A Tool for Classification of Cacao Production in Colombia Based on Multiple Classifier Systems

Plazas, Julián Eduardo; López, Iván Darío; Corrales, Juan Carlos

doi:10.1007/978-3-319-62395-5_5

Cited by 9 publications

(4 citation statements)

References 6 publications

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“…In order to predict future behaviors or relationships, there is a subset of artificial intelligence called machine learning, which includes building classification and regression models [8]. For example, crop production [9] and disease incidence [10,11] can be predicted by using several machine learning techniques and also weather data collection. Models used to predict avocado sales in the United States can be generated using weather and market data.…”

Section: Introductionmentioning

confidence: 99%

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

2018

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Persea americana, commonly known as avocado, is becoming increasingly important in global agriculture. There are dozens of avocado varieties, but more than 85% of the avocados harvested and sold in the world are of the Hass one. Furthermore, information on the market of agricultural products is valuable for decision-making; this has made researchers try to determine the behavior of the avocado market, based on data that might affect it one way or another. In this paper, a machine learning approach for estimating the number of units sold monthly and the total sales of Hass avocados in several cities in the United States, using weather data and historical sales records, is presented. For that purpose, four algorithms were evaluated: Linear Regression, Multilayer Perceptron, Support Vector Machine for Regression and Multivariate Regression Prediction Model. The last two showed the best accuracy, with a correlation coefficient of 0.995 and 0.996, and a Relative Absolute Error of 7.971 and 7.812, respectively. Using the Multivariate Regression Prediction Model, an application that allows avocado producers and sellers to plan sales through the estimation of the profits in dollars and the number of avocados that could be sold in the United States was created.

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Section: Introductionmentioning

confidence: 99%

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

2018

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“…The sub-discipline of machine learning studies various human learning processes as well as the scientific examination of various learning algorithms and methodologies for a variety of application areas [ 2 ]. Machine learning research has prompted researchers and businesses to predict massive mortality incidents [ 3 ], uncontaminated water management [ 4 ], client segmentation in commercial banking [ 5 ], text categorization [ 6 ], and crop productivity, like cocoa [ 7 ]. Machine learning has also been applied in the field of Optimization [ 8 ], intrusion detection [ 9 ], email spam filtering [ 10 ], image processing [ 11 ], crude oil price prediction [ 12 ], and others.…”

Section: Introductionmentioning

confidence: 99%

Exploring machine learning: a scientometrics approach using bibliometrix and VOSviewer

Oyewola

Dada

2022

SN Appl. Sci.

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Machine Learning has found application in solving complex problems in different fields of human endeavors such as intelligent gaming, automated transportation, cyborg technology, environmental protection, enhanced health care, innovation in banking and home security, and smart homes. This research is motivated by the need to explore the global structure of machine learning to ascertain the level of bibliographic coupling, collaboration among research institutions, co-authorship network of countries, and sources coupling in publications on machine learning techniques. The Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was applied to clustering prediction of authors dominance ranking in this paper. Publications related to machine learning were retrieved and extracted from the Dimensions database with no language restrictions. Bibliometrix was employed in computation and visualization to extract bibliographic information and perform a descriptive analysis. VOSviewer (version 1.6.16) tool was used to construct and visualize structure map of source coupling networks of researchers and co-authorship. About 10,814 research papers on machine learning published from 2010 to 2020 were retrieved for the research. Experimental results showed that the highest degree of betweenness centrality was obtained from cluster 3 with 153.86 from the University of California and Harvard University with 24.70. In cluster 1, the national university of Singapore has the highest degree betweenness of 91.72. Also, in cluster 5, the University of Cambridge (52.24) and imperial college London (4.52) having the highest betweenness centrality manifesting that he could control the collaborative relationship and that they possessed and controlled a large number of research resources. Findings revealed that this work has the potential to provide valuable guidance for new perspectives and future research work in the rapidly developing field of machine learning.

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“…Regarding cocoa crops, the yield prediction has been approached from different perspectives. Corrales et al [11] predicted the cocoa yield level in Santander. The authors evaluated the daily average temperature, daily relative humidity, and total daily precipitations rate, using ten different algorithms implemented in WEKA software.…”

Section: Introductionmentioning

confidence: 99%

Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia

2020

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The identification of influencing factors in crop yield (kg·ha-1) provides essential information for decision-making processes related to the prediction and improvement of productivity, which gives farmers the opportunity to increase their income. The current study investigates the application of multiple machine learning algorithms for cocoa yield prediction and influencing factors identification. The Support Vector Machines (SVM) and Ensemble Learning Models (Random Forests, Gradient Boosting) are compared with Least Absolute Shrinkage and Selection Operator (LASSO) regression models. The considered predictors were climate conditions, cocoa variety, fertilization level and sun exposition in an experimental crop located in Rionegro, Santander. Results showed that Gradient Boosting is the best prediction alternative with Coefficient of determination (R2) = 68%, Mean Absolute Error (MAE) = 13.32, and Root Mean Square Error (RMSE) = 20.41. The crop yield variability is explained mainly by the radiation one month before harvest, the accumulated rainfall on the harvest month, and the temperature one month before harvest. Likewise, the crop yields are evaluated based on the kind of sun exposure, and it was found that radiation one month before harvest is the most influential factor in shade-grown plants. On the other hand, rainfall and soil moisture are determining variables in sun-grown plants, which is associated with the water requirements. These results suggest a differentiated management for crops depending on the kind of sun exposure to avoid compromising productivity, since there is no significant difference in the yield of both agricultural managements.

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A Tool for Classification of Cacao Production in Colombia Based on Multiple Classifier Systems

Cited by 9 publications

References 6 publications

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

Exploring machine learning: a scientometrics approach using bibliometrix and VOSviewer

Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia

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