of 10 m at comfortable speed with controlled step frequency, walking forward or backward. Sagittal plane lower limb kinematics were obtained from three dimensional video analysis to compare spatiotemporal gait parameters and joint angles at selected events using two-way repeated measures ANOVA. Key findings were a reduced walking speed, stride length, step length and a support phase in water, and step length asymmetry was higher compared to the land condition (p<0.05). At initial contact, knees and hips were more flexed during walking forward in water, whilst, ankles were more dorsiflexed during walking backward in water. At final stance, knees and ankles were more flexed during forward walking, whilst the hip was more flexed during backward walking. These results show how walking in water differs from walking on land, and provide valuable insights into the development and prescription of rehabilitation and training programs

Toroidal magnetized iron neutrino detector for a neutrino factory

A neutrino factory has unparalleled physics reach for the discovery and measurement of CP violation in the neutrino sector. A far detector for a neutrino factory must have good charge identification with excellent background rejection and a large mass. An elegant solution is to construct a magnetized iron neutrino detector (MIND) along the lines of MINOS, where iron plates provide a toroidal magnetic field and scintillator planes provide 3D space points. In this paper, the current status of a simulation of a toroidal MIND for a neutrino factory is discussed in light of the recent measurements of large θ13. The response and performance using the 10 GeV neutrino factory configuration are presented. It is shown that this setup has equivalent δCP reach to a MIND with a dipole field and is sensitive to the discovery of CP violation over 85% of the values of δCP

A fuzzy random forest

AbstractWhen individual classifiers are combined appropriately, a statistically significant increase in classification accuracy is usually obtained. Multiple classifier systems are the result of combining several individual classifiers. Following Breiman’s methodology, in this paper a multiple classifier system based on a “forest” of fuzzy decision trees, i.e., a fuzzy random forest, is proposed. This approach combines the robustness of multiple classifier systems, the power of the randomness to increase the diversity of the trees, and the flexibility of fuzzy logic and fuzzy sets for imperfect data management. Various combination methods to obtain the final decision of the multiple classifier system are proposed and compared. Some of them are weighted combination methods which make a weighting of the decisions of the different elements of the multiple classifier system (leaves or trees). A comparative study with several datasets is made to show the efficiency of the proposed multiple classifier system and the various combination methods. The proposed multiple classifier system exhibits a good accuracy classification, comparable to that of the best classifiers when tested with conventional data sets. However, unlike other classifiers, the proposed classifier provides a similar accuracy when tested with imperfect datasets (with missing and fuzzy values) and with datasets with noise

Making decisions for frost prediction in agricultural crops in a softcomputing framework

© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Computers and Electronics in Agriculture. To access the final edited and published work see https://doi.org/10.1016/j.compag.2020.105587Nowadays, there are many areas of daily life that can obtain benefit from technological advances and the large amounts of information stored. One of these areas is agriculture, giving place to precision agriculture. Frosts in crops are among the problems that precision agriculture tries to solve because produce great economic losses to farmers. The problem of early detection of frost is a process that involves a large amount of wheather data. However, the use of these data, both for the classification and regression task, must be carried out in an adequate way to obtain an inference with quality. A preprocessing of them is carried out in order to obtain a dataset grouping attributes that refer to the same measure in a single attribute expressed by a fuzzy value. From these fuzzy time series data we must use techniques for data analysis that are capable of manipulating them. Therefore, first a regression technique based on k-nearest neighbors in a Soft Computing framework is proposed that can deal with fuzzy data, and second, this technique and others to classification are used for the early detection of a frost from data obtained from different weather stations in the Region of Murcia (south-east Spain) with the aim of decrease the damages that these frosts can cause in crops. From the models obtained, an interpretation of the provided information is performed and the most relevant set of attributes is obtained for the anticipated prediction of a frost and of the temperature value. Several experiments are carried out on the datasets to obtain the models with the best performance in the prediction validating the results by means of a statistical analysis

Mobile Zoos and Other Itinerant Animal Handling Events: Current Status and Recommendations for Future Policies

