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

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--Soil erosion modeling in olive groves requires precise and accurate spatial data for the representation of topography associated with each time epoch considered. The precision and accuracy of altimetric values affect the quality of the digital elevation model (DEM) and therefore these requirements must be added to the necessity to generate high resolution DEMs. The increase of quality implies: 1. Improving the quality of the instrumentation and methodology applied in the field data collection and 2. Minimizing errors from the interpolation algorithm used to generate the digital terrain model. Currently, RTK networks are an indispensable complement to global navigation satellite systems (GNSS) precise positioning. The availability of highly accurate three-dimensional real time positioning has opened the door to new applications, making network-based real time kinematic (NRTK) positioning an attractive spatial data source for modeling soil erosion in small areas. This paper analyzes the quality of NRTK altimetric positioning supported by a local active network and its application in a test olive grove in SE Spain for soil erosion modeling. An evaluation procedure was implemented at several test sites distributed throughout an olive grove environment with special emphasis on filtering and checking the NRTK solutions in the vertical component. The precision in this component revealed a mean value of 15 mm and the vertical accuracy reached maximum values of 30 mm. In order to generate high resolution and accuracy DEM from the NRTK data, cross sections on the test olive grove were surveyed. The average altimetric quality value (CQ1D) of points surveyed was 0.017 m, according to the standard deviation estimated at test points. Based on the quality results, NRTK positioning is an accurate and reliable methodology for monitoring the erosion processes of small areas in an olive grove environment.Support provided by the Institute of Statistics and Cartography of Andalusia (RAP network) during this project is gratefully acknowledged. The authors thank the Editor and two anonymous reviewers for their valuable comments and recommendations, which contributed to the improvement of this paper. This work was funded by the University of Jaén and “Caja Rural Jaén” (UJA2015/06/11 Project), RNM282-Microgeodesia Jaén Research Group (Junta de Andalucía) and PAI UJA 2017/18

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

The influence of anthropometric, kinematic and energetic variables and gender on swimming performance in youth athletes

The aim of this study was to assess the: (i) gender; (ii) performance and; (iii) gender versus performance interactions in young swimmers’ anthropometric, kinematic and energetic variables. One hundred and thirty six young swimmers (62 boys: 12.76 ± 0.72 years old at Tanner stages 1-2 by self-evaluation; and 64 girls: 11.89 ± 0.93 years old at Tanner stages 1-2 by self-evaluation) were evaluated. Performance, anthropometrics, kinematics and energetic variables were selected. There was a non-significant gender effect on performance, body mass, height, arm span, trunk transverse surface area, stroke length, speed fluctuation, swimming velocity, propulsive efficiency, stroke index and critical velocity. A significant gender effect was found for foot surface area, hand surface area and stroke frequency. A significant sports level effect was verified for all variables, except for stroke frequency, speed fluctuation and propulsive efficiency. Overall, swimmers in quartile 1 (the ones with highest sports level) had higher anthropometric dimensions, better stroke mechanics and energetics. These traits decrease consistently throughout following quartiles up to the fourth one (i.e. swimmers with the lowest sports level). There was a non-significant interaction between gender and sports level for all variables. Our main conclusions were as follows: (i) there are non-significant differences in performance, anthropometrics, kinematics and energetics between boys and girls; (ii) swimmers with best performance are taller, have higher surface areas and better stroke mechanics; (iii) there are non-significant interactions between sports level and gender for anthropometrics, kinematics and energetics

Training evaluation in male age-group swimmers

Monitoring the training process represents an important task during sports preparation. However, not always the applied protocols help to address the coaches’ concerns, namely regarding its complexity and difficulty to be used in large samples. Therefore, the aim of this study was to apply a simple protocol to control the training process in a group of male age-group swimmer

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

Use of a D-optimal design with constrains to quantify the effects of the mixture of sodium, potassium, calcium and magnesium chloride salts on the growth parameters of Saccharomyces cerevisiae.

The combined effect of NaCl, KCl, CaCl(2), and MgCl(2) on the water activity (a (w)) and the growth parameters of Saccharomyces cerevisiae was studied by means of a D-optimal mixture design with constrains (total salt concentrationsor = 9.0%, w/v). The a (w) was linearly related to the concentrations of the diverse salts; its decrease, by similar concentrations of salts, followed the order NaClCaCl(2)KClMgCl(2), regardless of the reference concentrations used (total absence of salts or 5% NaCl). The equations that expressed the maximum specific growth (mu (max)), lag phase duration (lambda), and maximum population reached (N (max)) showed that the values of these parameters depended on linear effects and two-way interactions of the studied chloride salts. The mu (max) decreased as NaCl and CaCl(2) increased (regardless of the presence or not of previous NaCl); however, in the presence of a 5% NaCl, a further addition of KCl and MgCl(2) markedly increased mu (max). The lambda was mainly affected by MgCl(2) and the interactions NaCl x CaCl(2) and CaCl(2) x MgCl(2). The further addition of NaCl and CaCl(2) to a 5% NaCl medium increased the lag phase while KCl and MgCl(2) had negligible or slightly negative effect, respectively. N (max) was mainly affected by MgCl(2) and its interactions with NaCl, KCl, and CaCl(2); MgCl(2) stimulated N (max) in the presence of 5% NaCl while KCl, NaCl, and CaCl(2) had a progressive decreasing effect. These results can be of interest for the fermentation and preservation of vegetable products, and foods in general, in which this yeast could be present

Can 8 weeks of training in female swimmers affect active drag?

Hydrodynamic drag is the force that a swimmer has to overcome in order to maintain his movement through water and is influenced by velocity, shape, size and the frontal surface area Thus, the aim of this study was to assess the effects of 8 weeks of training on active drag in young female swimmers. 8 female age group swimmers belonging to the same swimming club participated in this study. Active drag measurements were conducted in two different trials: at the beginning of the season and after 8 weeks of training. The velocity perturbation method was used to determine active drag in front crawl swimming. After 8 weeks of training, mean active drag decreased, although no significant differences were found between the two trials. No significant differences were observed in swimming velocity between the two trials. It seems that 8 weeks of swimming training were not enough to allow significant improvements on swimming technique. One can recommend that specific training sets concerning technique correction and improvement in young swimmers should be a main aim during training planning

Evaporation Forecasting through Interpretable Data Analysis Techniques

©2022. 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 Electronics. To access the final edited and published work see https://doi.org/10.3390/electronics11040536Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this process. Finally, the results of the interpretable presented model are validated with the best literature models for evaporation rate prediction, i.e., Artificial Neural Networks, obtaining results very similar to those obtained for them, reaching up to 0.85R2 and 0.6MAE. Therefore, in this work, a double objective is faced: to maintain the performance obtained by the models most frequently used in the problem while maintaining the interpretability of the knowledge captured in it, which allows better understanding the problem and carrying out appropriate actions