Mobile zoos are events in which non-domesticated (exotic) and domesticated species are transported to venues such as schools, hospitals, parties, and community centres, for the purposes of education, entertainment, or social and therapeutic assistance. We conducted literature searches and surveyed related government agencies regarding existing provisions within laws and policies, number of mobile zoos, and formal guidance issued concerning operation of such events in 74 countries or regions. We also examined governmental and non-governmental guidance standards for mobile zoos, as well as websites for mobile zoo operations, assessed promotional or educational materials for scientific accuracy, and recorded the diversity of species in use. We used the EMODE (Easy, Moderate, Difficult, or Extreme) algorithm, to evaluate identified species associated with mobile zoos for their suitability for keeping. We recorded 14 areas of concern regarding animal biology and public health and safety, and 8 areas of false and misleading content in promotional or educational materials. We identified at least 341 species used for mobile zoos. Mobile zoos are largely unregulated, unmonitored, and uncontrolled, and appear to be increasing. Issues regarding poor animal welfare, public health and safety, and education raise several serious concerns. Using the precautionary principle when empirical evidence was not available, we advise that exotic species should not be used for mobile zoos and similar itinerant events

A Fuzzy k-Nearest Neighbors Classifier to Deal with Imperfect Data

© 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Soft Computing. To access the final edited and published work see https://doi.org/10.1007/s00500-017-2567-xThe k-nearest neighbors method (kNN) is a nonparametric, instance-based method used for regression and classification. To classify a new instance, the kNN method computes its k nearest neighbors and generates a class value from them. Usually, this method requires that the information available in the datasets be precise and accurate, except for the existence of missing values. However, data imperfection is inevitable when dealing with real-world scenarios. In this paper, we present the kNNimp classifier, a k-nearest neighbors method to perform classification from datasets with imperfect value. The importance of each neighbor in the output decision is based on relative distance and its degree of imperfection. Furthermore, by using external parameters, the classifier enables us to define the maximum allowed imperfection, and to decide if the final output could be derived solely from the greatest weight class (the best class) or from the best class and a weighted combination of the closest classes to the best one. To test the proposed method, we performed several experiments with both synthetic and realworld datasets with imperfect data. The results, validated through statistical tests, show that the kNNimp classifier is robust when working with imperfect data and maintains a good performance when compared with other methods in the literature, applied to datasets with or without imperfection

The Golden Channel at a Neutrino Factory revisited: improved sensitivities from a Magnetised Iron Neutrino Detector

This paper describes the performance and sensitivity to neutrino mixing parameters of a Magnetised Iron Neutrino Detector (MIND) at a Neutrino Factory with a neutrino beam created from the decay of 10 GeV muons. Specifically, it is concerned with the ability of such a detector to detect muons of the opposite sign to those stored (wrong-sign muons) while suppressing contamination of the signal from the interactions of other neutrino species in the beam. A new more realistic simulation and analysis, which improves the efficiency of this detector at low energies, has been developed using the GENIE neutrino event generator and the GEANT4 simulation toolkit. Low energy neutrino events down to 1 GeV were selected, while reducing backgrounds to the $10^{-4}$ level. Signal efficiency plateaus of ~60% for $\nu_\mu$ and ~70% for $\bar{\nu}_\mu$ events were achieved starting at ~5 GeV. Contamination from the $\nu_\mu\rightarrow \nu_\tau$ oscillation channel was studied for the first time and was found to be at the level between 1% and 4%. Full response matrices are supplied for all the signal and background channels from 1 GeV to 10 GeV. The sensitivity of an experiment involving a MIND detector of 100 ktonnes at 2000 km from the Neutrino Factory is calculated for the case of $\sin^2 2\theta_{13}\sim 10^{-1}$. For this value of $\theta_{13}$, the accuracy in the measurement of the CP violating phase is estimated to be $\Delta \delta_{CP}\sim 3^\circ - 5^\circ$, depending on the value of $\delta_{CP}$, the CP coverage at $5\sigma$ is 85% and the mass hierarchy would be determined with better than $5\sigma$ level for all values of $\delta_{CP}$